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Bridges & Structures

Prefabricated Steel Bridge Systems: Final Report

9. Appendix B - SBO Optimization

File: sboResult.txt

Running MPI executable in serial mode.
Writing new restart file dakota.rst
Constructing Surrogate-Based Optimization Strategy...
methodName = dace
gradientType = none
hessianType = none

Adjusting the number of symbols and samples....
num_variables   = 6
OLD num_samples = 10    OLD num_symbols = 0
NEW num_samples = 10    NEW num_symbols = 10
methodName = conmin_mfd
gradientType = numerical
Numerical gradients using forward differences
to be calculated by the dakota finite difference routine.
hessianType = none
Running Surrogate-Based Optimization Strategy...

*********************************************
Begin SBO Iteration Number 1

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.4500000000e+01
1.2000000000e+01
7.8250000000e-01
4.3750000000e-01
7.6250000000e-01
Current Trust Region Upper Bounds
1.2400000000e+01
5.7500000000e+01
1.2600000000e+01
8.5750000000e-01
5.0312500000e-01
8.3750000000e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed (user-specified) = 12345

------------------------------
Begin Function Evaluation    1
------------------------------
Parameters for function evaluation 1:
1.2117159189e+01 w_top
5.5488687348e+01 hw
1.2564033020e+01 w_bot
8.3255450091e-01 t_top
4.8243696557e-01 tw
8.1595395996e-01 t_bot

(./SBOdrive /tmp/filew4TsNe /tmp/fileMPOKqj)

Active response data for function evaluation 1:
Active set vector = { 1 1 1 }
1.8247300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation    3
------------------------------
Parameters for function evaluation 3:
1.2235235582e+01 w_top
5.7290571722e+01 hw
1.2440331736e+01 w_bot
8.0566385236e-01 t_top
4.9932217365e-01 tw
7.7090789393e-01 t_bot

(./SBOdrive /tmp/fileowshDv /tmp/fileemwhBB)

Active response data for function evaluation 3:
Active set vector = { 1 1 1 }
1.8324400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation    5
------------------------------
Parameters for function evaluation 5:
1.2158288909e+01 w_top
5.4983358276e+01 hw
1.2199564214e+01 w_bot
7.8419956377e-01 t_top
4.6459523983e-01 tw
8.3356353936e-01 t_bot

(./SBOdrive /tmp/fileEwACtT /tmp/fileIrxJaY)

Active response data for function evaluation 5:
Active set vector = { 1 1 1 }
1.9128400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation    7
------------------------------
Parameters for function evaluation 7:
1.2149269407e+01 w_top
5.5367825890e+01 hw
1.2489510531e+01 w_bot
8.0707323642e-01 t_top
4.7195392543e-01 tw
8.2274379554e-01 t_bot

(./SBOdrive /tmp/fileQKkdCe /tmp/fileWUYark)

Active response data for function evaluation 7:
Active set vector = { 1 1 1 }
1.8174000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation    9
------------------------------
Parameters for function evaluation 9:
1.2318782285e+01 w_top
5.5040165557e+01 hw
1.2185566372e+01 w_bot
7.9636602102e-01 t_top
4.8183702673e-01 tw
8.1276101193e-01 t_bot

(./SBOdrive /tmp/file49nrbL /tmp/fileimN9pU)

Active response data for function evaluation 9:
Active set vector = { 1 1 1 }
1.9204200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   11
------------------------------
Parameters for function evaluation 11:
1.2217029216e+01 w_top
5.7492244526e+01 hw
1.2585015996e+01 w_bot
8.1563811184e-01 t_top
5.0250611281e-01 tw
8.0259064714e-01 t_bot

(./SBOdrive /tmp/fileipW1Eh /tmp/file0i24Ip)

Active response data for function evaluation 11:
Active set vector = { 1 1 1 }
1.8398000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   13
------------------------------
Parameters for function evaluation 13:
1.2012381666e+01 w_top
5.6335168622e+01 hw
1.2088576489e+01 w_bot
8.5247227044e-01 t_top
4.8527365139e-01 tw
7.7588320619e-01 t_bot

(./SBOdrive /tmp/fileMYqvpX /tmp/fileCsnf28)

Active response data for function evaluation 13:
Active set vector = { 1 1 1 }
1.8234900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   15
------------------------------
Parameters for function evaluation 15:
1.2207766701e+01 w_top
5.6769354027e+01 hw
1.2036007709e+01 w_bot
8.4095082305e-01 t_top
4.6701682214e-01 tw
7.6427759921e-01 t_bot

(./SBOdrive /tmp/file83pxoD /tmp/file8oh4VN)

Active response data for function evaluation 15:
Active set vector = { 1 1 1 }
1.8154800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   17
------------------------------
Parameters for function evaluation 17:
1.2177131376e+01 w_top
5.7244538013e+01 hw
1.2504475027e+01 w_bot
8.2911487200e-01 t_top
4.3825018196e-01 tw
7.6881265386e-01 t_bot

(./SBOdrive /tmp/filearTJys /tmp/fileGoO4vG)

Active response data for function evaluation 17:
Active set vector = { 1 1 1 }
2.6337200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   19
------------------------------
Parameters for function evaluation 19:
1.2080722945e+01 w_top
5.4735302638e+01 hw
1.2241685404e+01 w_bot
8.4862179614e-01 t_top
4.9528670155e-01 tw
8.0339776532e-01 t_bot

(./SBOdrive /tmp/file41Bbrh /tmp/file8m8hau)

Active response data for function evaluation 19:
Active set vector = { 1 1 1 }
1.9288600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   21
------------------------------
Parameters for function evaluation 21:
1.2394968791e+01 w_top
5.7148603251e+01 hw
1.2257527990e+01 w_bot
8.4980067170e-01 t_top
4.6203799334e-01 tw
8.1732913435e-01 t_bot

(./SBOdrive /tmp/fileO3ywCf /tmp/fileIrNVKv)

Active response data for function evaluation 21:
Active set vector = { 1 1 1 }
1.8234800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   23
------------------------------
Parameters for function evaluation 23:
1.2058587519e+01 w_top
5.4550885567e+01 hw
1.2350956705e+01 w_bot
8.4216347972e-01 t_top
4.5707192557e-01 tw
7.8944054488e-01 t_bot

(./SBOdrive /tmp/fileqz7fhd /tmp/filecrnrcs)

Active response data for function evaluation 23:
Active set vector = { 1 1 1 }
1.9160600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   25
------------------------------
Parameters for function evaluation 25:
1.2341043738e+01 w_top
5.6292452370e+01 hw
1.2076340681e+01 w_bot
8.0342445721e-01 t_top
4.9698531571e-01 tw
8.2577254365e-01 t_bot

(./SBOdrive /tmp/file2mXRfk /tmp/fileUHaFAC)

Active response data for function evaluation 25:
Active set vector = { 1 1 1 }
1.8308900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   27
------------------------------
Parameters for function evaluation 27:
1.2265441938e+01 w_top
5.5950597201e+01 hw
1.2324647389e+01 w_bot
8.2178254376e-01 t_top
4.4976949028e-01 tw
8.0986041635e-01 t_bot

(./SBOdrive /tmp/file6sfpHq /tmp/file699ROH)

Active response data for function evaluation 27:
Active set vector = { 1 1 1 }
1.8093400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   29
------------------------------
Parameters for function evaluation 29:
1.2000000000e+01 w_top
5.6000000000e+01 hw
1.2000000000e+01 w_bot
8.2000000000e-01 t_top
4.5000000000e-01 tw
8.0000000000e-01 t_bot

(./SBOdrive /tmp/file0d9NKG /tmp/fileeIl3z1)

Active response data for function evaluation 29:
Active set vector = { 1 1 1 }
1.9071200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.
Beginning Approximate Fn Evaluations...

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.54500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.76250E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12400E+02  0.57500E+02  0.12600E+02  0.85750E+00  0.50313E+00  0.83750E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.191738E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56000E+02  0.12000E+02  0.82000E+00  0.45000E+00  0.80000E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -8.1506262275e+05 -1.5431636836e+05  4.5590698934e+05  4.0864851431e+06
4.0673292540e+06  1.3222368805e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =   0.65542E+05

DECISION VARIABLES (X-VECTOR)
1)    0.12003E+02  0.56000E+02  0.12000E+02  0.80744E+00  0.43750E+00  0.79594E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -4.8868649927e+05 -1.6411020010e+05  4.9607038802e+05  5.9915220431e+06
5.3730978034e+06 -2.7342310754e+05 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =  -0.11430E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12005E+02  0.56001E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.79707E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -3.5764397378e+05 -1.6798092458e+05  5.7308740856e+05  8.2986975242e+06
7.1175927975e+06 -7.1210133715e+05 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =  -0.13097E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12022E+02  0.56010E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83268E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.5338218973e+05 -2.3944274410e+05  5.9945839362e+05  8.8504450603e+06
1.0192109557e+07  4.3694704024e+05 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =  -0.14665E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12051E+02  0.56019E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.81593E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -3.2157302977e+05 -2.0098298245e+05  5.5082706100e+05  8.3914971534e+06
8.2166242540e+06 -6.6872880753e+05 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =  -0.18841E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.56058E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.1280507683e+05 -2.3831164594e+05  5.2086203825e+05  8.4459038253e+06
9.3558109306e+06 -8.0210042966e+05 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    6     OBJ =  -0.20559E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12186E+02  0.56138E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.6402566082e+05 -2.3202837718e+05  4.6912681700e+05  8.0693202902e+06
8.4228222324e+06 -1.9934002254e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    7     OBJ =  -0.22285E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12111E+02  0.56203E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.1229803878e+05 -2.4667942123e+05  5.7050058600e+05  8.4736363530e+06
9.6080779316e+06 -1.0554521494e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    8     OBJ =  -0.24140E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12185E+02  0.56289E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.8014978393e+05 -2.4036441549e+05  5.1810375359e+05  8.0853037156e+06
8.6497957769e+06 -2.2910084508e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    9     OBJ =  -0.26003E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12107E+02  0.56355E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.1932710864e+05 -2.5563190833e+05  6.2383716719e+05  8.5089512877e+06
9.8897700387e+06 -1.3045874443e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   10     OBJ =  -0.27996E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12183E+02  0.56444E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.9114876560e+05 -2.4910104429e+05  5.6961309256e+05  8.1064536533e+06
8.8968696317e+06 -2.5858377012e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   11     OBJ =  -0.29999E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12102E+02  0.56513E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.2670926916e+05 -2.6491893679e+05  6.7916959903e+05  8.5456533036e+06
1.0182129466e+07 -1.5627707799e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   12     OBJ =  -0.32140E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12181E+02  0.56605E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.0242611168e+05 -2.5816271803e+05  6.2304279403e+05  8.1284880204e+06
9.1533397138e+06 -2.8912572125e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   13     OBJ =  -0.34295E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12097E+02  0.56677E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.3447219648e+05 -2.7455260972e+05  7.3657053944e+05  8.5838036903e+06
1.0485565355e+07 -1.8302863581e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   14     OBJ =  -0.36594E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12179E+02  0.56772E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.1399719152e+05 -2.6756055888e+05  6.7845839075e+05  8.1514327206e+06
9.4195184348e+06 -3.2076462750e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   15     OBJ =  -0.38911E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12092E+02  0.56846E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine

------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.4260759529e+05 -2.8454421149e+05  7.9610669773e+05  8.6234341688e+06
1.0800406043e+07 -2.1074978697e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   16     OBJ =  -0.41380E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12177E+02  0.56946E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.2589525517e+05 -2.7730591627e+05  7.3592622192e+05  8.1753010955e+06
9.6956972131e+06 -3.5354427847e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   17     OBJ =  -0.43871E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12087E+02  0.57022E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.5111252545e+05 -2.9490572476e+05  8.5784908907e+05  8.6645828062e+06
1.1127009319e+07 -2.3947745256e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   18     OBJ =  -0.46524E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12175E+02  0.57125E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.3814937487e+05 -2.8741072485e+05  7.9551622581e+05  8.2001115643e+06
9.9821916711e+06 -3.8750901589e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   19     OBJ =  -0.49202E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12081E+02  0.57205E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.5998773024e+05 -3.0564977571e+05  9.2187265889e+05  8.7072925697e+06
1.1465758188e+07 -2.6924942412e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   20     OBJ =  -0.52051E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12172E+02  0.57312E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.5078590761e+05 -2.9788748515e+05  8.5730178139e+05  8.2258864729e+06
1.0279338791e+07 -4.2270409859e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   21     OBJ =  -0.54929E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12076E+02  0.57394E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.6923671718e+05 -3.1678960831e+05  9.8825610311e+05  8.7516105678e+06
1.1817058636e+07 -3.0010455292e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   22     OBJ =  -0.57982E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12166E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.3660701130e+05 -3.0909499833e+05  9.2423624214e+05  8.2741070647e+06
1.0640367531e+07 -4.5233573626e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   23     OBJ =  -0.58828E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  4.3672780803e+03 -3.1668017246e+05  9.7931497675e+05  8.5472721002e+06
1.1387434639e+07 -3.7898415292e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   24     OBJ =  -0.58828E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

ITER =   25     OBJ =  -0.58828E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  4.3672781295e+03 -3.1668017246e+05  9.7931497680e+05  8.5472721010e+06
1.1387434640e+07 -3.7898415285e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   26     OBJ =  -0.58828E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =  -0.588284E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12115E+02  0.57500E+02  0.12000E+02  0.78250E+00  0.43750E+00  0.83750E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    5 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
2   -3   -4   -5    6

TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR  3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1))   LESS THAN DABFUN FOR  3 ITERATIONS

NUMBER OF ITERATIONS =   26

OBJECTIVE FUNCTION WAS EVALUATED           82  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED        82  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED       25  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED   25  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation   31
------------------------------
Parameters for function evaluation 31:
1.2096137303e+01 w_top
5.6055345201e+01 hw
1.2110593474e+01 w_bot
8.2162870698e-01 t_top
4.7022998611e-01 tw
8.0218611341e-01 t_bot

(./SBOdrive /tmp/fileo0LvK6 /tmp/fileqAclYr)

Active response data for function evaluation 31:
Active set vector = { 1 1 1 }
1.8157700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   33
------------------------------
Parameters for function evaluation 33:
1.2059282820e+01 w_top
5.5856834452e+01 hw
1.2132682887e+01 w_bot
8.1688861310e-01 t_top
4.4262870075e-01 tw
8.1228296740e-01 t_bot

(./SBOdrive /tmp/filegyfkgy /tmp/fileAVk4TW)

Active response data for function evaluation 33:
Active set vector = { 1 1 1 }
1.9052700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   35
------------------------------
Parameters for function evaluation 35:
1.2056968408e+01 w_top
5.6641773338e+01 hw
1.2188860011e+01 w_bot
8.1173292685e-01 t_top
4.4429615686e-01 tw
7.9740162996e-01 t_bot

(./SBOdrive /tmp/file0hxq6a /tmp/fileOgGkPA)

Active response data for function evaluation 35:
Active set vector = { 1 1 1 }
1.8048200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   37
------------------------------
Parameters for function evaluation 37:
1.2138210281e+01 w_top
5.5986258346e+01 hw
1.2088908961e+01 w_bot
8.3139012961e-01 t_top
4.5913283578e-01 tw
8.0314437570e-01 t_bot

(./SBOdrive /tmp/fileoOtgnL /tmp/fileKWoxY9)

Active response data for function evaluation 37:
Active set vector = { 1 1 1 }
1.9122500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2 ------------------------------
Begin Function Evaluation   39
------------------------------
Parameters for function evaluation 39:
1.2001582128e+01 w_top
5.6583650968e+01 hw
1.2160652176e+01 w_bot
8.1952004107e-01 t_top
4.6122467311e-01 tw
8.0129802158e-01 t_bot

(./SBOdrive /tmp/fileed9WSu /tmp/fileYN4hUW)

Active response data for function evaluation 39:
Active set vector = { 1 1 1 }
1.8130300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   41
------------------------------
Parameters for function evaluation 41:
1.2156083526e+01 w_top
5.5507506688e+01 hw
1.2043349973e+01 w_bot
8.0428880310e-01 t_top
4.7224226612e-01 tw
7.8859793033e-01 t_bot

(./SBOdrive /tmp/fileqT9a1d /tmp/filey6M7NE)

Active response data for function evaluation 41:
Active set vector = { 1 1 1 }
1.9154300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   43
------------------------------
Parameters for function evaluation 43:
1.2135419003e+01 w_top
5.5623311364e+01 hw
1.2028687107e+01 w_bot
8.0271039244e-01 t_top
4.5053979522e-01 tw
8.1054624441e-01 t_bot

(./SBOdrive /tmp/fileIM9ze6 /tmp/file0AlqrA)

Active response data for function evaluation 43:
Active set vector = { 1 1 1 }
1.9077000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   45
------------------------------
Parameters for function evaluation 45:
1.2046001555e+01 w_top
5.6145442372e+01 hw
1.2105119803e+01 w_bot
8.1792492913e-01 t_top
4.4814899295e-01 tw
7.9155356487e-01 t_bot

(./SBOdrive /tmp/fileseyGmY /tmp/fileA5TZir)

Active response data for function evaluation 45:
Active set vector = { 1 1 1 }
1.9062300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   47
------------------------------
Parameters for function evaluation 47:
1.2119694061e+01 w_top
5.6469539959e+01 hw
1.2221503307e+01 w_bot
8.0912172480e-01 t_top
4.6757421146e-01 tw
7.8995401938e-01 t_bot

(./SBOdrive /tmp/filekuMnAZ /tmp/fileg3Ti6v)

Active response data for function evaluation 47:
Active set vector = { 1 1 1 }
1.8145600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   49
------------------------------
Parameters for function evaluation 49:
1.2190795046e+01 w_top
5.5925847224e+01 hw
1.2073946626e+01 w_bot
8.1452440530e-01 t_top
4.6578027127e-01 tw
7.8721157179e-01 t_bot

(./SBOdrive /tmp/filegUSQB0 /tmp/filesdQQRv)

Active response data for function evaluation 49:
Active set vector = { 1 1 1 }
1.9133400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   51
------------------------------
Parameters for function evaluation 51:
1.2012263525e+01 w_top
5.5440460647e+01 hw
1.2200912312e+01 w_bot
8.3436733633e-01 t_top
4.4997534909e-01 tw
7.9410891316e-01 t_bot

(./SBOdrive /tmp/fileKNd2Va /tmp/fileeqm5BJ)

Active response data for function evaluation 51:
Active set vector = { 1 1 1 }
1.9100900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   53
------------------------------
Parameters for function evaluation 53:
1.2088666863e+01 w_top
5.6733366822e+01 hw
1.2205279197e+01 w_bot
8.2573218705e-01 t_top
4.5289839000e-01 tw
8.1161109058e-01 t_bot

(./SBOdrive /tmp/fileOfZuWk /tmp/fileMpjfmS)

Active response data for function evaluation 53:
Active set vector = { 1 1 1 }
1.8122600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   55
------------------------------
Parameters for function evaluation 55:
1.2169349170e+01 w_top
5.6415012777e+01 hw
1.2015990312e+01 w_bot
8.2076112920e-01 t_top
4.5158833489e-01 tw
8.1632784487e-01 t_bot

(./SBOdrive /tmp/fileC1qk3D /tmp/filecnxJ0e)

Active response data for function evaluation 55:
Active set vector = { 1 1 1 }
1.8097300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   57
------------------------------
Parameters for function evaluation 57:
1.2103611196e+01 w_top
5.5291060214e+01 hw
1.2004440195e+01 w_bot
8.0584007845e-01 t_top
4.6016036955e-01 tw
7.8555774366e-01 t_bot

(./SBOdrive /tmp/file2UUFA1 /tmp/file6jJUhB)

Active response data for function evaluation 57:
Active set vector = { 1 1 1 }
1.9118100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   59
------------------------------
Parameters for function evaluation 59:
1.2000000000e+01 w_top
5.6000447715e+01 hw
1.2009776838e+01 w_bot
8.0125000000e-01 t_top
4.3750000000e-01 tw
8.1630106742e-01 t_bot

(./SBOdrive /tmp/file62PKrn /tmp/filey0RQZY)

Active response data for function evaluation 59:
Active set vector = { 1 1 1 }
2.3939900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio = -6.4854078971e-01:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 3

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5625000000e+01
1.2000000000e+01
8.1062500000e-01
4.3750000000e-01
7.9062500000e-01
Current Trust Region Upper Bounds
1.2100000000e+01
5.6375000000e+01
1.2150000000e+01
8.2937500000e-01
4.6328125000e-01
8.0937500000e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution

------------------------------
Begin Function Evaluation   61
------------------------------
Parameters for function evaluation 61:
1.2069106192e+01 w_top
5.6302808025e+01 hw
1.2106452306e+01 w_bot
8.1412865307e-01 t_top
4.5301225992e-01 tw
7.9603493561e-01 t_bot

(./SBOdrive /tmp/fileqtCoiS /tmp/filee2Vsmx)

Active response data for function evaluation 61:
Active set vector = { 1 1 1 }
1.9072900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   63
------------------------------
Parameters for function evaluation 63:
1.2027686035e+01 w_top
5.6193986106e+01 hw
1.2058263515e+01 w_bot
8.2004229152e-01 t_top
4.5864974306e-01 tw
8.0500713667e-01 t_bot

(./SBOdrive /tmp/filegoIyww /tmp/fileMt7Qpa)

Active response data for function evaluation 63:
Active set vector = { 1 1 1 }
1.8103100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   65
------------------------------
Parameters for function evaluation 65:
1.2039985084e+01 w_top
5.6273849250e+01 hw
1.2028049642e+01 w_bot
8.1269789523e-01 t_top
4.5336155868e-01 tw
7.9190066304e-01 t_bot

(./SBOdrive /tmp/fileCXYvk8 /tmp/fileCyi9hN)

Active response data for function evaluation 65:
Active set vector = { 1 1 1 }
1.9071000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   67
------------------------------
Parameters for function evaluation 67:
1.2050982093e+01 w_top
5.5838349857e+01 hw
1.2120910748e+01 w_bot
8.1508324445e-01 t_top
4.4107435051e-01 tw
7.9999164716e-01 t_bot

(./SBOdrive /tmp/fileMiNMCV /tmp/fileI9fO8D)

Active response data for function evaluation 67:
Active set vector = { 1 1 1 }
1.9045200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   69
------------------------------
Parameters for function evaluation 69:
1.2045333431e+01 w_top
5.6027218254e+01 hw
1.2128542484e+01 w_bot
8.2786760612e-01 t_top
4.3790653535e-01 tw
8.0131847739e-01 t_bot

(./SBOdrive /tmp/fileClpV0I /tmp/fileqkCXkq)

Active response data for function evaluation 69:
Active set vector = { 1 1 1 }
2.3610500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   71
------------------------------
Parameters for function evaluation 71:
1.2061541049e+01 w_top
5.6364698414e+01 hw
1.2078342842e+01 w_bot
8.1254824293e-01 t_top
4.4428302351e-01 tw
8.0412336841e-01 t_bot

(./SBOdrive /tmp/fileqxSYzD /tmp/fileo9qN7l)

Active response data for function evaluation 71:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   73
------------------------------
Parameters for function evaluation 73:
1.2001681679e+01 w_top
5.6256042092e+01 hw
1.2145069454e+01 w_bot
8.1957149484e-01 t_top
4.4493832943e-01 tw
8.0835551154e-01 t_bot

(./SBOdrive /tmp/fileuhPCzA /tmp/filecjjoFm)

Active response data for function evaluation 73:
Active set vector = { 1 1 1 }
1.8049200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   75
------------------------------
Parameters for function evaluation 75:
1.2057018935e+01 w_top
5.6065550088e+01 hw
1.2036313622e+01 w_bot
8.1907617777e-01 t_top
4.5417705428e-01 tw
7.9094380790e-01 t_bot

(./SBOdrive /tmp/fileuX63YI /tmp/file63YF8v)

Active response data for function evaluation 75:
Active set vector = { 1 1 1 }
1.9086100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   77
------------------------------
Parameters for function evaluation 77:
1.2087626023e+01 w_top
5.5675911512e+01 hw
1.2136554266e+01 w_bot
8.1819767571e-01 t_top
4.5054751449e-01 tw
8.0730104573e-01 t_bot

(./SBOdrive /tmp/fileWVE8cP /tmp/fileULsZdB)

Active response data for function evaluation 77:
Active set vector = { 1 1 1 }
1.9086900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   79
------------------------------
Parameters for function evaluation 79:
1.2093060541e+01 w_top
5.5943074013e+01 hw
1.2072613429e+01 w_bot
8.2812515412e-01 t_top
4.4330562908e-01 tw
8.0879329757e-01 t_bot

(./SBOdrive /tmp/filegcqsI4 /tmp/fileYZIFhU)

Active response data for function evaluation 79:
Active set vector = { 1 1 1 }
1.9064500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   81
------------------------------
Parameters for function evaluation 81:
1.2032742248e+01 w_top
5.6133116275e+01 hw
1.2044106508e+01 w_bot
8.2462240110e-01 t_top
4.6066542764e-01 tw
7.9385742748e-01 t_bot

(./SBOdrive /tmp/fileyDXnUj /tmp/file2J9Ob8)

Active response data for function evaluation 81:
Active set vector = { 1 1 1 }
1.9109900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   83
------------------------------
Parameters for function evaluation 83:
1.2029193146e+01 w_top
5.6337213438e+01 hw
1.2017219077e+01 w_bot
8.2484290420e-01 t_top
4.4989154157e-01 tw
7.9467869639e-01 t_bot

(./SBOdrive /tmp/fileWDNirI /tmp/fileSAfyfA)

Active response data for function evaluation 83:
Active set vector = { 1 1 1 }
1.9068000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   85
------------------------------
Parameters for function evaluation 85:
1.2037098081e+01 w_top
5.5705156631e+01 hw
1.2063919192e+01 w_bot
8.2156370672e-01 t_top
4.4121390971e-01 tw
8.0545845716e-01 t_bot

(./SBOdrive /tmp/file2g0sU6 /tmp/filecffhrX)

Active response data for function evaluation 85:
Active set vector = { 1 1 1 }
1.9054400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   87
------------------------------
Parameters for function evaluation 87:
1.2047240551e+01 w_top
5.5860696887e+01 hw
1.2096489024e+01 w_bot
8.1616455841e-01 t_top
4.5916614901e-01 tw
7.9265604166e-01 t_bot

(./SBOdrive /tmp/fileA4mXEE /tmp/fileyMaoHy)

Active response data for function evaluation 87:
Active set vector = { 1 1 1 }
1.9105400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed

<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.55625E+02  0.12000E+02  0.81062E+00  0.43750E+00  0.79063E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12100E+02  0.56375E+02  0.12150E+02  0.82937E+00  0.46328E+00  0.80938E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.190944E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56000E+02  0.12000E+02  0.82000E+00  0.45000E+00  0.80000E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -5.1206042344e+05 -2.7566435507e+03  2.1051798803e+05  1.3262836780e+05
-6.7326362580e+05 -2.3732619154e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =   0.16686E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12002E+02  0.56000E+02  0.12000E+02  0.81948E+00  0.45266E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -3.0070217584e+05 -5.1325010331e+04  2.1182426689e+05 -2.3145950051e+05
1.5665856408e+06 -2.8470496579e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =   0.16369E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12003E+02  0.56000E+02  0.12000E+02  0.82005E+00  0.44881E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -3.5292659381e+05 -3.4836764578e+04  2.7287923254e+05  5.2668640070e+03
-6.1963704917e+04 -3.2868204006e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =   0.16107E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12017E+02  0.56002E+02  0.12000E+02  0.81984E+00  0.45125E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.9452716536e+05 -5.6225874506e+04  2.7844110884e+05 -3.4884445418e+05
1.1445776406e+06 -2.7842950055e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =   0.15925E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12017E+02  0.56002E+02  0.12000E+02  0.82071E+00  0.44838E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.3973150031e+05 -4.3947367669e+04  3.2600185700e+05 -2.3573329212e+05
-1.0809087161e+05 -3.1221403277e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =   0.14937E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12026E+02  0.56003E+02  0.12000E+02  0.82937E+00  0.45236E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.1877514707e+05 -6.9301655478e+04  3.3824262229e+05 -1.9160302121e+06
9.0658293029e+05 -2.7055241706e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    6     OBJ =   0.14825E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12027E+02  0.56003E+02  0.12000E+02  0.82937E+00  0.45001E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.4527493177e+05 -5.9301430017e+04  3.7416063892e+05 -1.7238510892e+06
-5.5819123796e+04 -2.9652487501e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    7     OBJ =   0.14714E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12034E+02  0.56005E+02  0.12000E+02  0.82937E+00  0.45180E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.5248799197e+05 -7.3296532670e+04  3.7264389676e+05 -1.9758730533e+06
7.8108905326e+05 -2.6406606807e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    8     OBJ =   0.14634E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12035E+02  0.56006E+02  0.12000E+02  0.82937E+00  0.44981E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.7581106578e+05 -6.4746444492e+04  4.0286111869e+05 -1.8117003317e+06
-3.6651164343e+04 -2.8625826361e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    9     OBJ =   0.14560E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12042E+02  0.56008E+02  0.12000E+02  0.82937E+00  0.45123E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -9.7988830427e+04 -7.6309698423e+04  4.0398268583e+05 -2.0186165163e+06
6.2929454079e+05 -2.6000894760e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   10     OBJ =   0.14510E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12042E+02  0.56008E+02  0.12000E+02  0.82937E+00  0.44964E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.1711717272e+05 -6.9468882658e+04  4.2784226619e+05 -1.8873407859e+06
-2.1702054335e+04 -2.7776521779e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient

** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   11     OBJ =   0.14455E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12048E+02  0.56012E+02  0.12000E+02  0.82937E+00  0.45083E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -4.7192072017e+04 -7.9727990080e+04  4.3194740447e+05 -2.0689999403e+06
5.3540539351e+05 -2.5537761025e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   12     OBJ =   0.14419E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12048E+02  0.56012E+02  0.12000E+02  0.82937E+00  0.44950E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -6.4064837832e+04 -7.3924093016e+04  4.5171836880e+05 -1.9577538119e+06
-1.2023443707e+04 -2.7043029542e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   13     OBJ =   0.14368E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12055E+02  0.56019E+02  0.12000E+02  0.82937E+00  0.45068E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.4238295165e+03 -8.4283289109e+04  4.5776851676e+05 -2.1385452420e+06
5.2777317627e+05 -2.4951403592e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   14     OBJ =   0.14334E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12055E+02  0.56020E+02  0.12000E+02  0.82937E+00  0.44938E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.5202646428e+04 -7.8503787720e+04  4.7680027040e+05 -2.0279709919e+06
-1.0524919058e+04 -2.6446942142e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   15     OBJ =   0.14282E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12057E+02  0.56032E+02  0.12000E+02  0.82937E+00  0.45100E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.8157315896e+04 -8.8048935142e+04  4.6780594486e+05 -2.1960765644e+06
6.3421360302e+05 -2.4678449299e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   16     OBJ =   0.14233E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12057E+02  0.56032E+02  0.12000E+02  0.82937E+00  0.44942E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -3.5230085348e+03 -8.1012769083e+04  4.9074329721e+05 -2.0615543692e+06
-1.8676511970e+04 -2.6496227817e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   17     OBJ =   0.14214E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12057E+02  0.56036E+02  0.12000E+02  0.82937E+00  0.45043E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  8.4446899961e+03 -8.5886966737e+04  4.7933067895e+05 -2.1506214201e+06
3.8311543649e+05 -2.5472189480e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   18     OBJ =   0.14195E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12057E+02  0.56037E+02  0.12000E+02  0.82937E+00  0.44948E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -4.6194118606e+03 -8.1657612010e+04  4.9321621639e+05 -2.0696822473e+06
-1.0420586832e+04 -2.6569147029e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   19     OBJ =   0.14133E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12058E+02  0.56050E+02  0.12000E+02  0.82937E+00  0.45116E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:

>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.4591578565e+04 -9.0347928052e+04  4.7927694641e+05 -2.2232868510e+06
6.3467479974e+05 -2.5025306878e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   20     OBJ =   0.14084E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12058E+02  0.56050E+02  0.12000E+02  0.82937E+00  0.44958E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.1359045519e+03 -8.3278948242e+04  5.0237417576e+05 -2.0881168846e+06
-2.1816470534e+04 -2.6852143652e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   21     OBJ =   0.14068E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12058E+02  0.56053E+02  0.12000E+02  0.82937E+00  0.45050E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  5.2116666631e+03 -8.7770406879e+04  4.9210254697e+05 -2.1704318804e+06
3.4632993132e+05 -2.5885067524e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   22     OBJ =   0.14052E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12058E+02  0.56054E+02  0.12000E+02  0.82937E+00  0.44963E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -6.6403209070e+03 -8.3927991443e+04  5.0477064930e+05 -2.0968656636e+06
-1.1775173264e+04 -2.6883152759e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   23     OBJ =   0.13999E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12059E+02  0.56064E+02  0.12000E+02  0.82937E+00  0.45114E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.2997118285e+04 -9.1798370556e+04  4.9214760132e+05 -2.2366805298e+06
5.7344621481e+05 -2.5430317779e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   24     OBJ =   0.13958E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12059E+02  0.56065E+02  0.12000E+02  0.82937E+00  0.44970E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -6.7490893934e+03 -8.5380525463e+04  5.1314009927e+05 -2.1139388701e+06
-2.2833675627e+04 -2.7090190122e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   25     OBJ =   0.13942E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12059E+02  0.56068E+02  0.12000E+02  0.82937E+00  0.45062E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  5.4879754836e+03 -8.9881098134e+04  5.0265699906e+05 -2.1965129455e+06
3.4809068930e+05 -2.6117621683e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   26     OBJ =   0.13926E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12059E+02  0.56068E+02  0.12000E+02  0.82937E+00  0.44975E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -6.5256363136e+03 -8.5988860415e+04  5.1548926296e+05 -2.1219904110e+06
-1.4660226514e+04 -2.7128839088e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   27     OBJ =   0.13888E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56076E+02  0.12000E+02  0.82937E+00  0.45112E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.1278932132e+04 -9.2974429072e+04  5.0237114174e+05 -2.2475567951e+06
5.2593977708e+05 -2.5761481620e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   28     OBJ =   0.13854E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56077E+02  0.12000E+02  0.82937E+00  0.44978E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.0844975159e+03 -8.7009101587e+04  5.2191007363e+05 -2.1334477824e+06
-2.8592974969e+04 -2.7305504596e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   29     OBJ =   0.13842E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56079E+02  0.12000E+02  0.82937E+00  0.45057E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  3.3903673491e+03 -9.0777315815e+04  5.1254642415e+05 -2.2030711582e+06
2.8850941742e+05 -2.6464556035e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   30     OBJ =   0.13830E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56079E+02  0.12000E+02  0.82937E+00  0.44983E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -6.7627469490e+03 -8.7493788950e+04  5.2342116353e+05 -2.1401626752e+06
-1.8059956232e+04 -2.7319846024e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   31     OBJ =   0.13803E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56085E+02  0.12000E+02  0.82937E+00  0.45098E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  8.2642030205e+03 -9.3207005116e+04  5.1148854050e+05 -2.2438549577e+06
4.3749228021e+05 -2.6146968385e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   32     OBJ =   0.13779E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12060E+02  0.56085E+02  0.12000E+02  0.82937E+00  0.44984E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.3286524922e+03 -8.8147624202e+04  5.2810860924e+05 -2.1470355450e+06
-3.3364502548e+04 -2.7458688751e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   33     OBJ =   0.13769E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56087E+02  0.12000E+02  0.82937E+00  0.45056E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.1689909782e+03 -9.1521786921e+04  5.1941250923e+05 -2.2096354890e+06
2.5404991227e+05 -2.6691609762e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   34     OBJ =   0.13760E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56087E+02  0.12000E+02  0.82937E+00  0.44988E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.0538118062e+03 -8.8543522505e+04  5.2931237953e+05 -2.1525456769e+06
-2.4420907828e+04 -2.7469014289e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   35     OBJ =   0.13743E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56091E+02  0.12000E+02  0.82937E+00  0.45079E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  4.9001115914e+03 -9.2921775345e+04  5.1900901265e+05 -2.2329653323e+06
3.3759845968e+05 -2.6517896196e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   36     OBJ =   0.13728E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56091E+02  0.12000E+02  0.82937E+00  0.44988E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.5703132859e+03 -8.8883661163e+04  5.3234070264e+05 -2.1556346618e+06
-3.8953730805e+04 -2.7567836555e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   37     OBJ =   0.13720E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56092E+02  0.12000E+02  0.82937E+00  0.45052E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  9.8909434978e+02 -9.1890445856e+04  5.2433768687e+05 -2.2116281034e+06
2.1999414934e+05 -2.6872828576e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   38     OBJ =   0.13713E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56092E+02  0.12000E+02  0.82937E+00  0.44991E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.3882623872e+03 -8.9190231337e+04  5.3335464718e+05 -2.1598340786e+06
-3.2939268047e+04 -2.7579519294e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   39     OBJ =   0.13702E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56094E+02  0.12000E+02  0.82937E+00  0.45062E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.1077559369e+03 -9.2570701817e+04  5.2469397622e+05 -2.2225084501e+06
2.5443665058e+05 -2.6813334712e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   40     OBJ =   0.13693E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56095E+02  0.12000E+02  0.82937E+00  0.44990E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.8021720558e+03 -8.9370915689e+04  5.3533324686e+05 -2.1611698127e+06
-4.4780688319e+04 -2.7648699543e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   41     OBJ =   0.13686E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56096E+02  0.12000E+02  0.82937E+00  0.45048E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.8197065445e+01 -9.2097021025e+04  5.2789198014e+05 -2.2120892348e+06
1.9205948908e+05 -2.7010209382e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   42     OBJ =   0.13680E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56096E+02  0.12000E+02  0.82937E+00  0.44991E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -7.7757721642e+03 -8.9586666894e+04  5.3631640814e+05 -2.1639027917e+06
-4.3551522233e+04 -2.7669081412e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   43     OBJ =   0.13673E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56097E+02  0.12000E+02  0.82937E+00  0.45050E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.3472927931e+02 -9.2345974950e+04  5.2882490464e+05 -2.2154090664e+06
1.9572322019e+05 -2.7024636685e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   44     OBJ =   0.13667E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56098E+02  0.12000E+02  0.82937E+00  0.44990E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -8.0877291752e+03 -8.9700340220e+04  5.3769786455e+05 -2.1646303992e+06
-5.2523898583e+04 -2.7718768742e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   45     OBJ =   0.13661E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56099E+02  0.12000E+02  0.82937E+00  0.45044E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -9.0606062703e+02 -9.2178035501e+04  5.3076293287e+05 -2.2110515840e+06
1.6464809464e+05 -2.7130712701e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   46     OBJ =   0.13656E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56099E+02  0.12000E+02  0.82937E+00  0.44991E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -8.2088056155e+03 -8.9835038583e+04  5.3867619115e+05 -2.1660359470e+06
-5.5817681422e+04 -2.7747935954e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   47     OBJ =   0.13650E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56100E+02  0.12000E+02  0.82937E+00  0.45042E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.2304208541e+03 -9.2235136561e+04  5.3190113398e+05 -2.2110505829e+06
1.5519142723e+05 -2.7175711385e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   48     OBJ =   0.13645E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56100E+02  0.12000E+02  0.82937E+00  0.44990E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -8.4689107762e+03 -8.9915358735e+04  5.3975747667e+05 -2.1664633110e+06
-6.3329124060e+04 -2.7787787971e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   49     OBJ =   0.13640E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56101E+02  0.12000E+02  0.82937E+00  0.45039E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -1.8104313520e+03 -9.2191739372e+04  5.3322617672e+05 -2.2092446003e+06
1.3797826092e+05 -2.7240301935e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   50     OBJ =   0.13636E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56101E+02  0.12000E+02  0.82937E+00  0.44989E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =   0.136359E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12061E+02  0.56101E+02  0.12000E+02  0.82937E+00  0.44989E+00  0.80938E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    3 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-3    4    6

TERMINATION CRITERION
ITER EQUALS ITMAX

NUMBER OF ITERATIONS =   50

OBJECTIVE FUNCTION WAS EVALUATED          155  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED       155  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED       50  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED   50  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation   89
------------------------------
Parameters for function evaluation 89:
1.2004157579e+01 w_top
5.6128461628e+01 hw
1.2062692723e+01 w_bot
8.2273510264e-01 t_top
4.4549441184e-01 tw
7.9599752919e-01 t_bot

(./SBOdrive /tmp/fileGNiJ0m /tmp/file0aiw3k)

Active response data for function evaluation 89:
Active set vector = { 1 1 1 }
1.9055400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   91
------------------------------
Parameters for function evaluation 91:
1.2002900409e+01 w_top
5.5925327539e+01 hw
1.2019162366e+01 w_bot
8.2441982383e-01 t_top
4.4722775929e-01 tw
7.9827643746e-01 t_bot

(./SBOdrive /tmp/fileSFX0bf /tmp/fileu2BN4b)

Active response data for function evaluation 91:
Active set vector = { 1 1 1 }
1.9068100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   93
------------------------------
Parameters for function evaluation 93:
1.2000072472e+01 w_top
5.5952240440e+01 hw
1.2065600714e+01 w_bot
8.1709964445e-01 t_top
4.5190403755e-01 tw
8.0431757778e-01 t_bot

(./SBOdrive /tmp/file4LVa86 /tmp/file02Bqr7)

Active response data for function evaluation 93:
Active set vector = { 1 1 1 }
1.9076800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   95
------------------------------
Parameters for function evaluation 95:
1.2021540609e+01 w_top
5.5973057198e+01 hw
1.2043133159e+01 w_bot
8.1653061925e-01 t_top
4.5311939603e-01 tw
8.0397147413e-01 t_bot

(./SBOdrive /tmp/fileQYLtj8 /tmp/fileaEOpm7)

Active response data for function evaluation 95:
Active set vector = { 1 1 1 }
1.9081100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   97
------------------------------
Parameters for function evaluation 97:
1.2037353863e+01 w_top
5.5824253712e+01 hw
1.2017152027e+01 w_bot
8.2236438824e-01 t_top
4.5444347905e-01 tw
7.9535406502e-01 t_bot

(./SBOdrive /tmp/file8BrMa9 /tmp/fileAjftDb)

Active response data for function evaluation 97:
Active set vector = { 1 1 1 }
1.9095300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation   99
------------------------------
Parameters for function evaluation 99:
1.2006376829e+01 w_top
5.5836833838e+01 hw
1.2059116065e+01 w_bot
8.2363753457e-01 t_top
4.5620014322e-01 tw
8.0196786106e-01 t_bot

(./SBOdrive /tmp/filembWdbj /tmp/fileEz88wk)

Active response data for function evaluation 99:
Active set vector = { 1 1 1 }
1.9100500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  101
------------------------------
Parameters for function evaluation 101:
1.2043399206e+01 w_top
5.5881223217e+01 hw
1.2039915070e+01 w_bot
8.1573682840e-01 t_top
4.5136712505e-01 tw
8.0346789641e-01 t_bot

(./SBOdrive /tmp/filekyYzFq /tmp/file4Ec27s)

Active response data for function evaluation 101:
Active set vector = { 1 1 1 }
1.9078300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  103
------------------------------
Parameters for function evaluation 103:
1.2029792804e+01 w_top
5.6040934337e+01 hw
1.2015934404e+01 w_bot
8.2010793265e-01 t_top
4.5062820640e-01 tw
7.9631760853e-01 t_bot

(./SBOdrive /tmp/file8z4luJ /tmp/fileobawlP)

Active response data for function evaluation 103:
Active set vector = { 1 1 1 }
1.9074200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  105
------------------------------
Parameters for function evaluation 105:
1.2018878808e+01 w_top
5.5849054528e+01 hw
1.2012611530e+01 w_bot
8.1888768992e-01 t_top
4.5362867860e-01 tw
7.9766012610e-01 t_bot

(./SBOdrive /tmp/fileuZ9lf2 /tmp/fileI3QVR6)

Active response data for function evaluation 105:
Active set vector = { 1 1 1 }
1.9087600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  107
------------------------------
Parameters for function evaluation 107:
1.2045240363e+01 w_top
5.6138676392e+01 hw
1.2027385789e+01 w_bot
8.2148697386e-01 t_top
4.5613684952e-01 tw
7.9845623040e-01 t_bot

(./SBOdrive /tmp/fileef0d7t /tmp/fileYTdRhC)

Active response data for function evaluation 107:
Active set vector = { 1 1 1 }
1.9092600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  109
------------------------------
Parameters for function evaluation 109:
1.2047086864e+01 w_top
5.6001031518e+01 hw
1.2001463949e+01 w_bot
8.2429421151e-01 t_top
4.4870667188e-01 tw
7.9872657498e-01 t_bot

(./SBOdrive /tmp/file8PQpRV /tmp/fileqYynK2)

Active response data for function evaluation 109:
Active set vector = { 1 1 1 }
1.9073800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  111
------------------------------
Parameters for function evaluation 111:
1.2048965929e+01 w_top
5.6029929120e+01 hw
1.2073145025e+01 w_bot
8.2309014491e-01 t_top
4.4783326845e-01 tw
8.0037799221e-01 t_bot

(./SBOdrive /tmp/file22GrLw /tmp/fileMbo43G)

Active response data for function evaluation 111:
Active set vector = { 1 1 1 }
1.9069500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  113
------------------------------
Parameters for function evaluation 113:
1.2038204245e+01 w_top
5.5864443883e+01 hw
1.2045881755e+01 w_bot
8.1974991971e-01 t_top
4.4577357867e-01 tw
7.9589704355e-01 t_bot

(./SBOdrive /tmp/file7tMgEL /tmp/fileWmuCzN)

Active response data for function evaluation 113:
Active set vector = { 1 1 1 }
1.9063100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  115
------------------------------
Parameters for function evaluation 115:
1.2030431781e+01 w_top
5.5897298936e+01 hw
1.2056185666e+01 w_bot
8.2384191935e-01 t_top
4.4851469507e-01 tw
7.9985723478e-01 t_bot

(./SBOdrive /tmp/filevIsgGO /tmp/fileCqkYlP)

Active response data for function evaluation 115:
Active set vector = { 1 1 1 }
1.9074700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  117
------------------------------
Parameters for function evaluation 117:
1.2000000000e+01 w_top
5.6019819143e+01 hw
1.2000000000e+01 w_bot
8.1531250000e-01 t_top
4.4335937500e-01 tw
8.0045218220e-01 t_bot

(./SBOdrive /tmp/filenIFo0Y /tmp/file2TnYp1)

Active response data for function evaluation 117:
Active set vector = { 1 1 1 }
1.9044200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio = 1.0013551026e+00:
<<<<< Excellent Progress, Accept Step, INCREASE Trust Region Size
*********************************************
Begin SBO Iteration Number 5

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5738569143e+01
1.2000000000e+01
8.0828125000e-01
4.3750000000e-01
7.9342093220e-01
Current Trust Region Upper Bounds
1.2075000000e+01
5.6301069143e+01
1.2112500000e+01
8.2234375000e-01
4.5332031250e-01
8.0748343220e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution

------------------------------
Begin Function Evaluation  119
------------------------------
Parameters for function evaluation 119:
1.2020226351e+01 w_top
5.6026040929e+01 hw
1.2062766205e+01 w_bot
8.0956568898e-01 t_top
4.4267110157e-01 tw
8.0306263542e-01 t_bot

(./SBOdrive /tmp/file18zMhd /tmp/filekNqGcj)

Active response data for function evaluation 119:
Active set vector = { 1 1 1 }
1.9038200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  121
------------------------------
Parameters for function evaluation 121:
1.2023359129e+01 w_top
5.5847683563e+01 hw
1.2054166848e+01 w_bot
8.1552744764e-01 t_top
4.4613332130e-01 tw
8.0427355176e-01 t_bot

(./SBOdrive /tmp/filelrRpXC /tmp/filemSr8VJ)

Active response data for function evaluation 121:
Active set vector = { 1 1 1 }
1.9060300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  123
------------------------------
Parameters for function evaluation 123:
1.2061904785e+01 w_top
5.6189480706e+01 hw
1.2007823944e+01 w_bot
8.1123271954e-01 t_top
4.4015983361e-01 tw
8.0559708328e-01 t_bot

(./SBOdrive /tmp/filetl3K8Z /tmp/file2rqsR5)

Active response data for function evaluation 123:
Active set vector = { 1 1 1 }
2.1215800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  125
------------------------------
Parameters for function evaluation 125:
1.2026612541e+01 w_top
5.5964299582e+01 hw
1.2026001584e+01 w_bot
8.2107725908e-01 t_top
4.5281245826e-01 tw
7.9620609158e-01 t_bot

(./SBOdrive /tmp/filelXL1xw /tmp/fileS0hmQF)

Active response data for function evaluation 125:
Active set vector = { 1 1 1 }
1.9084200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2 ------------------------------
Begin Function Evaluation  127
------------------------------
Parameters for function evaluation 127:
1.2043577053e+01 w_top
5.6280442938e+01 hw
1.2086053970e+01 w_bot
8.1330020281e-01 t_top
4.5217125128e-01 tw
8.0726779112e-01 t_bot

(./SBOdrive /tmp/fileT8NKG2 /tmp/fileyI0RIa)

Active response data for function evaluation 127:
Active set vector = { 1 1 1 }
1.8075000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  129
------------------------------
Parameters for function evaluation 129:
1.2004482563e+01 w_top
5.5949912927e+01 hw
1.2056504583e+01 w_bot
8.1646498845e-01 t_top
4.4052756160e-01 tw
8.0205552493e-01 t_bot

(./SBOdrive /tmp/filerq5XbI /tmp/fileoH3qHT)

Active response data for function evaluation 129:
Active set vector = { 1 1 1 }
1.9038300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  131
------------------------------
Parameters for function evaluation 131:
1.2060844425e+01 w_top
5.6290133817e+01 hw
1.2036998244e+01 w_bot
8.0872875632e-01 t_top
4.4438373279e-01 tw
7.9668832632e-01 t_bot

(./SBOdrive /tmp/fileJNnKCn /tmp/fileq8rz1x)

Active response data for function evaluation 131:
Active set vector = { 1 1 1 }
1.9038300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  133
------------------------------
Parameters for function evaluation 133:
1.2042626906e+01 w_top
5.5903864965e+01 hw
1.2102434908e+01 w_bot
8.1236148719e-01 t_top
4.4406729745e-01 tw
8.0619893531e-01 t_bot

(./SBOdrive /tmp/file3RZDdc /tmp/fileOGQC2p)

Active response data for function evaluation 133:
Active set vector = { 1 1 1 }
1.9050600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  135
------------------------------
Parameters for function evaluation 135:
1.2064548444e+01 w_top
5.5775507406e+01 hw
1.2015178823e+01 w_bot
8.1501309344e-01 t_top
4.3864630716e-01 tw
7.9878065096e-01 t_bot

(./SBOdrive /tmp/file9m5jI0 /tmp/fileqC9Cfd)

Active response data for function evaluation 135:
Active set vector = { 1 1 1 }
2.1715100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  137
------------------------------
Parameters for function evaluation 137:
1.2046799981e+01 w_top
5.5886874981e+01 hw
1.2071958185e+01 w_bot
8.2026107623e-01 t_top
4.4257995505e-01 tw
8.0539186734e-01 t_bot

(./SBOdrive /tmp/fileJTN9jY /tmp/fileKgCcre)

Active response data for function evaluation 137:
Active set vector = { 1 1 1 }
1.9053300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  139
------------------------------
Parameters for function evaluation 139:
1.2058551748e+01 w_top
5.6159535470e+01 hw
1.2065511186e+01 w_bot
8.1374036298e-01 t_top
4.3810824745e-01 tw
8.0290820848e-01 t_bot

(./SBOdrive /tmp/filedv0QGV /tmp/file06Bzva)

Active response data for function evaluation 139:
Active set vector = { 1 1 1 }
2.3747200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  141
------------------------------
Parameters for function evaluation 141:
1.2016724245e+01 w_top
5.5860644246e+01 hw
1.2045085803e+01 w_bot
8.1445031761e-01 t_top
4.4683620334e-01 tw
7.9779911547e-01 t_bot

(./SBOdrive /tmp/file3D4Zk2 /tmp/filekVBlAk)

Active response data for function evaluation 141:
Active set vector = { 1 1 1 }
1.9060600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  143
------------------------------
Parameters for function evaluation 143:
1.2073477469e+01 w_top
5.6043289251e+01 hw
1.2034817118e+01 w_bot
8.1206322828e-01 t_top
4.5247559090e-01 tw
8.0154454699e-01 t_bot

(./SBOdrive /tmp/file1mNpL8 /tmp/file6bjaTp)

Active response data for function evaluation 143:
Active set vector = { 1 1 1 }
1.9076200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  145
------------------------------
Parameters for function evaluation 145:
1.2039439439e+01 w_top
5.5981475583e+01 hw
1.2073975777e+01 w_bot
8.1914110616e-01 t_top
4.4125319444e-01 tw
7.9415105682e-01 t_bot

(./SBOdrive /tmp/filehhbOmo /tmp/file8gcMVI)

Active response data for function evaluation 145:
Active set vector = { 1 1 1 }
1.9044300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed

<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.55739E+02  0.12000E+02  0.80828E+00  0.43750E+00  0.79342E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12075E+02  0.56301E+02  0.12113E+02  0.82234E+00  0.45332E+00  0.80748E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.190376E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56020E+02  0.12000E+02  0.81531E+00  0.44336E+00  0.80045E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  9.2044861048e+05  4.2585021462e+04 -9.7608170609e+04  8.6148359694e+05
4.5206345989e+06  5.8475225704e+06 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =   0.12372E+05

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56020E+02  0.12000E+02  0.81272E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.6164480226e+06  8.4193408529e+04 -4.2485997646e+05  1.5551683298e+07
1.5880412722e+07  2.1559379577e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =  -0.59456E+05

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56020E+02  0.12000E+02  0.80874E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.4909114183e+06  1.3875508435e+05 -4.6319335206e+05  2.0465088412e+07
2.0084852037e+07  2.4547600622e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =  -0.69094E+05

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56020E+02  0.12001E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.4759412027e+06  1.4525187041e+05 -4.6704417446e+05  2.1034974660e+07
2.0571614329e+07  2.4898769712e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =  -0.11782E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55943E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.1713032016e+06  2.9401103659e+05 -9.6576957486e+04  2.3177508801e+07
2.1554715820e+07  2.8964662629e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =  -0.18244E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55739E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.3312303039e+06  3.3746042954e+05 -3.3828401821e+05  2.5974961483e+07
2.2980099415e+07  2.7941492954e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    6     OBJ =  -0.18244E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55739E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

ITER =    7     OBJ =  -0.18244E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55739E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.3312303039e+06  3.3746042954e+05 -3.3828401821e+05  2.5974961483e+07
2.2980099415e+07  2.7941492954e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    8     OBJ =  -0.18244E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55739E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =  -0.182443E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.55739E+02  0.12113E+02  0.80828E+00  0.43750E+00  0.79342E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1   -2    3   -4   -5   -6

TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR  3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1))   LESS THAN DABFUN FOR  3 ITERATIONS

NUMBER OF ITERATIONS =    8

OBJECTIVE FUNCTION WAS EVALUATED           21  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED        21  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED        7  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED    7  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation  147
------------------------------
Parameters for function evaluation 147:
1.2012427942e+01 w_top
5.6035777775e+01 hw
1.2050576612e+01 w_bot
8.1844147180e-01 t_top
4.4114119036e-01 tw
7.9773653978e-01 t_bot

(./SBOdrive /tmp/fileD8zi4H /tmp/fileMvWPt4)

Active response data for function evaluation 147:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  149
------------------------------
Parameters for function evaluation 149:
1.2035531427e+01 w_top
5.6024567848e+01 hw
1.2005999581e+01 w_bot
8.1186581964e-01 t_top
4.3996141417e-01 tw
8.0064288034e-01 t_bot

(./SBOdrive /tmp/fileNjamy9 /tmp/fileAB1rEu)

Active response data for function evaluation 149:
Active set vector = { 1 1 1 }
2.0876400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  151
------------------------------
Parameters for function evaluation 151:
1.2029289750e+01 w_top
5.5881282622e+01 hw
1.2001770722e+01 w_bot
8.1670963508e-01 t_top
4.4758453382e-01 tw
8.0030784379e-01 t_bot

(./SBOdrive /tmp/fileBdPfJA /tmp/filewfMppZ)

Active response data for function evaluation 151:
Active set vector = { 1 1 1 }
1.9065300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  153
------------------------------
Parameters for function evaluation 153:
1.2008012115e+01 w_top
5.6151479633e+01 hw
1.2017090100e+01 w_bot
8.1459300393e-01 t_top
4.4370790020e-01 tw
8.0293333133e-01 t_bot

(./SBOdrive /tmp/fileNtNkqd /tmp/fileGimjbD)

Active response data for function evaluation 153:
Active set vector = { 1 1 1 }
1.9041800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  155
------------------------------
Parameters for function evaluation 155:
1.2002559615e+01 w_top
5.6113285028e+01 hw
1.2041315941e+01 w_bot
8.1267677045e-01 t_top
4.3964063775e-01 tw
8.0256254766e-01 t_bot

(./SBOdrive /tmp/fileTS5qGN /tmp/fileofEt9b)

Active response data for function evaluation 155:
Active set vector = { 1 1 1 }
2.1604600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  157
------------------------------
Parameters for function evaluation 157:
1.2023438797e+01 w_top
5.6002493953e+01 hw
1.2043171184e+01 w_bot
8.1284533909e-01 t_top
4.4801885465e-01 tw
8.0328976361e-01 t_bot

(./SBOdrive /tmp/fileXGRZ4w /tmp/filequ2w7Y)

Active response data for function evaluation 157:
Active set vector = { 1 1 1 }
1.9059900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  159
------------------------------
Parameters for function evaluation 159:
1.2025474911e+01 w_top
5.6070858947e+01 hw
1.2035637854e+01 w_bot
8.1645345819e-01 t_top
4.4170555748e-01 tw
8.0094879064e-01 t_bot

(./SBOdrive /tmp/filel5undg /tmp/fileMHy5ZG)

Active response data for function evaluation 159:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2 ------------------------------
Begin Function Evaluation  161
------------------------------
Parameters for function evaluation 161:
1.2027246172e+01 w_top
5.5966902406e+01 hw
1.2020388650e+01 w_bot
8.1316365548e-01 t_top
4.3910962540e-01 tw
8.0384108328e-01 t_bot

(./SBOdrive /tmp/fileVLQoq8 /tmp/fileUzL3CC)

Active response data for function evaluation 161:
Active set vector = { 1 1 1 }
2.1785700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  163
------------------------------
Parameters for function evaluation 163:
1.2015894004e+01 w_top
5.5904792439e+01 hw
1.2012852265e+01 w_bot
8.1585154293e-01 t_top
4.4029987040e-01 tw
8.0225621952e-01 t_bot

(./SBOdrive /tmp/filefqh9B0 /tmp/filegZT8Ft)

Active response data for function evaluation 163:
Active set vector = { 1 1 1 }
1.9038900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  165
------------------------------
Parameters for function evaluation 165:
1.2000599733e+01 w_top
5.5933159597e+01 hw
1.2025313200e+01 w_bot
8.1621979151e-01 t_top
4.4412591242e-01 tw
7.9747334663e-01 t_bot

(./SBOdrive /tmp/fileDoFrU1 /tmp/fileSg4ipy)

Active response data for function evaluation 165:
Active set vector = { 1 1 1 }
1.9050000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  167
------------------------------
Parameters for function evaluation 167:
1.2021277183e+01 w_top
5.6018767657e+01 hw
1.2023316509e+01 w_bot
8.1720791795e-01 t_top
4.3886436086e-01 tw
8.0151402073e-01 t_bot

(./SBOdrive /tmp/fileFHzk72 /tmp/fileqsC6jy)

Active response data for function evaluation 167:
Active set vector = { 1 1 1 }
2.2287700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  169
------------------------------
Parameters for function evaluation 169:
1.2032650725e+01 w_top
5.5950290617e+01 hw
1.2008822290e+01 w_bot
8.1408168721e-01 t_top
4.4402068677e-01 tw
8.0139666912e-01 t_bot

(./SBOdrive /tmp/file5gCoqd /tmp/fileaWcacM)

Active response data for function evaluation 169:
Active set vector = { 1 1 1 }
1.9049400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  171
------------------------------
Parameters for function evaluation 171:
1.2009762542e+01 w_top
5.5927328095e+01 hw
1.2038297713e+01 w_bot
8.1767554805e-01 t_top
4.4683216950e-01 tw
7.9727548314e-01 t_bot

(./SBOdrive /tmp/filenY08un /tmp/fileonaO0U)

Active response data for function evaluation 171:
Active set vector = { 1 1 1 }
1.9061200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  173
------------------------------
Parameters for function evaluation 173:
1.2019695875e+01 w_top
5.6092770144e+01 hw
1.2003053215e+01 w_bot
8.1798968670e-01 t_top
4.4699522471e-01 tw
7.9997663689e-01 t_bot

(./SBOdrive /tmp/fileBXxQYG /tmp/filec1Ii2h)

Active response data for function evaluation 173:
Active set vector = { 1 1 1 }
1.9058100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  175
------------------------------
Parameters for function evaluation 175:
1.2000000000e+01 w_top
5.5996491627e+01 hw
1.2000001085e+01 w_bot
8.1179687500e-01 t_top
4.4554561670e-01 tw
8.0200698534e-01 t_bot

(./SBOdrive /tmp/filejSgbP2 /tmp/fileGSR3dF)

Active response data for function evaluation 175:
Active set vector = { 1 1 1 }
1.9049100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio = -8.4662702547e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 7

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5949506643e+01
1.2000000000e+01
8.1355468750e-01
4.4086914063e-01
7.9869436970e-01
Current Trust Region Upper Bounds
1.2018750000e+01
5.6090131643e+01
1.2028125000e+01
8.1707031250e-01
4.4584960938e-01
8.0220999470e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution

------------------------------
Begin Function Evaluation  177
------------------------------
Parameters for function evaluation 177:
1.2001531104e+01 w_top
5.6010343931e+01 hw
1.2024695474e+01 w_bot
8.1599906303e-01 t_top
4.4267553609e-01 tw
8.0125761926e-01 t_bot

(./SBOdrive /tmp/file5KDL2F /tmp/filecncZWl)

Active response data for function evaluation 177:
Active set vector = { 1 1 1 }
1.9043000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  179
------------------------------
Parameters for function evaluation 179:
1.2007540268e+01 w_top
5.6085592884e+01 hw
1.2022885515e+01 w_bot
8.1580939852e-01 t_top
4.4352440064e-01 tw
8.0208625094e-01 t_bot

(./SBOdrive /tmp/file50s87i /tmp/fileAFCNVX)

Active response data for function evaluation 179:
Active set vector = { 1 1 1 }
1.9044100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  181
------------------------------
Parameters for function evaluation 181:
1.2015776288e+01 w_top
5.6064637549e+01 hw
1.2012012900e+01 w_bot
8.1499743944e-01 t_top
4.4506931703e-01 tw
8.0117193820e-01 t_bot

(./SBOdrive /tmp/filezgSnk5 /tmp/fileOglVwN)

Active response data for function evaluation 181:
Active set vector = { 1 1 1 }
1.9049500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  183
------------------------------
Parameters for function evaluation 183:
1.2017992727e+01 w_top
5.6076034072e+01 hw
1.2005315720e+01 w_bot
8.1653330019e-01 t_top
4.4359152641e-01 tw
7.9936081693e-01 t_bot

(./SBOdrive /tmp/filelrI0nR /tmp/filemMDzry)

Active response data for function evaluation 183:
Active set vector = { 1 1 1 }
1.9045700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  185
------------------------------
Parameters for function evaluation 185:
1.2000518489e+01 w_top
5.5973135635e+01 hw
1.2017858728e+01 w_bot
8.1700184339e-01 t_top
4.4305146468e-01 tw
8.0138820341e-01 t_bot

(./SBOdrive /tmp/filenmSPHM /tmp/fileYxBjcx)

Active response data for function evaluation 185:
Active set vector = { 1 1 1 }
1.9046100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  187
------------------------------
Parameters for function evaluation 187:
1.2016531615e+01 w_top
5.5985664324e+01 hw
1.2021694265e+01 w_bot
8.1368692102e-01 t_top
4.4570911058e-01 tw
8.0156783142e-01 t_bot

(./SBOdrive /tmp/file1g61XH /tmp/fileGcs2er)

Active response data for function evaluation 187:
Active set vector = { 1 1 1 }
1.9052800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  189
------------------------------
Parameters for function evaluation 189:
1.2018531271e+01 w_top
5.6074383932e+01 hw
1.2020087027e+01 w_bot
8.1381792143e-01 t_top
4.4234541081e-01 tw
8.0098627224e-01 t_bot

(./SBOdrive /tmp/filefgFCBM /tmp/fileK09Msz)

Active response data for function evaluation 189:
Active set vector = { 1 1 1 }
1.9039300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  191
------------------------------
Parameters for function evaluation 191:
1.2011377535e+01 w_top
5.5967725259e+01 hw
1.2023786211e+01 w_bot
8.1358732846e-01 t_top
4.4140709085e-01 tw
7.9884276618e-01 t_bot

(./SBOdrive /tmp/filenqBDWQ /tmp/fileyKxkvC)

Active response data for function evaluation 191:
Active set vector = { 1 1 1 }
1.9038400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  193
------------------------------
Parameters for function evaluation 193:
1.2008266114e+01 w_top
5.6046448778e+01 hw
1.2001200998e+01 w_bot
8.1537530318e-01 t_top
4.4495653814e-01 tw
8.0164132784e-01 t_bot

(./SBOdrive /tmp/filejMcTy4 /tmp/fileO0a3xT)

Active response data for function evaluation 193:
Active set vector = { 1 1 1 }
1.9049500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  195
------------------------------
Parameters for function evaluation 195:
1.2006134133e+01 w_top
5.6037696182e+01 hw
1.2012880745e+01 w_bot
8.1455899476e-01 t_top
4.4536523589e-01 tw
7.9981838575e-01 t_bot

(./SBOdrive /tmp/filePCEoZh /tmp/fileO220R5)

Active response data for function evaluation 195:
Active set vector = { 1 1 1 }
1.9050200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  197
------------------------------
Parameters for function evaluation 197:
1.2009198805e+01 w_top
5.5998981763e+01 hw
1.2003182381e+01 w_bot
8.1527927248e-01 t_top
4.4165590386e-01 tw
8.0199835563e-01 t_bot

(./SBOdrive /tmp/file9c1VOz /tmp/fileCAwo6q)

Active response data for function evaluation 197:
Active set vector = { 1 1 1 }
1.9039800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  199
------------------------------
Parameters for function evaluation 199:
1.2003959129e+01 w_top
5.6021305414e+01 hw
1.2000076930e+01 w_bot
8.1683978906e-01 t_top
4.4409963629e-01 tw
8.0004872165e-01 t_bot

(./SBOdrive /tmp/fileZR3pY0 /tmp/filemI1m6Q)

Active response data for function evaluation 199:
Active set vector = { 1 1 1 }
1.9048300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  201
------------------------------
Parameters for function evaluation 201:
1.2002450138e+01 w_top
5.6008194187e+01 hw
1.2016702505e+01 w_bot
8.1456103326e-01 t_top
4.4472159035e-01 tw
8.0032767626e-01 t_bot

(./SBOdrive /tmp/fileDW0o5r /tmp/fileAX0KCl)

Active response data for function evaluation 201:
Active set vector = { 1 1 1 }
1.9048600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  203
------------------------------
Parameters for function evaluation 203:
1.2009603169e+01 w_top
5.6068863718e+01 hw
1.2015315265e+01 w_bot
8.1469224505e-01 t_top
4.4122107114e-01 tw
7.9957945028e-01 t_bot

(./SBOdrive /tmp/filejmKtj2 /tmp/fileSGaLJU)

Active response data for function evaluation 203:
Active set vector = { 1 1 1 }
1.9035800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed

<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.55950E+02  0.12000E+02  0.81355E+00  0.44087E+00  0.79869E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12019E+02  0.56090E+02  0.12028E+02  0.81707E+00  0.44585E+00  0.80221E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.190442E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56020E+02  0.12000E+02  0.81531E+00  0.44336E+00  0.80045E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6492035150e+02 -2.9823288839e+02 -6.2653656593e+01  9.1304288136e+03
3.3802692326e+04 -2.3985669685e+01 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =   0.19034E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56023E+02  0.12001E+02  0.81355E+00  0.44087E+00  0.80073E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  9.1238416702e+02 -3.1493927770e+02 -6.0436370422e+01  9.6373791203e+03
3.3560230721e+04  1.6436164742e+02 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =   0.19032E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12014E+02  0.81355E+00  0.44087E+00  0.79869E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  9.7330784648e+02 -2.9054866196e+02  2.6775538742e+01  1.0434255181e+04
3.3580582729e+04 -4.3938395773e+02 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =   0.19032E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12013E+02  0.81355E+00  0.44087E+00  0.80026E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.0061941723e+03 -2.9495400976e+02  4.7281670734e+01  1.0259482805e+04
3.3636523747e+04  1.3013682543e+01 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =   0.19032E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81355E+00  0.44087E+00  0.79989E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  9.9542440711e+02 -2.9467606106e+02  3.4172670915e+01  1.0281353519e+04
3.3632669549e+04 -1.1244973409e+02 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =   0.19032E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81355E+00  0.44087E+00  0.80034E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =   0.190320E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81355E+00  0.44087E+00  0.80034E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    4 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1    2   -4   -5

TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR  3 ITERATIONS

NUMBER OF ITERATIONS =    5

OBJECTIVE FUNCTION WAS EVALUATED           20  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED        20  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED        5  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED    5  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation  205
------------------------------
Parameters for function evaluation 205:
1.2024267101e+01 w_top
5.6011584610e+01 hw
1.2033758480e+01 w_bot
8.1590997923e-01 t_top
4.3955752106e-01 tw
8.0217365911e-01 t_bot

(./SBOdrive /tmp/filed7aL8I /tmp/fileMzhSHF)

Active response data for function evaluation 205:
Active set vector = { 1 1 1 }
2.1373800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2 ------------------------------
Begin Function Evaluation  207
------------------------------
Parameters for function evaluation 207:
1.2003856824e+01 w_top
5.6141376449e+01 hw
1.2040915953e+01 w_bot
8.1578305078e-01 t_top
4.4424681668e-01 tw
7.9947161159e-01 t_bot

(./SBOdrive /tmp/fileB50Nrq /tmp/fileUzEtLl)

Active response data for function evaluation 207:
Active set vector = { 1 1 1 }
1.9044700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  209
------------------------------
Parameters for function evaluation 209:
1.2009803439e+01 w_top
5.6193448454e+01 hw
1.2053905073e+01 w_bot
8.1229426082e-01 t_top
4.4385273589e-01 tw
7.9929624370e-01 t_bot

(./SBOdrive /tmp/filefOGr1e /tmp/filewRC3Eb)

Active response data for function evaluation 209:
Active set vector = { 1 1 1 }
1.9039200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  211
------------------------------
Parameters for function evaluation 211:
1.2004590041e+01 w_top
5.6145227506e+01 hw
1.2029932035e+01 w_bot
8.1380987552e-01 t_top
4.3815774609e-01 tw
8.0026087422e-01 t_bot

(./SBOdrive /tmp/filevqzWR3 /tmp/fileKKAeA1)

Active response data for function evaluation 211:
Active set vector = { 1 1 1 }
2.3605100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  213
------------------------------
Parameters for function evaluation 213:
1.2023543895e+01 w_top
5.6020793322e+01 hw
1.2002139607e+01 w_bot
8.1433580544e-01 t_top
4.4084845626e-01 tw
7.9870759550e-01 t_bot

(./SBOdrive /tmp/fileZzvE93 /tmp/fileeIEVp5)

Active response data for function evaluation 213:
Active set vector = { 1 1 1 }
1.9036400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  215
------------------------------
Parameters for function evaluation 215:
1.2010258246e+01 w_top
5.6071553438e+01 hw
1.2012685076e+01 w_bot
8.1259849192e-01 t_top
4.3831780430e-01 tw
7.9778422685e-01 t_bot

(./SBOdrive /tmp/fileTkuFz4 /tmp/fileOw4QF4)

Active response data for function evaluation 215:
Active set vector = { 1 1 1 }
2.3133300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  217
------------------------------
Parameters for function evaluation 217:
1.2018602489e+01 w_top
5.6047929946e+01 hw
1.2006729570e+01 w_bot
8.1187022551e-01 t_top
4.4334673550e-01 tw
8.0158697677e-01 t_bot

(./SBOdrive /tmp/file96LWZd /tmp/fileExu5vh)

Active response data for function evaluation 217:
Active set vector = { 1 1 1 }
1.9041600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  219
------------------------------
Parameters for function evaluation 219:
1.2027189448e+01 w_top
5.5990616792e+01 hw
1.2017103106e+01 w_bot
8.1153368733e-01 t_top
4.4156737484e-01 tw
7.9861186201e-01 t_bot

(./SBOdrive /tmp/file9IWQnn /tmp/file6BMFHp)

Active response data for function evaluation 219:
Active set vector = { 1 1 1 }
1.9037400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  221
------------------------------
Parameters for function evaluation 221:
1.2023034429e+01 w_top
5.6129389731e+01 hw
1.2001922592e+01 w_bot
8.1538166086e-01 t_top
4.3911005242e-01 tw
8.0034819794e-01 t_bot

(./SBOdrive /tmp/filexFpAqG /tmp/fileeMoepM)

Active response data for function evaluation 221:
Active set vector = { 1 1 1 }
2.2341100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  223
------------------------------
Parameters for function evaluation 223:
1.2017143797e+01 w_top
5.6087828483e+01 hw
1.2043842938e+01 w_bot
8.1611712254e-01 t_top
4.4269205193e-01 tw
8.0267868651e-01 t_bot

(./SBOdrive /tmp/fileJdPcoZ /tmp/fileuyEY43)

Active response data for function evaluation 223:
Active set vector = { 1 1 1 }
1.9042200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  225
------------------------------
Parameters for function evaluation 225:
1.2011842403e+01 w_top
5.6077813593e+01 hw
1.2013925772e+01 w_bot
8.1451241388e-01 t_top
4.4446260936e-01 tw
8.0088370447e-01 t_bot

(./SBOdrive /tmp/fileVtnuwr /tmp/file8GvlKz)

Active response data for function evaluation 225:
Active set vector = { 1 1 1 }
1.9046400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  227
------------------------------
Parameters for function evaluation 227:
1.2000016531e+01 w_top
5.6185604305e+01 hw
1.2036599437e+01 w_bot
8.1267444819e-01 t_top
4.4191793889e-01 tw
8.0101358625e-01 t_bot

(./SBOdrive /tmp/fileVp0fRT /tmp/file4KhjS0)

Active response data for function evaluation 227:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  229
------------------------------
Parameters for function evaluation 229:
1.2013747264e+01 w_top
5.6037515789e+01 hw
1.2038849560e+01 w_bot
8.1211922284e-01 t_top
4.4154708104e-01 tw
8.0010709926e-01 t_bot

(./SBOdrive /tmp/fileTy45mv /tmp/filecphKVF)

Active response data for function evaluation 229:
Active set vector = { 1 1 1 }
1.9035800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  231
------------------------------
Parameters for function evaluation 231:
1.2021067347e+01 w_top
5.6109399997e+01 hw
1.2028832891e+01 w_bot
8.1327328347e-01 t_top
4.4118279322e-01 tw
7.9817164146e-01 t_bot

(./SBOdrive /tmp/fileM3fphL /tmp/file6rMWrN)

Active response data for function evaluation 231:
Active set vector = { 1 1 1 }
1.9034000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  233
------------------------------
Parameters for function evaluation 233:
1.2000000000e+01 w_top
5.6048633687e+01 hw
1.2000000000e+01 w_bot
8.1091796875e-01 t_top
4.4460449219e-01 tw
8.0297654949e-01 t_bot

(./SBOdrive /tmp/fileUB9dCS /tmp/fileemVBCW)

Active response data for function evaluation 233:
Active set vector = { 1 1 1 }
1.9043700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio = -9.8338564179e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 9

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.6037397268e+01
1.2000000000e+01
8.1223632813e-01
4.3900146484e-01
7.9902147137e-01
Current Trust Region Upper Bounds
1.2014062500e+01
5.6142866018e+01
1.2033071784e+01
8.1487304688e-01
4.4273681641e-01
8.0165819012e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution

------------------------------
Begin Function Evaluation  235
------------------------------
Parameters for function evaluation 235:
1.2001882076e+01 w_top
5.6118787660e+01 hw
1.2017088071e+01 w_bot
8.1447356692e-01 t_top
4.4067093860e-01 tw
8.0025314918e-01 t_bot

(./SBOdrive /tmp/fileWudWB8 /tmp/fileKomvvb)

Active response data for function evaluation 235:
Active set vector = { 1 1 1 }
2.0294000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  237
------------------------------
Parameters for function evaluation 237:
1.2000857445e+01 w_top
5.6094198162e+01 hw
1.2028219705e+01 w_bot
8.1389422802e-01 t_top
4.3997849629e-01 tw
7.9916098828e-01 t_bot

(./SBOdrive /tmp/fileQzRkKt /tmp/fileu2uB7z)

Active response data for function evaluation 237:
Active set vector = { 1 1 1 }
2.1095600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  239
------------------------------
Parameters for function evaluation 239:
1.2002290989e+01 w_top
5.6072252428e+01 hw
1.2010336287e+01 w_bot
8.1356277995e-01 t_top
4.4259678479e-01 tw
8.0047603934e-01 t_bot

(./SBOdrive /tmp/fileqwOukT /tmp/fileWwPlzY)

Active response data for function evaluation 239:
Active set vector = { 1 1 1 }
1.9038800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  241
------------------------------
Parameters for function evaluation 241:
1.2008060134e+01 w_top
5.6042157513e+01 hw
1.2000726356e+01 w_bot
8.1285383104e-01 t_top
4.4051220966e-01 tw
7.9964715532e-01 t_bot

(./SBOdrive /tmp/fileicE7Fi /tmp/fileicP5jr)

Active response data for function evaluation 241:
Active set vector = { 1 1 1 }
2.0220700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  243
------------------------------
Parameters for function evaluation 243:
1.2009276677e+01 w_top
5.6078216467e+01 hw
1.2024468711e+01 w_bot
8.1332713077e-01 t_top
4.4147638486e-01 tw
7.9953177125e-01 t_bot

(./SBOdrive /tmp/file8uOEkR /tmp/fileUTxcQY)

Active response data for function evaluation 243:
Active set vector = { 1 1 1 }
1.9035200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  245
------------------------------
Parameters for function evaluation 245:
1.2013416299e+01 w_top
5.6037777168e+01 hw
1.2012031574e+01 w_bot
8.1454144318e-01 t_top
4.3944157451e-01 tw
7.9904346162e-01 t_bot

(./SBOdrive /tmp/fileGCTl1p /tmp/fileCMqaYA)

Active response data for function evaluation 245:
Active set vector = { 1 1 1 }
2.1591100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  247
------------------------------
Parameters for function evaluation 247:
1.2011228487e+01 w_top
5.6106326401e+01 hw
1.2022185164e+01 w_bot
8.1275334980e-01 t_top
4.4167563118e-01 tw
8.0001104252e-01 t_bot

(./SBOdrive /tmp/fileqyt5Q7 /tmp/fileCEoTDh)

Active response data for function evaluation 247:
Active set vector = { 1 1 1 }
1.9034700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  249
------------------------------
Parameters for function evaluation 249:
1.2004852333e+01 w_top
5.6046680381e+01 hw
1.2020875992e+01 w_bot
8.1318905617e-01 t_top
4.4125839655e-01 tw
8.0127459871e-01 t_bot

(./SBOdrive /tmp/fileUDaWCP /tmp/fileeWl8U2)

Active response data for function evaluation 249:
Active set vector = { 1 1 1 }
1.9035000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  251
------------------------------
Parameters for function evaluation 251:
1.2004236235e+01 w_top
5.6104933461e+01 hw
1.2006606541e+01 w_bot
8.1232179085e-01 t_top
4.4159231072e-01 tw
7.9927050654e-01 t_bot

(./SBOdrive /tmp/fileMzUXQG /tmp/fileMDlR0S)

Active response data for function evaluation 251:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  253
------------------------------
Parameters for function evaluation 253:
1.2003710066e+01 w_top
5.6056171776e+01 hw
1.2032271811e+01 w_bot
8.1373806658e-01 t_top
4.3932774379e-01 tw
8.0016671243e-01 t_bot

(./SBOdrive /tmp/fileCKw9Lx /tmp/file63f6iN)

Active response data for function evaluation 253:
Active set vector = { 1 1 1 }
2.1802800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  255
------------------------------
Parameters for function evaluation 255:
1.2003435360e+01 w_top
5.6062522883e+01 hw
1.2008041691e+01 w_bot
8.1383702879e-01 t_top
4.4031092832e-01 tw
7.9979120477e-01 t_bot

(./SBOdrive /tmp/fileK4gz0x /tmp/fileMH1frM)

Active response data for function evaluation 255:
Active set vector = { 1 1 1 }
2.0555800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  257
------------------------------
Parameters for function evaluation 257:
1.2010484000e+01 w_top
5.6125570109e+01 hw
1.2004972367e+01 w_bot
8.1396277223e-01 t_top
4.4226252211e-01 tw
7.9987160819e-01 t_bot

(./SBOdrive /tmp/fileohN52x /tmp/fileyQ9HSP)

Active response data for function evaluation 257:
Active set vector = { 1 1 1 }
1.9037100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  259
------------------------------
Parameters for function evaluation 259:
1.2005596799e+01 w_top
5.6137619532e+01 hw
1.2010986779e+01 w_bot
8.1419012251e-01 t_top
4.4099168920e-01 tw
8.0145288612e-01 t_bot

(./SBOdrive /tmp/filecjpZrH /tmp/fileCwYn9X)

Active response data for function evaluation 259:
Active set vector = { 1 1 1 }
1.9032500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  261
------------------------------
Parameters for function evaluation 261:
1.2006777534e+01 w_top
5.6092036993e+01 hw
1.2001758536e+01 w_bot
8.1426523082e-01 t_top
4.3922894892e-01 tw
8.0074841628e-01 t_bot

(./SBOdrive /tmp/file6DNXwQ /tmp/fileWGSUCa)

Active response data for function evaluation 261:
Active set vector = { 1 1 1 }
2.2052100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed

<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.56037E+02  0.12000E+02  0.81224E+00  0.43900E+00  0.79902E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12014E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.44274E+00  0.80166E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.190875E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81355E+00  0.44087E+00  0.80034E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  2.8762557491e+06  4.7174950008e+04 -4.2418143609e+04  7.9797936737e+06
2.9394703276e+06 -1.2334574254e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =   0.16903E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81270E+00  0.44055E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  5.5731561295e+06 -1.9023965511e+05  6.1420622460e+05 -4.8966649530e+06
8.1712411332e+05 -1.8902619718e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =   0.16732E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81329E+00  0.44046E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  5.3860348209e+06 -1.5158153770e+05  6.3544760705e+04  1.0705982354e+06
4.8769864849e+06 -1.9743383159e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =   0.16460E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81306E+00  0.43937E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6452411922e+06 -3.0180355249e+05  4.5053124294e+05 -9.1149729655e+06
2.1387576587e+06 -2.2940609516e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =   0.15830E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81418E+00  0.43911E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.4378381936e+06 -2.3768699778e+05 -5.7870113657e+05  1.5847683700e+06
9.7463529181e+06 -2.4787280195e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =   0.15722E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81413E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6844972952e+06 -2.5536944866e+05 -5.1156163497e+05  2.1689965027e+05
9.2665509191e+06 -2.5088356925e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    6     OBJ =   0.15717E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81405E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.7290843479e+06 -2.6228808370e+05 -4.3434931502e+05 -7.6515538715e+05
8.7324784001e+06 -2.5056046985e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    7     OBJ =   0.15705E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81419E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6424370209e+06 -2.5124718594e+05 -5.5747017220e+05  6.4337407420e+05
9.6556636878e+06 -2.5192427170e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    8     OBJ =   0.15702E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81412E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6812800487e+06 -2.5669849187e+05 -4.9661414467e+05 -9.3298211804e+04
9.2176408414e+06 -2.5146730928e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    9     OBJ =   0.15657E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12013E+02  0.81424E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.5945767109e+06 -2.4880861445e+05 -5.8401925942e+05  6.5036762194e+05
9.9884392140e+06 -2.5380691656e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   10     OBJ =   0.15655E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12013E+02  0.81417E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6402464120e+06 -2.5522013219e+05 -5.1234090188e+05 -2.1842834007e+05
9.4727663349e+06 -2.5327230337e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   11     OBJ =   0.15622E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12014E+02  0.81456E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.3773620121e+06 -2.2539192081e+05 -8.4259262880e+05  3.2449217613e+06
1.2086575667e+07 -2.5857594368e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   12     OBJ =   0.15582E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12014E+02  0.81429E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.5442120512e+06 -2.4785749139e+05 -5.9174605156e+05  2.7635413712e+05
1.0249923500e+07 -2.5632379446e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   13     OBJ =   0.15576E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12014E+02  0.81420E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.5998703853e+06 -2.5626917290e+05 -4.9698721754e+05 -9.2195090433e+05
9.5890602516e+06 -2.5586903665e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   14     OBJ =   0.15562E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12014E+02  0.81438E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.4823141313e+06 -2.4120306656e+05 -6.6447470011e+05  9.9581003965e+05
1.0842822191e+07 -2.5770301910e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   15     OBJ =   0.15557E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56091E+02  0.12014E+02  0.81430E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.5363676659e+06 -2.4866312650e+05 -5.8097590212e+05 -7.9269931913e+03
1.0238058701e+07 -2.5703290738e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   16     OBJ =   0.15468E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12015E+02  0.81431E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.5045487936e+06 -2.5102199109e+05 -5.4759417684e+05 -9.6937749379e+05
1.0233976468e+07 -2.5962861473e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   17     OBJ =   0.15452E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12016E+02  0.81450E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.3865049060e+06 -2.3588282065e+05 -7.1555345806e+05  9.5121176078e+05
1.1491568298e+07 -2.6147362122e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   18     OBJ =   0.15447E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12016E+02  0.81439E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.4510260216e+06 -2.4481341421e+05 -6.1536621923e+05 -2.5719396125e+05
1.0767199589e+07 -2.6068612614e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   19     OBJ =   0.15407E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12016E+02  0.81466E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.2696007299e+06 -2.2389746815e+05 -8.4324903871e+05  2.1215219115e+06
1.2566319084e+07 -2.6431802012e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   20     OBJ =   0.15389E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12016E+02  0.81449E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.3788253355e+06 -2.3870287300e+05 -6.7757618981e+05  1.5005081019e+05
1.1357332330e+07 -2.6288086872e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   21     OBJ =   0.15363E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56092E+02  0.12017E+02  0.81433E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.4699347766e+06 -2.5422252372e+05 -4.9776588260e+05 -2.2847183925e+06
1.0164071200e+07 -2.6277145158e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   22     OBJ =   0.15325E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56093E+02  0.12017E+02  0.81462E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.2836152919e+06 -2.2981408212e+05 -7.6915034549e+05  8.6484001241e+05
1.2176446593e+07 -2.6551618637e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   23     OBJ =   0.15320E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56093E+02  0.12017E+02  0.81452E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.3425410223e+06 -2.3801362247e+05 -6.7687231896e+05 -2.5408397012e+05
1.1511216356e+07 -2.6481598423e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   24     OBJ =   0.15129E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56093E+02  0.12019E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.0836357586e+06 -2.1402395789e+05 -9.2145418478e+05  1.6003385638e+06
1.3716782059e+07 -2.7208464251e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   25     OBJ =   0.15117E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56093E+02  0.12019E+02  0.81472E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.1802300914e+06 -2.2721312318e+05 -7.7329359270e+05 -1.7536431865e+05
1.2639882625e+07 -2.7085415885e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   26     OBJ =   0.14531E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56095E+02  0.12026E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  6.9796931092e+06 -2.3013377636e+05 -6.4986949471e+05 -4.7558609901e+06
1.2926785309e+07 -2.8446990366e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   27     OBJ =   0.13538E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56118E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  6.6780121311e+06 -3.5057973184e+05 -3.9897317820e+05 -9.2860034320e+06
1.4603317700e+07 -3.1640943297e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   28     OBJ =   0.12535E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  6.4343458517e+06 -4.7855928296e+05 -4.2016188017e+05 -7.1473170682e+06
1.7610937511e+07 -3.4053759897e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   29     OBJ =   0.12535E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

ITER =   30     OBJ =   0.12535E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  6.4343458517e+06 -4.7855928296e+05 -4.2016188017e+05 -7.1473170682e+06
1.7610937511e+07 -3.4053759897e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =   31     OBJ =   0.12535E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =   0.125352E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56143E+02  0.12033E+02  0.81487E+00  0.43900E+00  0.80166E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1    2    3    4   -5    6

TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR  3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1))   LESS THAN DABFUN FOR  3 ITERATIONS

NUMBER OF ITERATIONS =   31

OBJECTIVE FUNCTION WAS EVALUATED           92  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED        92  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED       30  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED   30  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation  263
------------------------------
Parameters for function evaluation 263:
1.2000920001e+01 w_top
5.6097225333e+01 hw
1.2014247342e+01 w_bot
8.1355417613e-01 t_top
4.4104661574e-01 tw
8.0083359682e-01 t_bot

(./SBOdrive /tmp/fileawuWyg /tmp/fileYRFVAD)

Active response data for function evaluation 263:
Active set vector = { 1 1 1 }
1.9032900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  265
------------------------------
Parameters for function evaluation 265:
1.2001210271e+01 w_top
5.6105828360e+01 hw
1.2007433771e+01 w_bot
8.1303610559e-01 t_top
4.4031155862e-01 tw
8.0064968883e-01 t_bot

(./SBOdrive /tmp/filea2aC0F /tmp/fileo7G1S1)

Active response data for function evaluation 265:
Active set vector = { 1 1 1 }
2.0704900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  267
------------------------------
Parameters for function evaluation 267:
1.2003458674e+01 w_top
5.6079810132e+01 hw
1.2013208768e+01 w_bot
8.1379867233e-01 t_top
4.4064834424e-01 tw
8.0072478929e-01 t_bot

(./SBOdrive /tmp/fileCIhSNe /tmp/fileCEOK4D)

Active response data for function evaluation 267:
Active set vector = { 1 1 1 }
2.0185300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  269
------------------------------
Parameters for function evaluation 269:
1.2001927938e+01 w_top
5.6084344913e+01 hw
1.2018379522e+01 w_bot
8.1369979905e-01 t_top
4.4127188439e-01 tw
7.9971619220e-01 t_bot

(./SBOdrive /tmp/filesnfrnN /tmp/fileszM3vb)

Active response data for function evaluation 269:
Active set vector = { 1 1 1 }
1.9034200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  271
------------------------------
Parameters for function evaluation 271:
1.2005636392e+01 w_top
5.6115313920e+01 hw
1.2013615855e+01 w_bot
8.1330118223e-01 t_top
4.4109112844e-01 tw
8.0040167211e-01 t_bot

(./SBOdrive /tmp/fileUvQTev /tmp/file6ND0MW)

Active response data for function evaluation 271:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  273
------------------------------
Parameters for function evaluation 273:
1.2000370010e+01 w_top
5.6110234603e+01 hw
1.2019195969e+01 w_bot
8.1291209361e-01 t_top
4.4004318077e-01 tw
8.0047334566e-01 t_bot

(./SBOdrive /tmp/file8eoJOc /tmp/file6rgyeD)

Active response data for function evaluation 273:
Active set vector = { 1 1 1 }
2.1066500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  275
------------------------------
Parameters for function evaluation 275:
1.2006276405e+01 w_top
5.6072739036e+01 hw
1.2005195498e+01 w_bot
8.1296789777e-01 t_top
4.4174085609e-01 tw
8.0086143110e-01 t_bot

(./SBOdrive /tmp/fileOoKlM3 /tmp/fileeAyyBx)

Active response data for function evaluation 275:
Active set vector = { 1 1 1 }
1.9035700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  277
------------------------------
Parameters for function evaluation 277:
1.2002420937e+01 w_top
5.6091451936e+01 hw
1.2002480259e+01 w_bot
8.1314026623e-01 t_top
4.4073728298e-01 tw
8.0061594506e-01 t_bot

(./SBOdrive /tmp/fileymZxaZ /tmp/fileCG9KQr)

Active response data for function evaluation 277:
Active set vector = { 1 1 1 }
2.0105900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  279
------------------------------
Parameters for function evaluation 279:
1.2001506410e+01 w_top
5.6112201961e+01 hw
1.2015589241e+01 w_bot
8.1336658906e-01 t_top
4.4014831607e-01 tw
8.0092421985e-01 t_bot

(./SBOdrive /tmp/fileUnUbEU /tmp/fileSgeyRq)

Active response data for function evaluation 279:
Active set vector = { 1 1 1 }
2.0941000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  281
------------------------------
Parameters for function evaluation 281:
1.2001632738e+01 w_top
5.6078004682e+01 hw
1.2005848219e+01 w_bot
8.1347385269e-01 t_top
4.4012587688e-01 tw
7.9980751144e-01 t_bot

(./SBOdrive /tmp/file0E9n9Y /tmp/file0iFy5t)

Active response data for function evaluation 281:
Active set vector = { 1 1 1 }
2.0845700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  283
------------------------------
Parameters for function evaluation 283:
1.2002891889e+01 w_top
5.6082185628e+01 hw
1.2012367742e+01 w_bot
8.1325237904e-01 t_top
4.4087452401e-01 tw
8.0096701120e-01 t_bot

(./SBOdrive /tmp/filesXTuE3 /tmp/fileCGXW8B)

Active response data for function evaluation 283:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  285
------------------------------
Parameters for function evaluation 285:
1.2006436298e+01 w_top
5.6089545693e+01 hw
1.2016524287e+01 w_bot
8.1419949999e-01 t_top
4.4116315682e-01 tw
7.9992662168e-01 t_bot

(./SBOdrive /tmp/filec9BQdh /tmp/fileaA7hpO)

Active response data for function evaluation 285:
Active set vector = { 1 1 1 }
1.9034400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  287
------------------------------
Parameters for function evaluation 287:
1.2003804608e+01 w_top
5.6093605781e+01 hw
1.2017677504e+01 w_bot
8.1361662662e-01 t_top
4.4084561858e-01 tw
8.0036871675e-01 t_bot

(./SBOdrive /tmp/fileIJ0XKu /tmp/fileKkABw5)

Active response data for function evaluation 287:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  289
------------------------------
Parameters for function evaluation 289:
1.2004018178e+01 w_top
5.6108566164e+01 hw
1.2003425666e+01 w_bot
8.1395889646e-01 t_top
4.4155420002e-01 tw
8.0050695156e-01 t_bot

(./SBOdrive /tmp/filegFdeGR /tmp/fileiiW98q)

Active response data for function evaluation 289:
Active set vector = { 1 1 1 }
1.9034800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  291
------------------------------
Parameters for function evaluation 291:
1.2000000000e+01 w_top
5.6085747754e+01 hw
1.2001431159e+01 w_bot
8.1421386719e-01 t_top
4.4120965921e-01 tw
7.9968065105e-01 t_bot

(./SBOdrive /tmp/fileQHPd1g /tmp/fileqcLmKW)

Active response data for function evaluation 291:
Active set vector = { 1 1 1 }
1.9034200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio = -3.7936707737e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 11

Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.6076948050e+01
1.2006704597e+01
8.1322509766e-01
4.4040222168e-01
8.0001024090e-01
Current Trust Region Upper Bounds
1.2003515625e+01
5.6103315237e+01
1.2017251472e+01
8.1388427734e-01
4.4133605957e-01
8.0066942058e-01
*********************************************

<<<<< Building global approximation.

DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution

------------------------------
Begin Function Evaluation  293
------------------------------
Parameters for function evaluation 293:
1.2002153696e+01 w_top
5.6096778874e+01 hw
1.2011096949e+01 w_bot
8.1361359928e-01 t_top
4.4066527881e-01 tw
8.0041113366e-01 t_bot

(./SBOdrive /tmp/fileQf5ccT /tmp/fileeQiUJx)

Active response data for function evaluation 293:
Active set vector = { 1 1 1 }
2.0220300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  295
------------------------------
Parameters for function evaluation 295:
1.2000844132e+01 w_top
5.6080109895e+01 hw
1.2013200729e+01 w_bot
8.1371823835e-01 t_top
4.4070429097e-01 tw
8.0011928969e-01 t_bot

(./SBOdrive /tmp/file000RME /tmp/file4cNIQm)

Active response data for function evaluation 295:
Active set vector = { 1 1 1 }
2.0111300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2 ------------------------------
Begin Function Evaluation  297
------------------------------
Parameters for function evaluation 297:
1.2003251160e+01 w_top
5.6084150458e+01 hw
1.2015976239e+01 w_bot
8.1386412435e-01 t_top
4.4123640288e-01 tw
8.0038654071e-01 t_bot

(./SBOdrive /tmp/fileIHt72p /tmp/fileItzuP6)

Active response data for function evaluation 297:
Active set vector = { 1 1 1 }
1.9034300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  299
------------------------------
Parameters for function evaluation 299:
1.2000265996e+01 w_top
5.6092895759e+01 hw
1.2017001020e+01 w_bot
8.1345551207e-01 t_top
4.4061926688e-01 tw
8.0049187437e-01 t_bot

(./SBOdrive /tmp/filek3UmDk /tmp/filewxsIY4)

Active response data for function evaluation 299:
Active set vector = { 1 1 1 }
2.0266400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  301
------------------------------
Parameters for function evaluation 301:
1.2001947441e+01 w_top
5.6081315252e+01 hw
1.2008649901e+01 w_bot
8.1351429127e-01 t_top
4.4108530161e-01 tw
8.0056996865e-01 t_bot

(./SBOdrive /tmp/filemCymef /tmp/file6vuuhY)

Active response data for function evaluation 301:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  303
------------------------------
Parameters for function evaluation 303:
1.2003475774e+01 w_top
5.6096042502e+01 hw
1.2008455917e+01 w_bot
8.1343328348e-01 t_top
4.4096657803e-01 tw
8.0008244353e-01 t_bot

(./SBOdrive /tmp/fileyKjEce /tmp/fileaGE3M0)

Active response data for function evaluation 303:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  305
------------------------------
Parameters for function evaluation 305:
1.2000930707e+01 w_top
5.6085937908e+01 hw
1.2015334987e+01 w_bot
8.1380588054e-01 t_top
4.4089380790e-01 tw
8.0021503793e-01 t_bot

(./SBOdrive /tmp/file8OeD6h /tmp/fileOMD2v3)

Active response data for function evaluation 305:
Active set vector = { 1 1 1 }
1.9032900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  307
------------------------------
Parameters for function evaluation 307:
1.2002482850e+01 w_top
5.6099258022e+01 hw
1.2014547133e+01 w_bot
8.1374370904e-01 t_top
4.4051430566e-01 tw
8.0014792965e-01 t_bot

(./SBOdrive /tmp/fileAf9Mpq /tmp/fileKyvBlf)

Active response data for function evaluation 307:
Active set vector = { 1 1 1 }
2.0424000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  309
------------------------------
Parameters for function evaluation 309:

1.2001635481e+01 w_top
5.6097909887e+01 hw
1.2006877365e+01 w_bot
8.1340542223e-01 t_top
4.4097677248e-01 tw
8.0001438087e-01 t_bot

(./SBOdrive /tmp/fileodHbdI /tmp/files9KR0v)

Active response data for function evaluation 309:
Active set vector = { 1 1 1 }
1.9032500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  311
------------------------------
Parameters for function evaluation 311:
1.2000210535e+01 w_top
5.6094655930e+01 hw
1.2016126153e+01 w_bot
8.1349364931e-01 t_top
4.4101217123e-01 tw
8.0031074026e-01 t_bot

(./SBOdrive /tmp/file2pjnJZ /tmp/fileacrYVQ)

Active response data for function evaluation 311:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  313
------------------------------
Parameters for function evaluation 313:
1.2001820747e+01 w_top
5.6090046136e+01 hw
1.2007412454e+01 w_bot
8.1365112051e-01 t_top
4.4055053975e-01 tw
8.0023028766e-01 t_bot

(./SBOdrive /tmp/fileQJc9zq /tmp/fileMV62Dg)

Active response data for function evaluation 313:
Active set vector = { 1 1 1 }
2.0343400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  315
------------------------------
Parameters for function evaluation 315:
1.2001403875e+01 w_top
5.6089165240e+01 hw
1.2015475852e+01 w_bot
8.1372657715e-01 t_top
4.4040972153e-01 tw
8.0004348096e-01 t_bot

(./SBOdrive /tmp/fileOqrPER /tmp/file0OpucL)

Active response data for function evaluation 315:
Active set vector = { 1 1 1 }
2.0523000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  317
------------------------------
Parameters for function evaluation 317:
1.2002778041e+01 w_top
5.6100996443e+01 hw
1.2009182623e+01 w_bot
8.1327323693e-01 t_top
4.4129675027e-01 tw
8.0033456425e-01 t_bot

(./SBOdrive /tmp/file6LZNTr /tmp/fileqmG1gk)

Active response data for function evaluation 317:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  319
------------------------------
Parameters for function evaluation 319:
1.2002613191e+01 w_top
5.6085110376e+01 hw
1.2008039575e+01 w_bot
8.1346875804e-01 t_top
4.4074454807e-01 tw
8.0016155207e-01 t_bot

(./SBOdrive /tmp/fileklgjo2 /tmp/fileYlhVoY)

Active response data for function evaluation 319:
Active set vector = { 1 1 1 }
2.0075400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed

<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed

<<<<< Evaluating approximation at trust region center.

<<<<< Starting approximate optimization cycle.
1

* * * * * * * * * * * * * * * * * * * * * * * * * * *
*                                                   *
*                    C O N M I N                    *
*                                                   *
*                FORTRAN PROGRAM FOR                *
*                                                   *
*         CONSTRAINED FUNCTION MINIMIZATION         *
*                                                   *
* * * * * * * * * * * * * * * * * * * * * * * * * * *

CONSTRAINED FUNCTION MINIMIZATION

CONTROL PARAMETERS

IPRINT  NDV    ITMAX    NCON    NSIDE  ICNDIR   NSCAL   NFDG
2       6      50       2       1       7       0       1

LINOBJ  ITRM     N1      N2      N3      N4      N5
0       3       8      14       9       9      18

CT              CTMIN           CTL             CTLMIN
-0.10000E+00     0.10000E-02    -0.10000E-01     0.10000E-02

THETA           PHI             DELFUN          DABFUN
0.10000E+01     0.50000E+01     0.10000E-03     0.10000E-03

FDCH            FDCHM           ALPHAX          ABOBJ1
0.10000E-04     0.10000E-04     0.10000E+00     0.10000E+00

LOWER BOUNDS ON DECISION VARIABLES (VLB)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81323E+00  0.44040E+00  0.80001E+00

UPPER BOUNDS ON DECISION VARIABLES (VUB)
1)    0.12004E+02  0.56103E+02  0.12017E+02  0.81388E+00  0.44134E+00  0.80067E+00

ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION

OBJ =   0.188945E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81355E+00  0.44087E+00  0.80034E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[ -2.0966195679e+06  3.8532102852e+06  3.7413144638e+06 -1.2726419900e+08
-1.9867839393e+08 -5.4596188786e+07 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    1     OBJ =  -0.60250E+05

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81385E+00  0.44134E+00  0.80047E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.0494451747e+07  1.1629886528e+07  1.0676594526e+07 -2.7036649665e+08
-5.2156438558e+08 -2.6029931795e+08 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    2     OBJ =  -0.12163E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56090E+02  0.12012E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  7.6815876967e+07  1.2332803161e+07  8.1895213922e+06 -3.2984580011e+08
-5.8726971207e+08 -2.4061790074e+08 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    3     OBJ =  -0.27202E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56082E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.1280860714e+08  1.2219433938e+07  1.4088058756e+07 -5.6650879914e+08
-6.6603083974e+08 -1.6721060365e+08 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    4     OBJ =  -0.32745E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.2271547238e+08  1.0613201556e+07  1.6393696624e+07 -6.8293195440e+08
-6.7880174219e+08 -1.6787793464e+08 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    5     OBJ =  -0.32745E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

ITER =    6     OBJ =  -0.32745E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------

>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:

Active set vector = { 2 2 2 }
[  1.2271547238e+08  1.0613201556e+07  1.6393696624e+07 -6.8293195440e+08
-6.7880174219e+08 -1.6787793464e+08 ] obj_fn gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con1 gradient
[  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00  0.0000000000e+00
0.0000000000e+00  0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT    1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT    2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET

ITER =    7     OBJ =  -0.32745E+06     NO CHANGE IN OBJ

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00
1

FINAL OPTIMIZATION INFORMATION

OBJ =  -0.327447E+06

DECISION VARIABLES (X-VECTOR)
1)    0.12000E+02  0.56077E+02  0.12007E+02  0.81388E+00  0.44134E+00  0.80067E+00

CONSTRAINT VALUES (G-VECTOR)
1)    0.00000E+00  0.00000E+00

THERE ARE    2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1    2

THERE ARE    0 VIOLATED CONSTRAINTS

THERE ARE    6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1   -2   -3    4    5    6

TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR  3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1))   LESS THAN DABFUN FOR  3 ITERATIONS

NUMBER OF ITERATIONS =    7

OBJECTIVE FUNCTION WAS EVALUATED           14  TIMES

CONSTRAINT FUNCTIONS WERE EVALUATED        14  TIMES

GRADIENT OF OBJECTIVE WAS CALCULATED        6  TIMES

GRADIENTS OF CONSTRAINTS WERE CALCULATED    6  TIMES

<<<<< Approximate optimization cycle completed.

<<<<< Evaluating approximate solution with actual model.

------------------------------
Begin Function Evaluation  321
------------------------------
Parameters for function evaluation 321:
1.2000156231e+01 w_top
5.6089959355e+01 hw
1.2012325375e+01 w_bot
8.1341297345e-01 t_top
4.4078547744e-01 tw
8.0047868271e-01 t_bot

(./SBOdrive /tmp/fileGGZDdJ /tmp/file2PPxCE)

Active response data for function evaluation 321:
Active set vector = { 1 1 1 }
2.0040600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  323
------------------------------
Parameters for function evaluation 323:
1.2000363466e+01 w_top
5.6087724241e+01 hw
1.2014131339e+01 w_bot
8.1357127876e-01 t_top
4.4093474775e-01 tw
8.0032033932e-01 t_bot

(./SBOdrive /tmp/fileCKm6Zx /tmp/fileGactFu)

Active response data for function evaluation 323:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  325
------------------------------
Parameters for function evaluation 325:
1.2001163695e+01 w_top
5.6083931664e+01 hw
1.2013676145e+01 w_bot
8.1341593606e-01 t_top
4.4095370718e-01 tw
8.0025541743e-01 t_bot

(./SBOdrive /tmp/fileAp4rEm /tmp/fileOQ36nk)

Active response data for function evaluation 325:
Active set vector = { 1 1 1 }
1.9032800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  327
------------------------------
Parameters for function evaluation 327:
1.2001324660e+01 w_top
5.6085609751e+01 hw
1.2013998163e+01 w_bot
8.1352011340e-01 t_top
4.4106851375e-01 tw
8.0021648679e-01 t_bot

(./SBOdrive /tmp/fileqibW2m /tmp/fileoFCGko)

Active response data for function evaluation 327:
Active set vector = { 1 1 1 }
1.9033300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  329
------------------------------
Parameters for function evaluation 329:
1.2001413444e+01 w_top
5.6095066558e+01 hw
1.2011473958e+01 w_bot
8.1365101618e-01 t_top
4.4098442572e-01 tw
8.0023824502e-01 t_bot

(./SBOdrive /tmp/file4YuU6m /tmp/fileoR0r5m)

Active response data for function evaluation 329:
Active set vector = { 1 1 1 }
1.9032800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  331
------------------------------
Parameters for function evaluation 331:
1.2000787167e+01 w_top
5.6091272482e+01 hw
1.2010362966e+01 w_bot
8.1368530117e-01 t_top
4.4088419036e-01 tw
8.0020954385e-01 t_bot

(./SBOdrive /tmp/file4KfKsw /tmp/filemitrYz)

Active response data for function evaluation 331:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  333
------------------------------
Parameters for function evaluation 333:
1.2000980300e+01 w_top
5.6094507566e+01 hw
1.2014559241e+01 w_bot
8.1370001433e-01 t_top
4.4109765058e-01 tw
8.0023120971e-01 t_bot

(./SBOdrive /tmp/filem6VYTF /tmp/fileGsXyhI)

Active response data for function evaluation 333:
Active set vector = { 1 1 1 }
1.9033300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  335
------------------------------
Parameters for function evaluation 335:
1.2001471240e+01 w_top
5.6085183322e+01 hw
1.2014334691e+01 w_bot
8.1346097257e-01 t_top
4.4083591913e-01 tw
8.0027420670e-01 t_bot

(./SBOdrive /tmp/filewlJDqY /tmp/fileUXbsc4)

Active response data for function evaluation 335:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  337
------------------------------
Parameters for function evaluation 337:
1.2000528901e+01 w_top
5.6089463835e+01 hw
1.2012068068e+01 w_bot
8.1347438047e-01 t_top
4.4072021137e-01 tw
8.0026552994e-01 t_bot

(./SBOdrive /tmp/filei0XYSg /tmp/file8wekwl)

Active response data for function evaluation 337:
Active set vector = { 1 1 1 }
2.0122700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  339
------------------------------
Parameters for function evaluation 339:
1.2000880636e+01 w_top
5.6090239452e+01 hw
1.2012527289e+01 w_bot
8.1355100995e-01 t_top
4.4075301302e-01 tw
8.0041364331e-01 t_bot

(./SBOdrive /tmp/fileUxDAQI /tmp/file4cxA0Q)

Active response data for function evaluation 339:
Active set vector = { 1 1 1 }
2.0084000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  341
------------------------------
Parameters for function evaluation 341:
1.2000625203e+01 w_top
5.6095336986e+01 hw
1.2012802966e+01 w_bot
8.1354262559e-01 t_top
4.4073872874e-01 tw
8.0018286887e-01 t_bot

(./SBOdrive /tmp/fileQTZpra /tmp/filesjW2ih)

Active response data for function evaluation 341:
Active set vector = { 1 1 1 }
2.0119500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  343
------------------------------
Parameters for function evaluation 343:
1.2001673810e+01 w_top
5.6084026752e+01 hw
1.2009866637e+01 w_bot
8.1345318277e-01 t_top
4.4065831695e-01 tw
8.0038955375e-01 t_bot

(./SBOdrive /tmp/filea4PClL /tmp/fileaYl7LV)

Active response data for function evaluation 343:
Active set vector = { 1 1 1 }
2.0183600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  345
------------------------------
Parameters for function evaluation 345:
1.2000841041e+01 w_top
5.6090754721e+01 hw
1.2010493986e+01 w_bot
8.1362875983e-01 t_top
4.4088753376e-01 tw
8.0050444884e-01 t_bot

(./SBOdrive /tmp/filePPM3A0 /tmp/fileYfnxB2)

Active response data for function evaluation 345:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  347
------------------------------
Parameters for function evaluation 347:
1.2001217607e+01 w_top
5.6088267432e+01 hw
1.2009606862e+01 w_bot
8.1349336557e-01 t_top
4.4083141661e-01 tw
8.0044035551e-01 t_bot

(./SBOdrive /tmp/filejRuf13 /tmp/filea6BqT4)

Active response data for function evaluation 347:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

------------------------------
Begin Function Evaluation  349
------------------------------
Parameters for function evaluation 349:
1.2000011138e+01 w_top
5.6090137954e+01 hw
1.2011943368e+01 w_bot
8.1371948242e-01 t_top
4.4081897525e-01 tw
8.0050462566e-01 t_bot

(./SBOdrive /tmp/fileblrYUe /tmp/fileeKW6lh)

Active response data for function evaluation 349:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2

<<<<< Trust Region Ratio Numerator = 0.0000000000e+00:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
Optimization Complete - Soft Convergence Tolerance Reached
Progress Between 5 Successive Iterations <= Convergence Tolerance
******************************************
Surrogate-Based Optimization (SBO) Results
******************************************
SBO Iterations = 12
Surrogate Model Evaluations = 2491 (2491 new, 0 duplicate)
Truth Model Evaluations     = 349 (349 new, 0 duplicate)

SBO Final Design Variables
w_top  =  1.2000000000e+01
hw  =  5.6090131643e+01
w_bot  =  1.2011978034e+01
t_top  =  8.1355468750e-01
tw  =  4.4086914063e-01
t_bot  =  8.0033983074e-01

SBO Final Truth Response Values
Objective Function   =  1.9032400000e+05
Ineq Constraint 1    =  0.0000000000e+00
Ineq Constraint 2    =  0.0000000000e+00
*******************************************
Updated: 07/23/2013
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