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+ | 
