Prefabricated Steel Bridge Systems: Final Report
9. Appendix B - SBO Optimization
File: sboResult.txt
Running MPI executable in serial mode.
Writing new restart file dakota.rst
Constructing Surrogate-Based Optimization Strategy...
methodName = dace
gradientType = none
hessianType = none
Adjusting the number of symbols and samples....
num_variables = 6
OLD num_samples = 10 OLD num_symbols = 0
NEW num_samples = 10 NEW num_symbols = 10
methodName = conmin_mfd
gradientType = numerical
Numerical gradients using forward differences
to be calculated by the dakota finite difference routine.
hessianType = none
Running Surrogate-Based Optimization Strategy...
*********************************************
Begin SBO Iteration Number 1
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.4500000000e+01
1.2000000000e+01
7.8250000000e-01
4.3750000000e-01
7.6250000000e-01
Current Trust Region Upper Bounds
1.2400000000e+01
5.7500000000e+01
1.2600000000e+01
8.5750000000e-01
5.0312500000e-01
8.3750000000e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed (user-specified) = 12345
------------------------------
Begin Function Evaluation 1
------------------------------
Parameters for function evaluation 1:
1.2117159189e+01 w_top
5.5488687348e+01 hw
1.2564033020e+01 w_bot
8.3255450091e-01 t_top
4.8243696557e-01 tw
8.1595395996e-01 t_bot
(./SBOdrive /tmp/filew4TsNe /tmp/fileMPOKqj)
Active response data for function evaluation 1:
Active set vector = { 1 1 1 }
1.8247300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 3
------------------------------
Parameters for function evaluation 3:
1.2235235582e+01 w_top
5.7290571722e+01 hw
1.2440331736e+01 w_bot
8.0566385236e-01 t_top
4.9932217365e-01 tw
7.7090789393e-01 t_bot
(./SBOdrive /tmp/fileowshDv /tmp/fileemwhBB)
Active response data for function evaluation 3:
Active set vector = { 1 1 1 }
1.8324400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 5
------------------------------
Parameters for function evaluation 5:
1.2158288909e+01 w_top
5.4983358276e+01 hw
1.2199564214e+01 w_bot
7.8419956377e-01 t_top
4.6459523983e-01 tw
8.3356353936e-01 t_bot
(./SBOdrive /tmp/fileEwACtT /tmp/fileIrxJaY)
Active response data for function evaluation 5:
Active set vector = { 1 1 1 }
1.9128400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 7
------------------------------
Parameters for function evaluation 7:
1.2149269407e+01 w_top
5.5367825890e+01 hw
1.2489510531e+01 w_bot
8.0707323642e-01 t_top
4.7195392543e-01 tw
8.2274379554e-01 t_bot
(./SBOdrive /tmp/fileQKkdCe /tmp/fileWUYark)
Active response data for function evaluation 7:
Active set vector = { 1 1 1 }
1.8174000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 9
------------------------------
Parameters for function evaluation 9:
1.2318782285e+01 w_top
5.5040165557e+01 hw
1.2185566372e+01 w_bot
7.9636602102e-01 t_top
4.8183702673e-01 tw
8.1276101193e-01 t_bot
(./SBOdrive /tmp/file49nrbL /tmp/fileimN9pU)
Active response data for function evaluation 9:
Active set vector = { 1 1 1 }
1.9204200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 11
------------------------------
Parameters for function evaluation 11:
1.2217029216e+01 w_top
5.7492244526e+01 hw
1.2585015996e+01 w_bot
8.1563811184e-01 t_top
5.0250611281e-01 tw
8.0259064714e-01 t_bot
(./SBOdrive /tmp/fileipW1Eh /tmp/file0i24Ip)
Active response data for function evaluation 11:
Active set vector = { 1 1 1 }
1.8398000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 13
------------------------------
Parameters for function evaluation 13:
1.2012381666e+01 w_top
5.6335168622e+01 hw
1.2088576489e+01 w_bot
8.5247227044e-01 t_top
4.8527365139e-01 tw
7.7588320619e-01 t_bot
(./SBOdrive /tmp/fileMYqvpX /tmp/fileCsnf28)
Active response data for function evaluation 13:
Active set vector = { 1 1 1 }
1.8234900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 15
------------------------------
Parameters for function evaluation 15:
1.2207766701e+01 w_top
5.6769354027e+01 hw
1.2036007709e+01 w_bot
8.4095082305e-01 t_top
4.6701682214e-01 tw
7.6427759921e-01 t_bot
(./SBOdrive /tmp/file83pxoD /tmp/file8oh4VN)
Active response data for function evaluation 15:
Active set vector = { 1 1 1 }
1.8154800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 17
------------------------------
Parameters for function evaluation 17:
1.2177131376e+01 w_top
5.7244538013e+01 hw
1.2504475027e+01 w_bot
8.2911487200e-01 t_top
4.3825018196e-01 tw
7.6881265386e-01 t_bot
(./SBOdrive /tmp/filearTJys /tmp/fileGoO4vG)
Active response data for function evaluation 17:
Active set vector = { 1 1 1 }
2.6337200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 19
------------------------------
Parameters for function evaluation 19:
1.2080722945e+01 w_top
5.4735302638e+01 hw
1.2241685404e+01 w_bot
8.4862179614e-01 t_top
4.9528670155e-01 tw
8.0339776532e-01 t_bot
(./SBOdrive /tmp/file41Bbrh /tmp/file8m8hau)
Active response data for function evaluation 19:
Active set vector = { 1 1 1 }
1.9288600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 21
------------------------------
Parameters for function evaluation 21:
1.2394968791e+01 w_top
5.7148603251e+01 hw
1.2257527990e+01 w_bot
8.4980067170e-01 t_top
4.6203799334e-01 tw
8.1732913435e-01 t_bot
(./SBOdrive /tmp/fileO3ywCf /tmp/fileIrNVKv)
Active response data for function evaluation 21:
Active set vector = { 1 1 1 }
1.8234800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 23
------------------------------
Parameters for function evaluation 23:
1.2058587519e+01 w_top
5.4550885567e+01 hw
1.2350956705e+01 w_bot
8.4216347972e-01 t_top
4.5707192557e-01 tw
7.8944054488e-01 t_bot
(./SBOdrive /tmp/fileqz7fhd /tmp/filecrnrcs)
Active response data for function evaluation 23:
Active set vector = { 1 1 1 }
1.9160600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 25
------------------------------
Parameters for function evaluation 25:
1.2341043738e+01 w_top
5.6292452370e+01 hw
1.2076340681e+01 w_bot
8.0342445721e-01 t_top
4.9698531571e-01 tw
8.2577254365e-01 t_bot
(./SBOdrive /tmp/file2mXRfk /tmp/fileUHaFAC)
Active response data for function evaluation 25:
Active set vector = { 1 1 1 }
1.8308900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 27
------------------------------
Parameters for function evaluation 27:
1.2265441938e+01 w_top
5.5950597201e+01 hw
1.2324647389e+01 w_bot
8.2178254376e-01 t_top
4.4976949028e-01 tw
8.0986041635e-01 t_bot
(./SBOdrive /tmp/file6sfpHq /tmp/file699ROH)
Active response data for function evaluation 27:
Active set vector = { 1 1 1 }
1.8093400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 29
------------------------------
Parameters for function evaluation 29:
1.2000000000e+01 w_top
5.6000000000e+01 hw
1.2000000000e+01 w_bot
8.2000000000e-01 t_top
4.5000000000e-01 tw
8.0000000000e-01 t_bot
(./SBOdrive /tmp/file0d9NKG /tmp/fileeIl3z1)
Active response data for function evaluation 29:
Active set vector = { 1 1 1 }
1.9071200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
Beginning Approximate Fn Evaluations...
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.54500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.76250E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12400E+02 0.57500E+02 0.12600E+02 0.85750E+00 0.50313E+00 0.83750E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.191738E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56000E+02 0.12000E+02 0.82000E+00 0.45000E+00 0.80000E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -8.1506262275e+05 -1.5431636836e+05 4.5590698934e+05 4.0864851431e+06
4.0673292540e+06 1.3222368805e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = 0.65542E+05
DECISION VARIABLES (X-VECTOR)
1) 0.12003E+02 0.56000E+02 0.12000E+02 0.80744E+00 0.43750E+00 0.79594E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -4.8868649927e+05 -1.6411020010e+05 4.9607038802e+05 5.9915220431e+06
5.3730978034e+06 -2.7342310754e+05 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = -0.11430E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12005E+02 0.56001E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.79707E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -3.5764397378e+05 -1.6798092458e+05 5.7308740856e+05 8.2986975242e+06
7.1175927975e+06 -7.1210133715e+05 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = -0.13097E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12022E+02 0.56010E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83268E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.5338218973e+05 -2.3944274410e+05 5.9945839362e+05 8.8504450603e+06
1.0192109557e+07 4.3694704024e+05 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = -0.14665E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12051E+02 0.56019E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.81593E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -3.2157302977e+05 -2.0098298245e+05 5.5082706100e+05 8.3914971534e+06
8.2166242540e+06 -6.6872880753e+05 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = -0.18841E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.56058E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.1280507683e+05 -2.3831164594e+05 5.2086203825e+05 8.4459038253e+06
9.3558109306e+06 -8.0210042966e+05 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 6 OBJ = -0.20559E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12186E+02 0.56138E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.6402566082e+05 -2.3202837718e+05 4.6912681700e+05 8.0693202902e+06
8.4228222324e+06 -1.9934002254e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 7 OBJ = -0.22285E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12111E+02 0.56203E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.1229803878e+05 -2.4667942123e+05 5.7050058600e+05 8.4736363530e+06
9.6080779316e+06 -1.0554521494e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 8 OBJ = -0.24140E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12185E+02 0.56289E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.8014978393e+05 -2.4036441549e+05 5.1810375359e+05 8.0853037156e+06
8.6497957769e+06 -2.2910084508e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 9 OBJ = -0.26003E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12107E+02 0.56355E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00 ------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.1932710864e+05 -2.5563190833e+05 6.2383716719e+05 8.5089512877e+06
9.8897700387e+06 -1.3045874443e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 10 OBJ = -0.27996E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12183E+02 0.56444E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.9114876560e+05 -2.4910104429e+05 5.6961309256e+05 8.1064536533e+06
8.8968696317e+06 -2.5858377012e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 11 OBJ = -0.29999E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12102E+02 0.56513E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.2670926916e+05 -2.6491893679e+05 6.7916959903e+05 8.5456533036e+06
1.0182129466e+07 -1.5627707799e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 12 OBJ = -0.32140E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12181E+02 0.56605E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.0242611168e+05 -2.5816271803e+05 6.2304279403e+05 8.1284880204e+06
9.1533397138e+06 -2.8912572125e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 13 OBJ = -0.34295E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12097E+02 0.56677E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.3447219648e+05 -2.7455260972e+05 7.3657053944e+05 8.5838036903e+06
1.0485565355e+07 -1.8302863581e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 14 OBJ = -0.36594E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12179E+02 0.56772E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.1399719152e+05 -2.6756055888e+05 6.7845839075e+05 8.1514327206e+06
9.4195184348e+06 -3.2076462750e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 15 OBJ = -0.38911E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12092E+02 0.56846E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.4260759529e+05 -2.8454421149e+05 7.9610669773e+05 8.6234341688e+06
1.0800406043e+07 -2.1074978697e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 16 OBJ = -0.41380E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12177E+02 0.56946E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.2589525517e+05 -2.7730591627e+05 7.3592622192e+05 8.1753010955e+06
9.6956972131e+06 -3.5354427847e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 17 OBJ = -0.43871E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12087E+02 0.57022E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.5111252545e+05 -2.9490572476e+05 8.5784908907e+05 8.6645828062e+06
1.1127009319e+07 -2.3947745256e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 18 OBJ = -0.46524E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12175E+02 0.57125E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.3814937487e+05 -2.8741072485e+05 7.9551622581e+05 8.2001115643e+06
9.9821916711e+06 -3.8750901589e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 19 OBJ = -0.49202E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12081E+02 0.57205E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.5998773024e+05 -3.0564977571e+05 9.2187265889e+05 8.7072925697e+06
1.1465758188e+07 -2.6924942412e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 20 OBJ = -0.52051E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12172E+02 0.57312E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.5078590761e+05 -2.9788748515e+05 8.5730178139e+05 8.2258864729e+06
1.0279338791e+07 -4.2270409859e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 21 OBJ = -0.54929E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12076E+02 0.57394E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.6923671718e+05 -3.1678960831e+05 9.8825610311e+05 8.7516105678e+06
1.1817058636e+07 -3.0010455292e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 22 OBJ = -0.57982E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12166E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.3660701130e+05 -3.0909499833e+05 9.2423624214e+05 8.2741070647e+06
1.0640367531e+07 -4.5233573626e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 23 OBJ = -0.58828E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 4.3672780803e+03 -3.1668017246e+05 9.7931497675e+05 8.5472721002e+06
1.1387434639e+07 -3.7898415292e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 24 OBJ = -0.58828E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
ITER = 25 OBJ = -0.58828E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 4.3672781295e+03 -3.1668017246e+05 9.7931497680e+05 8.5472721010e+06
1.1387434640e+07 -3.7898415285e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 26 OBJ = -0.58828E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = -0.588284E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12115E+02 0.57500E+02 0.12000E+02 0.78250E+00 0.43750E+00 0.83750E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 5 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
2 -3 -4 -5 6
TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR 3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1)) LESS THAN DABFUN FOR 3 ITERATIONS
NUMBER OF ITERATIONS = 26
OBJECTIVE FUNCTION WAS EVALUATED 82 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 82 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 25 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 25 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 31
------------------------------
Parameters for function evaluation 31:
1.2096137303e+01 w_top
5.6055345201e+01 hw
1.2110593474e+01 w_bot
8.2162870698e-01 t_top
4.7022998611e-01 tw
8.0218611341e-01 t_bot
(./SBOdrive /tmp/fileo0LvK6 /tmp/fileqAclYr)
Active response data for function evaluation 31:
Active set vector = { 1 1 1 }
1.8157700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 33
------------------------------
Parameters for function evaluation 33:
1.2059282820e+01 w_top
5.5856834452e+01 hw
1.2132682887e+01 w_bot
8.1688861310e-01 t_top
4.4262870075e-01 tw
8.1228296740e-01 t_bot
(./SBOdrive /tmp/filegyfkgy /tmp/fileAVk4TW)
Active response data for function evaluation 33:
Active set vector = { 1 1 1 }
1.9052700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 35
------------------------------
Parameters for function evaluation 35:
1.2056968408e+01 w_top
5.6641773338e+01 hw
1.2188860011e+01 w_bot
8.1173292685e-01 t_top
4.4429615686e-01 tw
7.9740162996e-01 t_bot
(./SBOdrive /tmp/file0hxq6a /tmp/fileOgGkPA)
Active response data for function evaluation 35:
Active set vector = { 1 1 1 }
1.8048200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 37
------------------------------
Parameters for function evaluation 37:
1.2138210281e+01 w_top
5.5986258346e+01 hw
1.2088908961e+01 w_bot
8.3139012961e-01 t_top
4.5913283578e-01 tw
8.0314437570e-01 t_bot
(./SBOdrive /tmp/fileoOtgnL /tmp/fileKWoxY9)
Active response data for function evaluation 37:
Active set vector = { 1 1 1 }
1.9122500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2------------------------------
Begin Function Evaluation 39
------------------------------
Parameters for function evaluation 39:
1.2001582128e+01 w_top
5.6583650968e+01 hw
1.2160652176e+01 w_bot
8.1952004107e-01 t_top
4.6122467311e-01 tw
8.0129802158e-01 t_bot
(./SBOdrive /tmp/fileed9WSu /tmp/fileYN4hUW)
Active response data for function evaluation 39:
Active set vector = { 1 1 1 }
1.8130300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 41
------------------------------
Parameters for function evaluation 41:
1.2156083526e+01 w_top
5.5507506688e+01 hw
1.2043349973e+01 w_bot
8.0428880310e-01 t_top
4.7224226612e-01 tw
7.8859793033e-01 t_bot
(./SBOdrive /tmp/fileqT9a1d /tmp/filey6M7NE)
Active response data for function evaluation 41:
Active set vector = { 1 1 1 }
1.9154300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 43
------------------------------
Parameters for function evaluation 43:
1.2135419003e+01 w_top
5.5623311364e+01 hw
1.2028687107e+01 w_bot
8.0271039244e-01 t_top
4.5053979522e-01 tw
8.1054624441e-01 t_bot
(./SBOdrive /tmp/fileIM9ze6 /tmp/file0AlqrA)
Active response data for function evaluation 43:
Active set vector = { 1 1 1 }
1.9077000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 45
------------------------------
Parameters for function evaluation 45:
1.2046001555e+01 w_top
5.6145442372e+01 hw
1.2105119803e+01 w_bot
8.1792492913e-01 t_top
4.4814899295e-01 tw
7.9155356487e-01 t_bot
(./SBOdrive /tmp/fileseyGmY /tmp/fileA5TZir)
Active response data for function evaluation 45:
Active set vector = { 1 1 1 }
1.9062300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 47
------------------------------
Parameters for function evaluation 47:
1.2119694061e+01 w_top
5.6469539959e+01 hw
1.2221503307e+01 w_bot
8.0912172480e-01 t_top
4.6757421146e-01 tw
7.8995401938e-01 t_bot
(./SBOdrive /tmp/filekuMnAZ /tmp/fileg3Ti6v)
Active response data for function evaluation 47:
Active set vector = { 1 1 1 }
1.8145600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 49
------------------------------
Parameters for function evaluation 49:
1.2190795046e+01 w_top
5.5925847224e+01 hw
1.2073946626e+01 w_bot
8.1452440530e-01 t_top
4.6578027127e-01 tw
7.8721157179e-01 t_bot
(./SBOdrive /tmp/filegUSQB0 /tmp/filesdQQRv)
Active response data for function evaluation 49:
Active set vector = { 1 1 1 }
1.9133400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 51
------------------------------
Parameters for function evaluation 51:
1.2012263525e+01 w_top
5.5440460647e+01 hw
1.2200912312e+01 w_bot
8.3436733633e-01 t_top
4.4997534909e-01 tw
7.9410891316e-01 t_bot
(./SBOdrive /tmp/fileKNd2Va /tmp/fileeqm5BJ)
Active response data for function evaluation 51:
Active set vector = { 1 1 1 }
1.9100900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 53
------------------------------
Parameters for function evaluation 53:
1.2088666863e+01 w_top
5.6733366822e+01 hw
1.2205279197e+01 w_bot
8.2573218705e-01 t_top
4.5289839000e-01 tw
8.1161109058e-01 t_bot
(./SBOdrive /tmp/fileOfZuWk /tmp/fileMpjfmS)
Active response data for function evaluation 53:
Active set vector = { 1 1 1 }
1.8122600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 55
------------------------------
Parameters for function evaluation 55:
1.2169349170e+01 w_top
5.6415012777e+01 hw
1.2015990312e+01 w_bot
8.2076112920e-01 t_top
4.5158833489e-01 tw
8.1632784487e-01 t_bot
(./SBOdrive /tmp/fileC1qk3D /tmp/filecnxJ0e)
Active response data for function evaluation 55:
Active set vector = { 1 1 1 }
1.8097300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 57
------------------------------
Parameters for function evaluation 57:
1.2103611196e+01 w_top
5.5291060214e+01 hw
1.2004440195e+01 w_bot
8.0584007845e-01 t_top
4.6016036955e-01 tw
7.8555774366e-01 t_bot
(./SBOdrive /tmp/file2UUFA1 /tmp/file6jJUhB)
Active response data for function evaluation 57:
Active set vector = { 1 1 1 }
1.9118100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 59
------------------------------
Parameters for function evaluation 59:
1.2000000000e+01 w_top
5.6000447715e+01 hw
1.2009776838e+01 w_bot
8.0125000000e-01 t_top
4.3750000000e-01 tw
8.1630106742e-01 t_bot
(./SBOdrive /tmp/file62PKrn /tmp/filey0RQZY)
Active response data for function evaluation 59:
Active set vector = { 1 1 1 }
2.3939900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio = -6.4854078971e-01:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 3
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5625000000e+01
1.2000000000e+01
8.1062500000e-01
4.3750000000e-01
7.9062500000e-01
Current Trust Region Upper Bounds
1.2100000000e+01
5.6375000000e+01
1.2150000000e+01
8.2937500000e-01
4.6328125000e-01
8.0937500000e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution
------------------------------
Begin Function Evaluation 61
------------------------------
Parameters for function evaluation 61:
1.2069106192e+01 w_top
5.6302808025e+01 hw
1.2106452306e+01 w_bot
8.1412865307e-01 t_top
4.5301225992e-01 tw
7.9603493561e-01 t_bot
(./SBOdrive /tmp/fileqtCoiS /tmp/filee2Vsmx)
Active response data for function evaluation 61:
Active set vector = { 1 1 1 }
1.9072900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 63
------------------------------
Parameters for function evaluation 63:
1.2027686035e+01 w_top
5.6193986106e+01 hw
1.2058263515e+01 w_bot
8.2004229152e-01 t_top
4.5864974306e-01 tw
8.0500713667e-01 t_bot
(./SBOdrive /tmp/filegoIyww /tmp/fileMt7Qpa)
Active response data for function evaluation 63:
Active set vector = { 1 1 1 }
1.8103100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 65
------------------------------
Parameters for function evaluation 65:
1.2039985084e+01 w_top
5.6273849250e+01 hw
1.2028049642e+01 w_bot
8.1269789523e-01 t_top
4.5336155868e-01 tw
7.9190066304e-01 t_bot
(./SBOdrive /tmp/fileCXYvk8 /tmp/fileCyi9hN)
Active response data for function evaluation 65:
Active set vector = { 1 1 1 }
1.9071000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 67
------------------------------
Parameters for function evaluation 67:
1.2050982093e+01 w_top
5.5838349857e+01 hw
1.2120910748e+01 w_bot
8.1508324445e-01 t_top
4.4107435051e-01 tw
7.9999164716e-01 t_bot
(./SBOdrive /tmp/fileMiNMCV /tmp/fileI9fO8D)
Active response data for function evaluation 67:
Active set vector = { 1 1 1 }
1.9045200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 69
------------------------------
Parameters for function evaluation 69:
1.2045333431e+01 w_top
5.6027218254e+01 hw
1.2128542484e+01 w_bot
8.2786760612e-01 t_top
4.3790653535e-01 tw
8.0131847739e-01 t_bot
(./SBOdrive /tmp/fileClpV0I /tmp/fileqkCXkq)
Active response data for function evaluation 69:
Active set vector = { 1 1 1 }
2.3610500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 71
------------------------------
Parameters for function evaluation 71:
1.2061541049e+01 w_top
5.6364698414e+01 hw
1.2078342842e+01 w_bot
8.1254824293e-01 t_top
4.4428302351e-01 tw
8.0412336841e-01 t_bot
(./SBOdrive /tmp/fileqxSYzD /tmp/fileo9qN7l)
Active response data for function evaluation 71:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 73
------------------------------
Parameters for function evaluation 73:
1.2001681679e+01 w_top
5.6256042092e+01 hw
1.2145069454e+01 w_bot
8.1957149484e-01 t_top
4.4493832943e-01 tw
8.0835551154e-01 t_bot
(./SBOdrive /tmp/fileuhPCzA /tmp/filecjjoFm)
Active response data for function evaluation 73:
Active set vector = { 1 1 1 }
1.8049200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 75
------------------------------
Parameters for function evaluation 75:
1.2057018935e+01 w_top
5.6065550088e+01 hw
1.2036313622e+01 w_bot
8.1907617777e-01 t_top
4.5417705428e-01 tw
7.9094380790e-01 t_bot
(./SBOdrive /tmp/fileuX63YI /tmp/file63YF8v)
Active response data for function evaluation 75:
Active set vector = { 1 1 1 }
1.9086100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 77
------------------------------
Parameters for function evaluation 77:
1.2087626023e+01 w_top
5.5675911512e+01 hw
1.2136554266e+01 w_bot
8.1819767571e-01 t_top
4.5054751449e-01 tw
8.0730104573e-01 t_bot
(./SBOdrive /tmp/fileWVE8cP /tmp/fileULsZdB)
Active response data for function evaluation 77:
Active set vector = { 1 1 1 }
1.9086900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 79
------------------------------
Parameters for function evaluation 79:
1.2093060541e+01 w_top
5.5943074013e+01 hw
1.2072613429e+01 w_bot
8.2812515412e-01 t_top
4.4330562908e-01 tw
8.0879329757e-01 t_bot
(./SBOdrive /tmp/filegcqsI4 /tmp/fileYZIFhU)
Active response data for function evaluation 79:
Active set vector = { 1 1 1 }
1.9064500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 81
------------------------------
Parameters for function evaluation 81:
1.2032742248e+01 w_top
5.6133116275e+01 hw
1.2044106508e+01 w_bot
8.2462240110e-01 t_top
4.6066542764e-01 tw
7.9385742748e-01 t_bot
(./SBOdrive /tmp/fileyDXnUj /tmp/file2J9Ob8)
Active response data for function evaluation 81:
Active set vector = { 1 1 1 }
1.9109900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 83
------------------------------
Parameters for function evaluation 83:
1.2029193146e+01 w_top
5.6337213438e+01 hw
1.2017219077e+01 w_bot
8.2484290420e-01 t_top
4.4989154157e-01 tw
7.9467869639e-01 t_bot
(./SBOdrive /tmp/fileWDNirI /tmp/fileSAfyfA)
Active response data for function evaluation 83:
Active set vector = { 1 1 1 }
1.9068000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 85
------------------------------
Parameters for function evaluation 85:
1.2037098081e+01 w_top
5.5705156631e+01 hw
1.2063919192e+01 w_bot
8.2156370672e-01 t_top
4.4121390971e-01 tw
8.0545845716e-01 t_bot
(./SBOdrive /tmp/file2g0sU6 /tmp/filecffhrX)
Active response data for function evaluation 85:
Active set vector = { 1 1 1 }
1.9054400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 87
------------------------------
Parameters for function evaluation 87:
1.2047240551e+01 w_top
5.5860696887e+01 hw
1.2096489024e+01 w_bot
8.1616455841e-01 t_top
4.5916614901e-01 tw
7.9265604166e-01 t_bot
(./SBOdrive /tmp/fileA4mXEE /tmp/fileyMaoHy)
Active response data for function evaluation 87:
Active set vector = { 1 1 1 }
1.9105400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.55625E+02 0.12000E+02 0.81062E+00 0.43750E+00 0.79063E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12100E+02 0.56375E+02 0.12150E+02 0.82937E+00 0.46328E+00 0.80938E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.190944E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56000E+02 0.12000E+02 0.82000E+00 0.45000E+00 0.80000E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -5.1206042344e+05 -2.7566435507e+03 2.1051798803e+05 1.3262836780e+05
-6.7326362580e+05 -2.3732619154e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = 0.16686E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12002E+02 0.56000E+02 0.12000E+02 0.81948E+00 0.45266E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -3.0070217584e+05 -5.1325010331e+04 2.1182426689e+05 -2.3145950051e+05
1.5665856408e+06 -2.8470496579e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = 0.16369E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12003E+02 0.56000E+02 0.12000E+02 0.82005E+00 0.44881E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -3.5292659381e+05 -3.4836764578e+04 2.7287923254e+05 5.2668640070e+03
-6.1963704917e+04 -3.2868204006e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = 0.16107E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12017E+02 0.56002E+02 0.12000E+02 0.81984E+00 0.45125E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.9452716536e+05 -5.6225874506e+04 2.7844110884e+05 -3.4884445418e+05
1.1445776406e+06 -2.7842950055e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = 0.15925E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12017E+02 0.56002E+02 0.12000E+02 0.82071E+00 0.44838E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.3973150031e+05 -4.3947367669e+04 3.2600185700e+05 -2.3573329212e+05
-1.0809087161e+05 -3.1221403277e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = 0.14937E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12026E+02 0.56003E+02 0.12000E+02 0.82937E+00 0.45236E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.1877514707e+05 -6.9301655478e+04 3.3824262229e+05 -1.9160302121e+06
9.0658293029e+05 -2.7055241706e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 6 OBJ = 0.14825E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12027E+02 0.56003E+02 0.12000E+02 0.82937E+00 0.45001E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.4527493177e+05 -5.9301430017e+04 3.7416063892e+05 -1.7238510892e+06
-5.5819123796e+04 -2.9652487501e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 7 OBJ = 0.14714E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12034E+02 0.56005E+02 0.12000E+02 0.82937E+00 0.45180E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.5248799197e+05 -7.3296532670e+04 3.7264389676e+05 -1.9758730533e+06
7.8108905326e+05 -2.6406606807e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 8 OBJ = 0.14634E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12035E+02 0.56006E+02 0.12000E+02 0.82937E+00 0.44981E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.7581106578e+05 -6.4746444492e+04 4.0286111869e+05 -1.8117003317e+06
-3.6651164343e+04 -2.8625826361e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 9 OBJ = 0.14560E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12042E+02 0.56008E+02 0.12000E+02 0.82937E+00 0.45123E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -9.7988830427e+04 -7.6309698423e+04 4.0398268583e+05 -2.0186165163e+06
6.2929454079e+05 -2.6000894760e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 10 OBJ = 0.14510E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12042E+02 0.56008E+02 0.12000E+02 0.82937E+00 0.44964E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.1711717272e+05 -6.9468882658e+04 4.2784226619e+05 -1.8873407859e+06
-2.1702054335e+04 -2.7776521779e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 11 OBJ = 0.14455E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12048E+02 0.56012E+02 0.12000E+02 0.82937E+00 0.45083E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -4.7192072017e+04 -7.9727990080e+04 4.3194740447e+05 -2.0689999403e+06
5.3540539351e+05 -2.5537761025e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 12 OBJ = 0.14419E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12048E+02 0.56012E+02 0.12000E+02 0.82937E+00 0.44950E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -6.4064837832e+04 -7.3924093016e+04 4.5171836880e+05 -1.9577538119e+06
-1.2023443707e+04 -2.7043029542e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 13 OBJ = 0.14368E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12055E+02 0.56019E+02 0.12000E+02 0.82937E+00 0.45068E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.4238295165e+03 -8.4283289109e+04 4.5776851676e+05 -2.1385452420e+06
5.2777317627e+05 -2.4951403592e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 14 OBJ = 0.14334E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12055E+02 0.56020E+02 0.12000E+02 0.82937E+00 0.44938E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.5202646428e+04 -7.8503787720e+04 4.7680027040e+05 -2.0279709919e+06
-1.0524919058e+04 -2.6446942142e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 15 OBJ = 0.14282E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12057E+02 0.56032E+02 0.12000E+02 0.82937E+00 0.45100E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.8157315896e+04 -8.8048935142e+04 4.6780594486e+05 -2.1960765644e+06
6.3421360302e+05 -2.4678449299e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 16 OBJ = 0.14233E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12057E+02 0.56032E+02 0.12000E+02 0.82937E+00 0.44942E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -3.5230085348e+03 -8.1012769083e+04 4.9074329721e+05 -2.0615543692e+06
-1.8676511970e+04 -2.6496227817e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 17 OBJ = 0.14214E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12057E+02 0.56036E+02 0.12000E+02 0.82937E+00 0.45043E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 8.4446899961e+03 -8.5886966737e+04 4.7933067895e+05 -2.1506214201e+06
3.8311543649e+05 -2.5472189480e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 18 OBJ = 0.14195E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12057E+02 0.56037E+02 0.12000E+02 0.82937E+00 0.44948E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -4.6194118606e+03 -8.1657612010e+04 4.9321621639e+05 -2.0696822473e+06
-1.0420586832e+04 -2.6569147029e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 19 OBJ = 0.14133E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12058E+02 0.56050E+02 0.12000E+02 0.82937E+00 0.45116E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.4591578565e+04 -9.0347928052e+04 4.7927694641e+05 -2.2232868510e+06
6.3467479974e+05 -2.5025306878e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 20 OBJ = 0.14084E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12058E+02 0.56050E+02 0.12000E+02 0.82937E+00 0.44958E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.1359045519e+03 -8.3278948242e+04 5.0237417576e+05 -2.0881168846e+06
-2.1816470534e+04 -2.6852143652e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 21 OBJ = 0.14068E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12058E+02 0.56053E+02 0.12000E+02 0.82937E+00 0.45050E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 5.2116666631e+03 -8.7770406879e+04 4.9210254697e+05 -2.1704318804e+06
3.4632993132e+05 -2.5885067524e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 22 OBJ = 0.14052E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12058E+02 0.56054E+02 0.12000E+02 0.82937E+00 0.44963E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -6.6403209070e+03 -8.3927991443e+04 5.0477064930e+05 -2.0968656636e+06
-1.1775173264e+04 -2.6883152759e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 23 OBJ = 0.13999E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12059E+02 0.56064E+02 0.12000E+02 0.82937E+00 0.45114E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.2997118285e+04 -9.1798370556e+04 4.9214760132e+05 -2.2366805298e+06
5.7344621481e+05 -2.5430317779e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 24 OBJ = 0.13958E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12059E+02 0.56065E+02 0.12000E+02 0.82937E+00 0.44970E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -6.7490893934e+03 -8.5380525463e+04 5.1314009927e+05 -2.1139388701e+06
-2.2833675627e+04 -2.7090190122e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 25 OBJ = 0.13942E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12059E+02 0.56068E+02 0.12000E+02 0.82937E+00 0.45062E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 5.4879754836e+03 -8.9881098134e+04 5.0265699906e+05 -2.1965129455e+06
3.4809068930e+05 -2.6117621683e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 26 OBJ = 0.13926E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12059E+02 0.56068E+02 0.12000E+02 0.82937E+00 0.44975E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -6.5256363136e+03 -8.5988860415e+04 5.1548926296e+05 -2.1219904110e+06
-1.4660226514e+04 -2.7128839088e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 27 OBJ = 0.13888E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56076E+02 0.12000E+02 0.82937E+00 0.45112E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.1278932132e+04 -9.2974429072e+04 5.0237114174e+05 -2.2475567951e+06
5.2593977708e+05 -2.5761481620e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 28 OBJ = 0.13854E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56077E+02 0.12000E+02 0.82937E+00 0.44978E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.0844975159e+03 -8.7009101587e+04 5.2191007363e+05 -2.1334477824e+06
-2.8592974969e+04 -2.7305504596e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 29 OBJ = 0.13842E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56079E+02 0.12000E+02 0.82937E+00 0.45057E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 3.3903673491e+03 -9.0777315815e+04 5.1254642415e+05 -2.2030711582e+06
2.8850941742e+05 -2.6464556035e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 30 OBJ = 0.13830E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56079E+02 0.12000E+02 0.82937E+00 0.44983E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -6.7627469490e+03 -8.7493788950e+04 5.2342116353e+05 -2.1401626752e+06
-1.8059956232e+04 -2.7319846024e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 31 OBJ = 0.13803E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56085E+02 0.12000E+02 0.82937E+00 0.45098E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 8.2642030205e+03 -9.3207005116e+04 5.1148854050e+05 -2.2438549577e+06
4.3749228021e+05 -2.6146968385e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 32 OBJ = 0.13779E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12060E+02 0.56085E+02 0.12000E+02 0.82937E+00 0.44984E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.3286524922e+03 -8.8147624202e+04 5.2810860924e+05 -2.1470355450e+06
-3.3364502548e+04 -2.7458688751e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 33 OBJ = 0.13769E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56087E+02 0.12000E+02 0.82937E+00 0.45056E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.1689909782e+03 -9.1521786921e+04 5.1941250923e+05 -2.2096354890e+06
2.5404991227e+05 -2.6691609762e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 34 OBJ = 0.13760E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56087E+02 0.12000E+02 0.82937E+00 0.44988E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.0538118062e+03 -8.8543522505e+04 5.2931237953e+05 -2.1525456769e+06
-2.4420907828e+04 -2.7469014289e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 35 OBJ = 0.13743E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56091E+02 0.12000E+02 0.82937E+00 0.45079E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 4.9001115914e+03 -9.2921775345e+04 5.1900901265e+05 -2.2329653323e+06
3.3759845968e+05 -2.6517896196e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 36 OBJ = 0.13728E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56091E+02 0.12000E+02 0.82937E+00 0.44988E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.5703132859e+03 -8.8883661163e+04 5.3234070264e+05 -2.1556346618e+06
-3.8953730805e+04 -2.7567836555e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 37 OBJ = 0.13720E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56092E+02 0.12000E+02 0.82937E+00 0.45052E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 9.8909434978e+02 -9.1890445856e+04 5.2433768687e+05 -2.2116281034e+06
2.1999414934e+05 -2.6872828576e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 38 OBJ = 0.13713E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56092E+02 0.12000E+02 0.82937E+00 0.44991E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.3882623872e+03 -8.9190231337e+04 5.3335464718e+05 -2.1598340786e+06
-3.2939268047e+04 -2.7579519294e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 39 OBJ = 0.13702E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56094E+02 0.12000E+02 0.82937E+00 0.45062E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.1077559369e+03 -9.2570701817e+04 5.2469397622e+05 -2.2225084501e+06
2.5443665058e+05 -2.6813334712e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 40 OBJ = 0.13693E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56095E+02 0.12000E+02 0.82937E+00 0.44990E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.8021720558e+03 -8.9370915689e+04 5.3533324686e+05 -2.1611698127e+06
-4.4780688319e+04 -2.7648699543e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 41 OBJ = 0.13686E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56096E+02 0.12000E+02 0.82937E+00 0.45048E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.8197065445e+01 -9.2097021025e+04 5.2789198014e+05 -2.2120892348e+06
1.9205948908e+05 -2.7010209382e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 42 OBJ = 0.13680E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56096E+02 0.12000E+02 0.82937E+00 0.44991E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -7.7757721642e+03 -8.9586666894e+04 5.3631640814e+05 -2.1639027917e+06
-4.3551522233e+04 -2.7669081412e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 43 OBJ = 0.13673E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56097E+02 0.12000E+02 0.82937E+00 0.45050E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.3472927931e+02 -9.2345974950e+04 5.2882490464e+05 -2.2154090664e+06
1.9572322019e+05 -2.7024636685e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 44 OBJ = 0.13667E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56098E+02 0.12000E+02 0.82937E+00 0.44990E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -8.0877291752e+03 -8.9700340220e+04 5.3769786455e+05 -2.1646303992e+06
-5.2523898583e+04 -2.7718768742e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 45 OBJ = 0.13661E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56099E+02 0.12000E+02 0.82937E+00 0.45044E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -9.0606062703e+02 -9.2178035501e+04 5.3076293287e+05 -2.2110515840e+06
1.6464809464e+05 -2.7130712701e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 46 OBJ = 0.13656E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56099E+02 0.12000E+02 0.82937E+00 0.44991E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -8.2088056155e+03 -8.9835038583e+04 5.3867619115e+05 -2.1660359470e+06
-5.5817681422e+04 -2.7747935954e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 47 OBJ = 0.13650E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56100E+02 0.12000E+02 0.82937E+00 0.45042E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.2304208541e+03 -9.2235136561e+04 5.3190113398e+05 -2.2110505829e+06
1.5519142723e+05 -2.7175711385e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 48 OBJ = 0.13645E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56100E+02 0.12000E+02 0.82937E+00 0.44990E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -8.4689107762e+03 -8.9915358735e+04 5.3975747667e+05 -2.1664633110e+06
-6.3329124060e+04 -2.7787787971e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 49 OBJ = 0.13640E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56101E+02 0.12000E+02 0.82937E+00 0.45039E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -1.8104313520e+03 -9.2191739372e+04 5.3322617672e+05 -2.2092446003e+06
1.3797826092e+05 -2.7240301935e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 50 OBJ = 0.13636E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56101E+02 0.12000E+02 0.82937E+00 0.44989E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = 0.136359E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12061E+02 0.56101E+02 0.12000E+02 0.82937E+00 0.44989E+00 0.80938E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 3 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-3 4 6
TERMINATION CRITERION
ITER EQUALS ITMAX
NUMBER OF ITERATIONS = 50
OBJECTIVE FUNCTION WAS EVALUATED 155 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 155 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 50 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 50 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 89
------------------------------
Parameters for function evaluation 89:
1.2004157579e+01 w_top
5.6128461628e+01 hw
1.2062692723e+01 w_bot
8.2273510264e-01 t_top
4.4549441184e-01 tw
7.9599752919e-01 t_bot
(./SBOdrive /tmp/fileGNiJ0m /tmp/file0aiw3k)
Active response data for function evaluation 89:
Active set vector = { 1 1 1 }
1.9055400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 91
------------------------------
Parameters for function evaluation 91:
1.2002900409e+01 w_top
5.5925327539e+01 hw
1.2019162366e+01 w_bot
8.2441982383e-01 t_top
4.4722775929e-01 tw
7.9827643746e-01 t_bot
(./SBOdrive /tmp/fileSFX0bf /tmp/fileu2BN4b)
Active response data for function evaluation 91:
Active set vector = { 1 1 1 }
1.9068100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 93
------------------------------
Parameters for function evaluation 93:
1.2000072472e+01 w_top
5.5952240440e+01 hw
1.2065600714e+01 w_bot
8.1709964445e-01 t_top
4.5190403755e-01 tw
8.0431757778e-01 t_bot
(./SBOdrive /tmp/file4LVa86 /tmp/file02Bqr7)
Active response data for function evaluation 93:
Active set vector = { 1 1 1 }
1.9076800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 95
------------------------------
Parameters for function evaluation 95:
1.2021540609e+01 w_top
5.5973057198e+01 hw
1.2043133159e+01 w_bot
8.1653061925e-01 t_top
4.5311939603e-01 tw
8.0397147413e-01 t_bot
(./SBOdrive /tmp/fileQYLtj8 /tmp/fileaEOpm7)
Active response data for function evaluation 95:
Active set vector = { 1 1 1 }
1.9081100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 97
------------------------------
Parameters for function evaluation 97:
1.2037353863e+01 w_top
5.5824253712e+01 hw
1.2017152027e+01 w_bot
8.2236438824e-01 t_top
4.5444347905e-01 tw
7.9535406502e-01 t_bot
(./SBOdrive /tmp/file8BrMa9 /tmp/fileAjftDb)
Active response data for function evaluation 97:
Active set vector = { 1 1 1 }
1.9095300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 99
------------------------------
Parameters for function evaluation 99:
1.2006376829e+01 w_top
5.5836833838e+01 hw
1.2059116065e+01 w_bot
8.2363753457e-01 t_top
4.5620014322e-01 tw
8.0196786106e-01 t_bot
(./SBOdrive /tmp/filembWdbj /tmp/fileEz88wk)
Active response data for function evaluation 99:
Active set vector = { 1 1 1 }
1.9100500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 101
------------------------------
Parameters for function evaluation 101:
1.2043399206e+01 w_top
5.5881223217e+01 hw
1.2039915070e+01 w_bot
8.1573682840e-01 t_top
4.5136712505e-01 tw
8.0346789641e-01 t_bot
(./SBOdrive /tmp/filekyYzFq /tmp/file4Ec27s)
Active response data for function evaluation 101:
Active set vector = { 1 1 1 }
1.9078300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 103
------------------------------
Parameters for function evaluation 103:
1.2029792804e+01 w_top
5.6040934337e+01 hw
1.2015934404e+01 w_bot
8.2010793265e-01 t_top
4.5062820640e-01 tw
7.9631760853e-01 t_bot
(./SBOdrive /tmp/file8z4luJ /tmp/fileobawlP)
Active response data for function evaluation 103:
Active set vector = { 1 1 1 }
1.9074200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 105
------------------------------
Parameters for function evaluation 105:
1.2018878808e+01 w_top
5.5849054528e+01 hw
1.2012611530e+01 w_bot
8.1888768992e-01 t_top
4.5362867860e-01 tw
7.9766012610e-01 t_bot
(./SBOdrive /tmp/fileuZ9lf2 /tmp/fileI3QVR6)
Active response data for function evaluation 105:
Active set vector = { 1 1 1 }
1.9087600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 107
------------------------------
Parameters for function evaluation 107:
1.2045240363e+01 w_top
5.6138676392e+01 hw
1.2027385789e+01 w_bot
8.2148697386e-01 t_top
4.5613684952e-01 tw
7.9845623040e-01 t_bot
(./SBOdrive /tmp/fileef0d7t /tmp/fileYTdRhC)
Active response data for function evaluation 107:
Active set vector = { 1 1 1 }
1.9092600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 109
------------------------------
Parameters for function evaluation 109:
1.2047086864e+01 w_top
5.6001031518e+01 hw
1.2001463949e+01 w_bot
8.2429421151e-01 t_top
4.4870667188e-01 tw
7.9872657498e-01 t_bot
(./SBOdrive /tmp/file8PQpRV /tmp/fileqYynK2)
Active response data for function evaluation 109:
Active set vector = { 1 1 1 }
1.9073800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 111
------------------------------
Parameters for function evaluation 111:
1.2048965929e+01 w_top
5.6029929120e+01 hw
1.2073145025e+01 w_bot
8.2309014491e-01 t_top
4.4783326845e-01 tw
8.0037799221e-01 t_bot
(./SBOdrive /tmp/file22GrLw /tmp/fileMbo43G)
Active response data for function evaluation 111:
Active set vector = { 1 1 1 }
1.9069500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 113
------------------------------
Parameters for function evaluation 113:
1.2038204245e+01 w_top
5.5864443883e+01 hw
1.2045881755e+01 w_bot
8.1974991971e-01 t_top
4.4577357867e-01 tw
7.9589704355e-01 t_bot
(./SBOdrive /tmp/file7tMgEL /tmp/fileWmuCzN)
Active response data for function evaluation 113:
Active set vector = { 1 1 1 }
1.9063100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 115
------------------------------
Parameters for function evaluation 115:
1.2030431781e+01 w_top
5.5897298936e+01 hw
1.2056185666e+01 w_bot
8.2384191935e-01 t_top
4.4851469507e-01 tw
7.9985723478e-01 t_bot
(./SBOdrive /tmp/filevIsgGO /tmp/fileCqkYlP)
Active response data for function evaluation 115:
Active set vector = { 1 1 1 }
1.9074700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 117
------------------------------
Parameters for function evaluation 117:
1.2000000000e+01 w_top
5.6019819143e+01 hw
1.2000000000e+01 w_bot
8.1531250000e-01 t_top
4.4335937500e-01 tw
8.0045218220e-01 t_bot
(./SBOdrive /tmp/filenIFo0Y /tmp/file2TnYp1)
Active response data for function evaluation 117:
Active set vector = { 1 1 1 }
1.9044200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio = 1.0013551026e+00:
<<<<< Excellent Progress, Accept Step, INCREASE Trust Region Size
*********************************************
Begin SBO Iteration Number 5
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5738569143e+01
1.2000000000e+01
8.0828125000e-01
4.3750000000e-01
7.9342093220e-01
Current Trust Region Upper Bounds
1.2075000000e+01
5.6301069143e+01
1.2112500000e+01
8.2234375000e-01
4.5332031250e-01
8.0748343220e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution
------------------------------
Begin Function Evaluation 119
------------------------------
Parameters for function evaluation 119:
1.2020226351e+01 w_top
5.6026040929e+01 hw
1.2062766205e+01 w_bot
8.0956568898e-01 t_top
4.4267110157e-01 tw
8.0306263542e-01 t_bot
(./SBOdrive /tmp/file18zMhd /tmp/filekNqGcj)
Active response data for function evaluation 119:
Active set vector = { 1 1 1 }
1.9038200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 121
------------------------------
Parameters for function evaluation 121:
1.2023359129e+01 w_top
5.5847683563e+01 hw
1.2054166848e+01 w_bot
8.1552744764e-01 t_top
4.4613332130e-01 tw
8.0427355176e-01 t_bot
(./SBOdrive /tmp/filelrRpXC /tmp/filemSr8VJ)
Active response data for function evaluation 121:
Active set vector = { 1 1 1 }
1.9060300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 123
------------------------------
Parameters for function evaluation 123:
1.2061904785e+01 w_top
5.6189480706e+01 hw
1.2007823944e+01 w_bot
8.1123271954e-01 t_top
4.4015983361e-01 tw
8.0559708328e-01 t_bot
(./SBOdrive /tmp/filetl3K8Z /tmp/file2rqsR5)
Active response data for function evaluation 123:
Active set vector = { 1 1 1 }
2.1215800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 125
------------------------------
Parameters for function evaluation 125:
1.2026612541e+01 w_top
5.5964299582e+01 hw
1.2026001584e+01 w_bot
8.2107725908e-01 t_top
4.5281245826e-01 tw
7.9620609158e-01 t_bot
(./SBOdrive /tmp/filelXL1xw /tmp/fileS0hmQF)
Active response data for function evaluation 125:
Active set vector = { 1 1 1 }
1.9084200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2------------------------------
Begin Function Evaluation 127
------------------------------
Parameters for function evaluation 127:
1.2043577053e+01 w_top
5.6280442938e+01 hw
1.2086053970e+01 w_bot
8.1330020281e-01 t_top
4.5217125128e-01 tw
8.0726779112e-01 t_bot
(./SBOdrive /tmp/fileT8NKG2 /tmp/fileyI0RIa)
Active response data for function evaluation 127:
Active set vector = { 1 1 1 }
1.8075000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 129
------------------------------
Parameters for function evaluation 129:
1.2004482563e+01 w_top
5.5949912927e+01 hw
1.2056504583e+01 w_bot
8.1646498845e-01 t_top
4.4052756160e-01 tw
8.0205552493e-01 t_bot
(./SBOdrive /tmp/filerq5XbI /tmp/fileoH3qHT)
Active response data for function evaluation 129:
Active set vector = { 1 1 1 }
1.9038300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 131
------------------------------
Parameters for function evaluation 131:
1.2060844425e+01 w_top
5.6290133817e+01 hw
1.2036998244e+01 w_bot
8.0872875632e-01 t_top
4.4438373279e-01 tw
7.9668832632e-01 t_bot
(./SBOdrive /tmp/fileJNnKCn /tmp/fileq8rz1x)
Active response data for function evaluation 131:
Active set vector = { 1 1 1 }
1.9038300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 133
------------------------------
Parameters for function evaluation 133:
1.2042626906e+01 w_top
5.5903864965e+01 hw
1.2102434908e+01 w_bot
8.1236148719e-01 t_top
4.4406729745e-01 tw
8.0619893531e-01 t_bot
(./SBOdrive /tmp/file3RZDdc /tmp/fileOGQC2p)
Active response data for function evaluation 133:
Active set vector = { 1 1 1 }
1.9050600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 135
------------------------------
Parameters for function evaluation 135:
1.2064548444e+01 w_top
5.5775507406e+01 hw
1.2015178823e+01 w_bot
8.1501309344e-01 t_top
4.3864630716e-01 tw
7.9878065096e-01 t_bot
(./SBOdrive /tmp/file9m5jI0 /tmp/fileqC9Cfd)
Active response data for function evaluation 135:
Active set vector = { 1 1 1 }
2.1715100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 137
------------------------------
Parameters for function evaluation 137:
1.2046799981e+01 w_top
5.5886874981e+01 hw
1.2071958185e+01 w_bot
8.2026107623e-01 t_top
4.4257995505e-01 tw
8.0539186734e-01 t_bot
(./SBOdrive /tmp/fileJTN9jY /tmp/fileKgCcre)
Active response data for function evaluation 137:
Active set vector = { 1 1 1 }
1.9053300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 139
------------------------------
Parameters for function evaluation 139:
1.2058551748e+01 w_top
5.6159535470e+01 hw
1.2065511186e+01 w_bot
8.1374036298e-01 t_top
4.3810824745e-01 tw
8.0290820848e-01 t_bot
(./SBOdrive /tmp/filedv0QGV /tmp/file06Bzva)
Active response data for function evaluation 139:
Active set vector = { 1 1 1 }
2.3747200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 141
------------------------------
Parameters for function evaluation 141:
1.2016724245e+01 w_top
5.5860644246e+01 hw
1.2045085803e+01 w_bot
8.1445031761e-01 t_top
4.4683620334e-01 tw
7.9779911547e-01 t_bot
(./SBOdrive /tmp/file3D4Zk2 /tmp/filekVBlAk)
Active response data for function evaluation 141:
Active set vector = { 1 1 1 }
1.9060600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 143
------------------------------
Parameters for function evaluation 143:
1.2073477469e+01 w_top
5.6043289251e+01 hw
1.2034817118e+01 w_bot
8.1206322828e-01 t_top
4.5247559090e-01 tw
8.0154454699e-01 t_bot
(./SBOdrive /tmp/file1mNpL8 /tmp/file6bjaTp)
Active response data for function evaluation 143:
Active set vector = { 1 1 1 }
1.9076200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 145
------------------------------
Parameters for function evaluation 145:
1.2039439439e+01 w_top
5.5981475583e+01 hw
1.2073975777e+01 w_bot
8.1914110616e-01 t_top
4.4125319444e-01 tw
7.9415105682e-01 t_bot
(./SBOdrive /tmp/filehhbOmo /tmp/file8gcMVI)
Active response data for function evaluation 145:
Active set vector = { 1 1 1 }
1.9044300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.55739E+02 0.12000E+02 0.80828E+00 0.43750E+00 0.79342E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12075E+02 0.56301E+02 0.12113E+02 0.82234E+00 0.45332E+00 0.80748E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.190376E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56020E+02 0.12000E+02 0.81531E+00 0.44336E+00 0.80045E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 9.2044861048e+05 4.2585021462e+04 -9.7608170609e+04 8.6148359694e+05
4.5206345989e+06 5.8475225704e+06 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = 0.12372E+05
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56020E+02 0.12000E+02 0.81272E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.6164480226e+06 8.4193408529e+04 -4.2485997646e+05 1.5551683298e+07
1.5880412722e+07 2.1559379577e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = -0.59456E+05
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56020E+02 0.12000E+02 0.80874E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.4909114183e+06 1.3875508435e+05 -4.6319335206e+05 2.0465088412e+07
2.0084852037e+07 2.4547600622e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = -0.69094E+05
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56020E+02 0.12001E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.4759412027e+06 1.4525187041e+05 -4.6704417446e+05 2.1034974660e+07
2.0571614329e+07 2.4898769712e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = -0.11782E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55943E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.1713032016e+06 2.9401103659e+05 -9.6576957486e+04 2.3177508801e+07
2.1554715820e+07 2.8964662629e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = -0.18244E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55739E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.3312303039e+06 3.3746042954e+05 -3.3828401821e+05 2.5974961483e+07
2.2980099415e+07 2.7941492954e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 6 OBJ = -0.18244E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55739E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
ITER = 7 OBJ = -0.18244E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55739E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.3312303039e+06 3.3746042954e+05 -3.3828401821e+05 2.5974961483e+07
2.2980099415e+07 2.7941492954e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 8 OBJ = -0.18244E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55739E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = -0.182443E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.55739E+02 0.12113E+02 0.80828E+00 0.43750E+00 0.79342E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1 -2 3 -4 -5 -6
TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR 3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1)) LESS THAN DABFUN FOR 3 ITERATIONS
NUMBER OF ITERATIONS = 8
OBJECTIVE FUNCTION WAS EVALUATED 21 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 21 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 7 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 7 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 147
------------------------------
Parameters for function evaluation 147:
1.2012427942e+01 w_top
5.6035777775e+01 hw
1.2050576612e+01 w_bot
8.1844147180e-01 t_top
4.4114119036e-01 tw
7.9773653978e-01 t_bot
(./SBOdrive /tmp/fileD8zi4H /tmp/fileMvWPt4)
Active response data for function evaluation 147:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 149
------------------------------
Parameters for function evaluation 149:
1.2035531427e+01 w_top
5.6024567848e+01 hw
1.2005999581e+01 w_bot
8.1186581964e-01 t_top
4.3996141417e-01 tw
8.0064288034e-01 t_bot
(./SBOdrive /tmp/fileNjamy9 /tmp/fileAB1rEu)
Active response data for function evaluation 149:
Active set vector = { 1 1 1 }
2.0876400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 151
------------------------------
Parameters for function evaluation 151:
1.2029289750e+01 w_top
5.5881282622e+01 hw
1.2001770722e+01 w_bot
8.1670963508e-01 t_top
4.4758453382e-01 tw
8.0030784379e-01 t_bot
(./SBOdrive /tmp/fileBdPfJA /tmp/filewfMppZ)
Active response data for function evaluation 151:
Active set vector = { 1 1 1 }
1.9065300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 153
------------------------------
Parameters for function evaluation 153:
1.2008012115e+01 w_top
5.6151479633e+01 hw
1.2017090100e+01 w_bot
8.1459300393e-01 t_top
4.4370790020e-01 tw
8.0293333133e-01 t_bot
(./SBOdrive /tmp/fileNtNkqd /tmp/fileGimjbD)
Active response data for function evaluation 153:
Active set vector = { 1 1 1 }
1.9041800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 155
------------------------------
Parameters for function evaluation 155:
1.2002559615e+01 w_top
5.6113285028e+01 hw
1.2041315941e+01 w_bot
8.1267677045e-01 t_top
4.3964063775e-01 tw
8.0256254766e-01 t_bot
(./SBOdrive /tmp/fileTS5qGN /tmp/fileofEt9b)
Active response data for function evaluation 155:
Active set vector = { 1 1 1 }
2.1604600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 157
------------------------------
Parameters for function evaluation 157:
1.2023438797e+01 w_top
5.6002493953e+01 hw
1.2043171184e+01 w_bot
8.1284533909e-01 t_top
4.4801885465e-01 tw
8.0328976361e-01 t_bot
(./SBOdrive /tmp/fileXGRZ4w /tmp/filequ2w7Y)
Active response data for function evaluation 157:
Active set vector = { 1 1 1 }
1.9059900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 159
------------------------------
Parameters for function evaluation 159:
1.2025474911e+01 w_top
5.6070858947e+01 hw
1.2035637854e+01 w_bot
8.1645345819e-01 t_top
4.4170555748e-01 tw
8.0094879064e-01 t_bot
(./SBOdrive /tmp/filel5undg /tmp/fileMHy5ZG)
Active response data for function evaluation 159:
Active set vector = { 1 1 1 }
1.9040100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2------------------------------
Begin Function Evaluation 161
------------------------------
Parameters for function evaluation 161:
1.2027246172e+01 w_top
5.5966902406e+01 hw
1.2020388650e+01 w_bot
8.1316365548e-01 t_top
4.3910962540e-01 tw
8.0384108328e-01 t_bot
(./SBOdrive /tmp/fileVLQoq8 /tmp/fileUzL3CC)
Active response data for function evaluation 161:
Active set vector = { 1 1 1 }
2.1785700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 163
------------------------------
Parameters for function evaluation 163:
1.2015894004e+01 w_top
5.5904792439e+01 hw
1.2012852265e+01 w_bot
8.1585154293e-01 t_top
4.4029987040e-01 tw
8.0225621952e-01 t_bot
(./SBOdrive /tmp/filefqh9B0 /tmp/filegZT8Ft)
Active response data for function evaluation 163:
Active set vector = { 1 1 1 }
1.9038900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 165
------------------------------
Parameters for function evaluation 165:
1.2000599733e+01 w_top
5.5933159597e+01 hw
1.2025313200e+01 w_bot
8.1621979151e-01 t_top
4.4412591242e-01 tw
7.9747334663e-01 t_bot
(./SBOdrive /tmp/fileDoFrU1 /tmp/fileSg4ipy)
Active response data for function evaluation 165:
Active set vector = { 1 1 1 }
1.9050000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 167
------------------------------
Parameters for function evaluation 167:
1.2021277183e+01 w_top
5.6018767657e+01 hw
1.2023316509e+01 w_bot
8.1720791795e-01 t_top
4.3886436086e-01 tw
8.0151402073e-01 t_bot
(./SBOdrive /tmp/fileFHzk72 /tmp/fileqsC6jy)
Active response data for function evaluation 167:
Active set vector = { 1 1 1 }
2.2287700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 169
------------------------------
Parameters for function evaluation 169:
1.2032650725e+01 w_top
5.5950290617e+01 hw
1.2008822290e+01 w_bot
8.1408168721e-01 t_top
4.4402068677e-01 tw
8.0139666912e-01 t_bot
(./SBOdrive /tmp/file5gCoqd /tmp/fileaWcacM)
Active response data for function evaluation 169:
Active set vector = { 1 1 1 }
1.9049400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 171
------------------------------
Parameters for function evaluation 171:
1.2009762542e+01 w_top
5.5927328095e+01 hw
1.2038297713e+01 w_bot
8.1767554805e-01 t_top
4.4683216950e-01 tw
7.9727548314e-01 t_bot
(./SBOdrive /tmp/filenY08un /tmp/fileonaO0U)
Active response data for function evaluation 171:
Active set vector = { 1 1 1 }
1.9061200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 173
------------------------------
Parameters for function evaluation 173:
1.2019695875e+01 w_top
5.6092770144e+01 hw
1.2003053215e+01 w_bot
8.1798968670e-01 t_top
4.4699522471e-01 tw
7.9997663689e-01 t_bot
(./SBOdrive /tmp/fileBXxQYG /tmp/filec1Ii2h)
Active response data for function evaluation 173:
Active set vector = { 1 1 1 }
1.9058100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 175
------------------------------
Parameters for function evaluation 175:
1.2000000000e+01 w_top
5.5996491627e+01 hw
1.2000001085e+01 w_bot
8.1179687500e-01 t_top
4.4554561670e-01 tw
8.0200698534e-01 t_bot
(./SBOdrive /tmp/filejSgbP2 /tmp/fileGSR3dF)
Active response data for function evaluation 175:
Active set vector = { 1 1 1 }
1.9049100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio = -8.4662702547e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 7
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.5949506643e+01
1.2000000000e+01
8.1355468750e-01
4.4086914063e-01
7.9869436970e-01
Current Trust Region Upper Bounds
1.2018750000e+01
5.6090131643e+01
1.2028125000e+01
8.1707031250e-01
4.4584960938e-01
8.0220999470e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution
------------------------------
Begin Function Evaluation 177
------------------------------
Parameters for function evaluation 177:
1.2001531104e+01 w_top
5.6010343931e+01 hw
1.2024695474e+01 w_bot
8.1599906303e-01 t_top
4.4267553609e-01 tw
8.0125761926e-01 t_bot
(./SBOdrive /tmp/file5KDL2F /tmp/filecncZWl)
Active response data for function evaluation 177:
Active set vector = { 1 1 1 }
1.9043000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 179
------------------------------
Parameters for function evaluation 179:
1.2007540268e+01 w_top
5.6085592884e+01 hw
1.2022885515e+01 w_bot
8.1580939852e-01 t_top
4.4352440064e-01 tw
8.0208625094e-01 t_bot
(./SBOdrive /tmp/file50s87i /tmp/fileAFCNVX)
Active response data for function evaluation 179:
Active set vector = { 1 1 1 }
1.9044100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 181
------------------------------
Parameters for function evaluation 181:
1.2015776288e+01 w_top
5.6064637549e+01 hw
1.2012012900e+01 w_bot
8.1499743944e-01 t_top
4.4506931703e-01 tw
8.0117193820e-01 t_bot
(./SBOdrive /tmp/filezgSnk5 /tmp/fileOglVwN)
Active response data for function evaluation 181:
Active set vector = { 1 1 1 }
1.9049500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 183
------------------------------
Parameters for function evaluation 183:
1.2017992727e+01 w_top
5.6076034072e+01 hw
1.2005315720e+01 w_bot
8.1653330019e-01 t_top
4.4359152641e-01 tw
7.9936081693e-01 t_bot
(./SBOdrive /tmp/filelrI0nR /tmp/filemMDzry)
Active response data for function evaluation 183:
Active set vector = { 1 1 1 }
1.9045700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 185
------------------------------
Parameters for function evaluation 185:
1.2000518489e+01 w_top
5.5973135635e+01 hw
1.2017858728e+01 w_bot
8.1700184339e-01 t_top
4.4305146468e-01 tw
8.0138820341e-01 t_bot
(./SBOdrive /tmp/filenmSPHM /tmp/fileYxBjcx)
Active response data for function evaluation 185:
Active set vector = { 1 1 1 }
1.9046100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 187
------------------------------
Parameters for function evaluation 187:
1.2016531615e+01 w_top
5.5985664324e+01 hw
1.2021694265e+01 w_bot
8.1368692102e-01 t_top
4.4570911058e-01 tw
8.0156783142e-01 t_bot
(./SBOdrive /tmp/file1g61XH /tmp/fileGcs2er)
Active response data for function evaluation 187:
Active set vector = { 1 1 1 }
1.9052800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 189
------------------------------
Parameters for function evaluation 189:
1.2018531271e+01 w_top
5.6074383932e+01 hw
1.2020087027e+01 w_bot
8.1381792143e-01 t_top
4.4234541081e-01 tw
8.0098627224e-01 t_bot
(./SBOdrive /tmp/filefgFCBM /tmp/fileK09Msz)
Active response data for function evaluation 189:
Active set vector = { 1 1 1 }
1.9039300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 191
------------------------------
Parameters for function evaluation 191:
1.2011377535e+01 w_top
5.5967725259e+01 hw
1.2023786211e+01 w_bot
8.1358732846e-01 t_top
4.4140709085e-01 tw
7.9884276618e-01 t_bot
(./SBOdrive /tmp/filenqBDWQ /tmp/fileyKxkvC)
Active response data for function evaluation 191:
Active set vector = { 1 1 1 }
1.9038400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 193
------------------------------
Parameters for function evaluation 193:
1.2008266114e+01 w_top
5.6046448778e+01 hw
1.2001200998e+01 w_bot
8.1537530318e-01 t_top
4.4495653814e-01 tw
8.0164132784e-01 t_bot
(./SBOdrive /tmp/filejMcTy4 /tmp/fileO0a3xT)
Active response data for function evaluation 193:
Active set vector = { 1 1 1 }
1.9049500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 195
------------------------------
Parameters for function evaluation 195:
1.2006134133e+01 w_top
5.6037696182e+01 hw
1.2012880745e+01 w_bot
8.1455899476e-01 t_top
4.4536523589e-01 tw
7.9981838575e-01 t_bot
(./SBOdrive /tmp/filePCEoZh /tmp/fileO220R5)
Active response data for function evaluation 195:
Active set vector = { 1 1 1 }
1.9050200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 197
------------------------------
Parameters for function evaluation 197:
1.2009198805e+01 w_top
5.5998981763e+01 hw
1.2003182381e+01 w_bot
8.1527927248e-01 t_top
4.4165590386e-01 tw
8.0199835563e-01 t_bot
(./SBOdrive /tmp/file9c1VOz /tmp/fileCAwo6q)
Active response data for function evaluation 197:
Active set vector = { 1 1 1 }
1.9039800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 199
------------------------------
Parameters for function evaluation 199:
1.2003959129e+01 w_top
5.6021305414e+01 hw
1.2000076930e+01 w_bot
8.1683978906e-01 t_top
4.4409963629e-01 tw
8.0004872165e-01 t_bot
(./SBOdrive /tmp/fileZR3pY0 /tmp/filemI1m6Q)
Active response data for function evaluation 199:
Active set vector = { 1 1 1 }
1.9048300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 201
------------------------------
Parameters for function evaluation 201:
1.2002450138e+01 w_top
5.6008194187e+01 hw
1.2016702505e+01 w_bot
8.1456103326e-01 t_top
4.4472159035e-01 tw
8.0032767626e-01 t_bot
(./SBOdrive /tmp/fileDW0o5r /tmp/fileAX0KCl)
Active response data for function evaluation 201:
Active set vector = { 1 1 1 }
1.9048600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 203
------------------------------
Parameters for function evaluation 203:
1.2009603169e+01 w_top
5.6068863718e+01 hw
1.2015315265e+01 w_bot
8.1469224505e-01 t_top
4.4122107114e-01 tw
7.9957945028e-01 t_bot
(./SBOdrive /tmp/filejmKtj2 /tmp/fileSGaLJU)
Active response data for function evaluation 203:
Active set vector = { 1 1 1 }
1.9035800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.55950E+02 0.12000E+02 0.81355E+00 0.44087E+00 0.79869E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12019E+02 0.56090E+02 0.12028E+02 0.81707E+00 0.44585E+00 0.80221E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.190442E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56020E+02 0.12000E+02 0.81531E+00 0.44336E+00 0.80045E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6492035150e+02 -2.9823288839e+02 -6.2653656593e+01 9.1304288136e+03
3.3802692326e+04 -2.3985669685e+01 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = 0.19034E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56023E+02 0.12001E+02 0.81355E+00 0.44087E+00 0.80073E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 9.1238416702e+02 -3.1493927770e+02 -6.0436370422e+01 9.6373791203e+03
3.3560230721e+04 1.6436164742e+02 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = 0.19032E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12014E+02 0.81355E+00 0.44087E+00 0.79869E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 9.7330784648e+02 -2.9054866196e+02 2.6775538742e+01 1.0434255181e+04
3.3580582729e+04 -4.3938395773e+02 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = 0.19032E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12013E+02 0.81355E+00 0.44087E+00 0.80026E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.0061941723e+03 -2.9495400976e+02 4.7281670734e+01 1.0259482805e+04
3.3636523747e+04 1.3013682543e+01 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = 0.19032E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81355E+00 0.44087E+00 0.79989E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 9.9542440711e+02 -2.9467606106e+02 3.4172670915e+01 1.0281353519e+04
3.3632669549e+04 -1.1244973409e+02 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = 0.19032E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81355E+00 0.44087E+00 0.80034E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = 0.190320E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81355E+00 0.44087E+00 0.80034E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 4 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1 2 -4 -5
TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR 3 ITERATIONS
NUMBER OF ITERATIONS = 5
OBJECTIVE FUNCTION WAS EVALUATED 20 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 20 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 5 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 5 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 205
------------------------------
Parameters for function evaluation 205:
1.2024267101e+01 w_top
5.6011584610e+01 hw
1.2033758480e+01 w_bot
8.1590997923e-01 t_top
4.3955752106e-01 tw
8.0217365911e-01 t_bot
(./SBOdrive /tmp/filed7aL8I /tmp/fileMzhSHF)
Active response data for function evaluation 205:
Active set vector = { 1 1 1 }
2.1373800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2------------------------------
Begin Function Evaluation 207
------------------------------
Parameters for function evaluation 207:
1.2003856824e+01 w_top
5.6141376449e+01 hw
1.2040915953e+01 w_bot
8.1578305078e-01 t_top
4.4424681668e-01 tw
7.9947161159e-01 t_bot
(./SBOdrive /tmp/fileB50Nrq /tmp/fileUzEtLl)
Active response data for function evaluation 207:
Active set vector = { 1 1 1 }
1.9044700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 209
------------------------------
Parameters for function evaluation 209:
1.2009803439e+01 w_top
5.6193448454e+01 hw
1.2053905073e+01 w_bot
8.1229426082e-01 t_top
4.4385273589e-01 tw
7.9929624370e-01 t_bot
(./SBOdrive /tmp/filefOGr1e /tmp/filewRC3Eb)
Active response data for function evaluation 209:
Active set vector = { 1 1 1 }
1.9039200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 211
------------------------------
Parameters for function evaluation 211:
1.2004590041e+01 w_top
5.6145227506e+01 hw
1.2029932035e+01 w_bot
8.1380987552e-01 t_top
4.3815774609e-01 tw
8.0026087422e-01 t_bot
(./SBOdrive /tmp/filevqzWR3 /tmp/fileKKAeA1)
Active response data for function evaluation 211:
Active set vector = { 1 1 1 }
2.3605100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 213
------------------------------
Parameters for function evaluation 213:
1.2023543895e+01 w_top
5.6020793322e+01 hw
1.2002139607e+01 w_bot
8.1433580544e-01 t_top
4.4084845626e-01 tw
7.9870759550e-01 t_bot
(./SBOdrive /tmp/fileZzvE93 /tmp/fileeIEVp5)
Active response data for function evaluation 213:
Active set vector = { 1 1 1 }
1.9036400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 215
------------------------------
Parameters for function evaluation 215:
1.2010258246e+01 w_top
5.6071553438e+01 hw
1.2012685076e+01 w_bot
8.1259849192e-01 t_top
4.3831780430e-01 tw
7.9778422685e-01 t_bot
(./SBOdrive /tmp/fileTkuFz4 /tmp/fileOw4QF4)
Active response data for function evaluation 215:
Active set vector = { 1 1 1 }
2.3133300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 217
------------------------------
Parameters for function evaluation 217:
1.2018602489e+01 w_top
5.6047929946e+01 hw
1.2006729570e+01 w_bot
8.1187022551e-01 t_top
4.4334673550e-01 tw
8.0158697677e-01 t_bot
(./SBOdrive /tmp/file96LWZd /tmp/fileExu5vh)
Active response data for function evaluation 217:
Active set vector = { 1 1 1 }
1.9041600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 219
------------------------------
Parameters for function evaluation 219:
1.2027189448e+01 w_top
5.5990616792e+01 hw
1.2017103106e+01 w_bot
8.1153368733e-01 t_top
4.4156737484e-01 tw
7.9861186201e-01 t_bot
(./SBOdrive /tmp/file9IWQnn /tmp/file6BMFHp)
Active response data for function evaluation 219:
Active set vector = { 1 1 1 }
1.9037400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 221
------------------------------
Parameters for function evaluation 221:
1.2023034429e+01 w_top
5.6129389731e+01 hw
1.2001922592e+01 w_bot
8.1538166086e-01 t_top
4.3911005242e-01 tw
8.0034819794e-01 t_bot
(./SBOdrive /tmp/filexFpAqG /tmp/fileeMoepM)
Active response data for function evaluation 221:
Active set vector = { 1 1 1 }
2.2341100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 223
------------------------------
Parameters for function evaluation 223:
1.2017143797e+01 w_top
5.6087828483e+01 hw
1.2043842938e+01 w_bot
8.1611712254e-01 t_top
4.4269205193e-01 tw
8.0267868651e-01 t_bot
(./SBOdrive /tmp/fileJdPcoZ /tmp/fileuyEY43)
Active response data for function evaluation 223:
Active set vector = { 1 1 1 }
1.9042200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 225
------------------------------
Parameters for function evaluation 225:
1.2011842403e+01 w_top
5.6077813593e+01 hw
1.2013925772e+01 w_bot
8.1451241388e-01 t_top
4.4446260936e-01 tw
8.0088370447e-01 t_bot
(./SBOdrive /tmp/fileVtnuwr /tmp/file8GvlKz)
Active response data for function evaluation 225:
Active set vector = { 1 1 1 }
1.9046400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 227
------------------------------
Parameters for function evaluation 227:
1.2000016531e+01 w_top
5.6185604305e+01 hw
1.2036599437e+01 w_bot
8.1267444819e-01 t_top
4.4191793889e-01 tw
8.0101358625e-01 t_bot
(./SBOdrive /tmp/fileVp0fRT /tmp/file4KhjS0)
Active response data for function evaluation 227:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 229
------------------------------
Parameters for function evaluation 229:
1.2013747264e+01 w_top
5.6037515789e+01 hw
1.2038849560e+01 w_bot
8.1211922284e-01 t_top
4.4154708104e-01 tw
8.0010709926e-01 t_bot
(./SBOdrive /tmp/fileTy45mv /tmp/filecphKVF)
Active response data for function evaluation 229:
Active set vector = { 1 1 1 }
1.9035800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 231
------------------------------
Parameters for function evaluation 231:
1.2021067347e+01 w_top
5.6109399997e+01 hw
1.2028832891e+01 w_bot
8.1327328347e-01 t_top
4.4118279322e-01 tw
7.9817164146e-01 t_bot
(./SBOdrive /tmp/fileM3fphL /tmp/file6rMWrN)
Active response data for function evaluation 231:
Active set vector = { 1 1 1 }
1.9034000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 233
------------------------------
Parameters for function evaluation 233:
1.2000000000e+01 w_top
5.6048633687e+01 hw
1.2000000000e+01 w_bot
8.1091796875e-01 t_top
4.4460449219e-01 tw
8.0297654949e-01 t_bot
(./SBOdrive /tmp/fileUB9dCS /tmp/fileemVBCW)
Active response data for function evaluation 233:
Active set vector = { 1 1 1 }
1.9043700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio = -9.8338564179e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 9
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.6037397268e+01
1.2000000000e+01
8.1223632813e-01
4.3900146484e-01
7.9902147137e-01
Current Trust Region Upper Bounds
1.2014062500e+01
5.6142866018e+01
1.2033071784e+01
8.1487304688e-01
4.4273681641e-01
8.0165819012e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution
------------------------------
Begin Function Evaluation 235
------------------------------
Parameters for function evaluation 235:
1.2001882076e+01 w_top
5.6118787660e+01 hw
1.2017088071e+01 w_bot
8.1447356692e-01 t_top
4.4067093860e-01 tw
8.0025314918e-01 t_bot
(./SBOdrive /tmp/fileWudWB8 /tmp/fileKomvvb)
Active response data for function evaluation 235:
Active set vector = { 1 1 1 }
2.0294000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 237
------------------------------
Parameters for function evaluation 237:
1.2000857445e+01 w_top
5.6094198162e+01 hw
1.2028219705e+01 w_bot
8.1389422802e-01 t_top
4.3997849629e-01 tw
7.9916098828e-01 t_bot
(./SBOdrive /tmp/fileQzRkKt /tmp/fileu2uB7z)
Active response data for function evaluation 237:
Active set vector = { 1 1 1 }
2.1095600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 239
------------------------------
Parameters for function evaluation 239:
1.2002290989e+01 w_top
5.6072252428e+01 hw
1.2010336287e+01 w_bot
8.1356277995e-01 t_top
4.4259678479e-01 tw
8.0047603934e-01 t_bot
(./SBOdrive /tmp/fileqwOukT /tmp/fileWwPlzY)
Active response data for function evaluation 239:
Active set vector = { 1 1 1 }
1.9038800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 241
------------------------------
Parameters for function evaluation 241:
1.2008060134e+01 w_top
5.6042157513e+01 hw
1.2000726356e+01 w_bot
8.1285383104e-01 t_top
4.4051220966e-01 tw
7.9964715532e-01 t_bot
(./SBOdrive /tmp/fileicE7Fi /tmp/fileicP5jr)
Active response data for function evaluation 241:
Active set vector = { 1 1 1 }
2.0220700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 243
------------------------------
Parameters for function evaluation 243:
1.2009276677e+01 w_top
5.6078216467e+01 hw
1.2024468711e+01 w_bot
8.1332713077e-01 t_top
4.4147638486e-01 tw
7.9953177125e-01 t_bot
(./SBOdrive /tmp/file8uOEkR /tmp/fileUTxcQY)
Active response data for function evaluation 243:
Active set vector = { 1 1 1 }
1.9035200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 245
------------------------------
Parameters for function evaluation 245:
1.2013416299e+01 w_top
5.6037777168e+01 hw
1.2012031574e+01 w_bot
8.1454144318e-01 t_top
4.3944157451e-01 tw
7.9904346162e-01 t_bot
(./SBOdrive /tmp/fileGCTl1p /tmp/fileCMqaYA)
Active response data for function evaluation 245:
Active set vector = { 1 1 1 }
2.1591100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 247
------------------------------
Parameters for function evaluation 247:
1.2011228487e+01 w_top
5.6106326401e+01 hw
1.2022185164e+01 w_bot
8.1275334980e-01 t_top
4.4167563118e-01 tw
8.0001104252e-01 t_bot
(./SBOdrive /tmp/fileqyt5Q7 /tmp/fileCEoTDh)
Active response data for function evaluation 247:
Active set vector = { 1 1 1 }
1.9034700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 249
------------------------------
Parameters for function evaluation 249:
1.2004852333e+01 w_top
5.6046680381e+01 hw
1.2020875992e+01 w_bot
8.1318905617e-01 t_top
4.4125839655e-01 tw
8.0127459871e-01 t_bot
(./SBOdrive /tmp/fileUDaWCP /tmp/fileeWl8U2)
Active response data for function evaluation 249:
Active set vector = { 1 1 1 }
1.9035000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 251
------------------------------
Parameters for function evaluation 251:
1.2004236235e+01 w_top
5.6104933461e+01 hw
1.2006606541e+01 w_bot
8.1232179085e-01 t_top
4.4159231072e-01 tw
7.9927050654e-01 t_bot
(./SBOdrive /tmp/fileMzUXQG /tmp/fileMDlR0S)
Active response data for function evaluation 251:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 253
------------------------------
Parameters for function evaluation 253:
1.2003710066e+01 w_top
5.6056171776e+01 hw
1.2032271811e+01 w_bot
8.1373806658e-01 t_top
4.3932774379e-01 tw
8.0016671243e-01 t_bot
(./SBOdrive /tmp/fileCKw9Lx /tmp/file63f6iN)
Active response data for function evaluation 253:
Active set vector = { 1 1 1 }
2.1802800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 255
------------------------------
Parameters for function evaluation 255:
1.2003435360e+01 w_top
5.6062522883e+01 hw
1.2008041691e+01 w_bot
8.1383702879e-01 t_top
4.4031092832e-01 tw
7.9979120477e-01 t_bot
(./SBOdrive /tmp/fileK4gz0x /tmp/fileMH1frM)
Active response data for function evaluation 255:
Active set vector = { 1 1 1 }
2.0555800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 257
------------------------------
Parameters for function evaluation 257:
1.2010484000e+01 w_top
5.6125570109e+01 hw
1.2004972367e+01 w_bot
8.1396277223e-01 t_top
4.4226252211e-01 tw
7.9987160819e-01 t_bot
(./SBOdrive /tmp/fileohN52x /tmp/fileyQ9HSP)
Active response data for function evaluation 257:
Active set vector = { 1 1 1 }
1.9037100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 259
------------------------------
Parameters for function evaluation 259:
1.2005596799e+01 w_top
5.6137619532e+01 hw
1.2010986779e+01 w_bot
8.1419012251e-01 t_top
4.4099168920e-01 tw
8.0145288612e-01 t_bot
(./SBOdrive /tmp/filecjpZrH /tmp/fileCwYn9X)
Active response data for function evaluation 259:
Active set vector = { 1 1 1 }
1.9032500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 261
------------------------------
Parameters for function evaluation 261:
1.2006777534e+01 w_top
5.6092036993e+01 hw
1.2001758536e+01 w_bot
8.1426523082e-01 t_top
4.3922894892e-01 tw
8.0074841628e-01 t_bot
(./SBOdrive /tmp/file6DNXwQ /tmp/fileWGSUCa)
Active response data for function evaluation 261:
Active set vector = { 1 1 1 }
2.2052100000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.56037E+02 0.12000E+02 0.81224E+00 0.43900E+00 0.79902E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12014E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.44274E+00 0.80166E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.190875E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81355E+00 0.44087E+00 0.80034E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 2.8762557491e+06 4.7174950008e+04 -4.2418143609e+04 7.9797936737e+06
2.9394703276e+06 -1.2334574254e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = 0.16903E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81270E+00 0.44055E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 5.5731561295e+06 -1.9023965511e+05 6.1420622460e+05 -4.8966649530e+06
8.1712411332e+05 -1.8902619718e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = 0.16732E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81329E+00 0.44046E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 5.3860348209e+06 -1.5158153770e+05 6.3544760705e+04 1.0705982354e+06
4.8769864849e+06 -1.9743383159e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = 0.16460E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81306E+00 0.43937E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6452411922e+06 -3.0180355249e+05 4.5053124294e+05 -9.1149729655e+06
2.1387576587e+06 -2.2940609516e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = 0.15830E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81418E+00 0.43911E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.4378381936e+06 -2.3768699778e+05 -5.7870113657e+05 1.5847683700e+06
9.7463529181e+06 -2.4787280195e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = 0.15722E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81413E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6844972952e+06 -2.5536944866e+05 -5.1156163497e+05 2.1689965027e+05
9.2665509191e+06 -2.5088356925e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 6 OBJ = 0.15717E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81405E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.7290843479e+06 -2.6228808370e+05 -4.3434931502e+05 -7.6515538715e+05
8.7324784001e+06 -2.5056046985e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 7 OBJ = 0.15705E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81419E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6424370209e+06 -2.5124718594e+05 -5.5747017220e+05 6.4337407420e+05
9.6556636878e+06 -2.5192427170e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 8 OBJ = 0.15702E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81412E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6812800487e+06 -2.5669849187e+05 -4.9661414467e+05 -9.3298211804e+04
9.2176408414e+06 -2.5146730928e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 9 OBJ = 0.15657E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12013E+02 0.81424E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.5945767109e+06 -2.4880861445e+05 -5.8401925942e+05 6.5036762194e+05
9.9884392140e+06 -2.5380691656e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 10 OBJ = 0.15655E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12013E+02 0.81417E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6402464120e+06 -2.5522013219e+05 -5.1234090188e+05 -2.1842834007e+05
9.4727663349e+06 -2.5327230337e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 11 OBJ = 0.15622E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12014E+02 0.81456E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.3773620121e+06 -2.2539192081e+05 -8.4259262880e+05 3.2449217613e+06
1.2086575667e+07 -2.5857594368e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 12 OBJ = 0.15582E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12014E+02 0.81429E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.5442120512e+06 -2.4785749139e+05 -5.9174605156e+05 2.7635413712e+05
1.0249923500e+07 -2.5632379446e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 13 OBJ = 0.15576E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12014E+02 0.81420E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.5998703853e+06 -2.5626917290e+05 -4.9698721754e+05 -9.2195090433e+05
9.5890602516e+06 -2.5586903665e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 14 OBJ = 0.15562E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12014E+02 0.81438E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.4823141313e+06 -2.4120306656e+05 -6.6447470011e+05 9.9581003965e+05
1.0842822191e+07 -2.5770301910e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 15 OBJ = 0.15557E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56091E+02 0.12014E+02 0.81430E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.5363676659e+06 -2.4866312650e+05 -5.8097590212e+05 -7.9269931913e+03
1.0238058701e+07 -2.5703290738e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 16 OBJ = 0.15468E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12015E+02 0.81431E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.5045487936e+06 -2.5102199109e+05 -5.4759417684e+05 -9.6937749379e+05
1.0233976468e+07 -2.5962861473e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 17 OBJ = 0.15452E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12016E+02 0.81450E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.3865049060e+06 -2.3588282065e+05 -7.1555345806e+05 9.5121176078e+05
1.1491568298e+07 -2.6147362122e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 18 OBJ = 0.15447E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12016E+02 0.81439E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.4510260216e+06 -2.4481341421e+05 -6.1536621923e+05 -2.5719396125e+05
1.0767199589e+07 -2.6068612614e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 19 OBJ = 0.15407E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12016E+02 0.81466E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.2696007299e+06 -2.2389746815e+05 -8.4324903871e+05 2.1215219115e+06
1.2566319084e+07 -2.6431802012e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 20 OBJ = 0.15389E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12016E+02 0.81449E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.3788253355e+06 -2.3870287300e+05 -6.7757618981e+05 1.5005081019e+05
1.1357332330e+07 -2.6288086872e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 21 OBJ = 0.15363E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56092E+02 0.12017E+02 0.81433E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.4699347766e+06 -2.5422252372e+05 -4.9776588260e+05 -2.2847183925e+06
1.0164071200e+07 -2.6277145158e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 22 OBJ = 0.15325E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56093E+02 0.12017E+02 0.81462E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.2836152919e+06 -2.2981408212e+05 -7.6915034549e+05 8.6484001241e+05
1.2176446593e+07 -2.6551618637e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 23 OBJ = 0.15320E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56093E+02 0.12017E+02 0.81452E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.3425410223e+06 -2.3801362247e+05 -6.7687231896e+05 -2.5408397012e+05
1.1511216356e+07 -2.6481598423e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 24 OBJ = 0.15129E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56093E+02 0.12019E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.0836357586e+06 -2.1402395789e+05 -9.2145418478e+05 1.6003385638e+06
1.3716782059e+07 -2.7208464251e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 25 OBJ = 0.15117E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56093E+02 0.12019E+02 0.81472E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.1802300914e+06 -2.2721312318e+05 -7.7329359270e+05 -1.7536431865e+05
1.2639882625e+07 -2.7085415885e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 26 OBJ = 0.14531E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56095E+02 0.12026E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 6.9796931092e+06 -2.3013377636e+05 -6.4986949471e+05 -4.7558609901e+06
1.2926785309e+07 -2.8446990366e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 27 OBJ = 0.13538E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56118E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 6.6780121311e+06 -3.5057973184e+05 -3.9897317820e+05 -9.2860034320e+06
1.4603317700e+07 -3.1640943297e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 28 OBJ = 0.12535E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 6.4343458517e+06 -4.7855928296e+05 -4.2016188017e+05 -7.1473170682e+06
1.7610937511e+07 -3.4053759897e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 29 OBJ = 0.12535E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
ITER = 30 OBJ = 0.12535E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 6.4343458517e+06 -4.7855928296e+05 -4.2016188017e+05 -7.1473170682e+06
1.7610937511e+07 -3.4053759897e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 31 OBJ = 0.12535E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = 0.125352E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56143E+02 0.12033E+02 0.81487E+00 0.43900E+00 0.80166E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1 2 3 4 -5 6
TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR 3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1)) LESS THAN DABFUN FOR 3 ITERATIONS
NUMBER OF ITERATIONS = 31
OBJECTIVE FUNCTION WAS EVALUATED 92 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 92 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 30 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 30 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 263
------------------------------
Parameters for function evaluation 263:
1.2000920001e+01 w_top
5.6097225333e+01 hw
1.2014247342e+01 w_bot
8.1355417613e-01 t_top
4.4104661574e-01 tw
8.0083359682e-01 t_bot
(./SBOdrive /tmp/fileawuWyg /tmp/fileYRFVAD)
Active response data for function evaluation 263:
Active set vector = { 1 1 1 }
1.9032900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 265
------------------------------
Parameters for function evaluation 265:
1.2001210271e+01 w_top
5.6105828360e+01 hw
1.2007433771e+01 w_bot
8.1303610559e-01 t_top
4.4031155862e-01 tw
8.0064968883e-01 t_bot
(./SBOdrive /tmp/filea2aC0F /tmp/fileo7G1S1)
Active response data for function evaluation 265:
Active set vector = { 1 1 1 }
2.0704900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 267
------------------------------
Parameters for function evaluation 267:
1.2003458674e+01 w_top
5.6079810132e+01 hw
1.2013208768e+01 w_bot
8.1379867233e-01 t_top
4.4064834424e-01 tw
8.0072478929e-01 t_bot
(./SBOdrive /tmp/fileCIhSNe /tmp/fileCEOK4D)
Active response data for function evaluation 267:
Active set vector = { 1 1 1 }
2.0185300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 269
------------------------------
Parameters for function evaluation 269:
1.2001927938e+01 w_top
5.6084344913e+01 hw
1.2018379522e+01 w_bot
8.1369979905e-01 t_top
4.4127188439e-01 tw
7.9971619220e-01 t_bot
(./SBOdrive /tmp/filesnfrnN /tmp/fileszM3vb)
Active response data for function evaluation 269:
Active set vector = { 1 1 1 }
1.9034200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 271
------------------------------
Parameters for function evaluation 271:
1.2005636392e+01 w_top
5.6115313920e+01 hw
1.2013615855e+01 w_bot
8.1330118223e-01 t_top
4.4109112844e-01 tw
8.0040167211e-01 t_bot
(./SBOdrive /tmp/fileUvQTev /tmp/file6ND0MW)
Active response data for function evaluation 271:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 273
------------------------------
Parameters for function evaluation 273:
1.2000370010e+01 w_top
5.6110234603e+01 hw
1.2019195969e+01 w_bot
8.1291209361e-01 t_top
4.4004318077e-01 tw
8.0047334566e-01 t_bot
(./SBOdrive /tmp/file8eoJOc /tmp/file6rgyeD)
Active response data for function evaluation 273:
Active set vector = { 1 1 1 }
2.1066500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 275
------------------------------
Parameters for function evaluation 275:
1.2006276405e+01 w_top
5.6072739036e+01 hw
1.2005195498e+01 w_bot
8.1296789777e-01 t_top
4.4174085609e-01 tw
8.0086143110e-01 t_bot
(./SBOdrive /tmp/fileOoKlM3 /tmp/fileeAyyBx)
Active response data for function evaluation 275:
Active set vector = { 1 1 1 }
1.9035700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 277
------------------------------
Parameters for function evaluation 277:
1.2002420937e+01 w_top
5.6091451936e+01 hw
1.2002480259e+01 w_bot
8.1314026623e-01 t_top
4.4073728298e-01 tw
8.0061594506e-01 t_bot
(./SBOdrive /tmp/fileymZxaZ /tmp/fileCG9KQr)
Active response data for function evaluation 277:
Active set vector = { 1 1 1 }
2.0105900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 279
------------------------------
Parameters for function evaluation 279:
1.2001506410e+01 w_top
5.6112201961e+01 hw
1.2015589241e+01 w_bot
8.1336658906e-01 t_top
4.4014831607e-01 tw
8.0092421985e-01 t_bot
(./SBOdrive /tmp/fileUnUbEU /tmp/fileSgeyRq)
Active response data for function evaluation 279:
Active set vector = { 1 1 1 }
2.0941000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 281
------------------------------
Parameters for function evaluation 281:
1.2001632738e+01 w_top
5.6078004682e+01 hw
1.2005848219e+01 w_bot
8.1347385269e-01 t_top
4.4012587688e-01 tw
7.9980751144e-01 t_bot
(./SBOdrive /tmp/file0E9n9Y /tmp/file0iFy5t)
Active response data for function evaluation 281:
Active set vector = { 1 1 1 }
2.0845700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 283
------------------------------
Parameters for function evaluation 283:
1.2002891889e+01 w_top
5.6082185628e+01 hw
1.2012367742e+01 w_bot
8.1325237904e-01 t_top
4.4087452401e-01 tw
8.0096701120e-01 t_bot
(./SBOdrive /tmp/filesXTuE3 /tmp/fileCGXW8B)
Active response data for function evaluation 283:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 285
------------------------------
Parameters for function evaluation 285:
1.2006436298e+01 w_top
5.6089545693e+01 hw
1.2016524287e+01 w_bot
8.1419949999e-01 t_top
4.4116315682e-01 tw
7.9992662168e-01 t_bot
(./SBOdrive /tmp/filec9BQdh /tmp/fileaA7hpO)
Active response data for function evaluation 285:
Active set vector = { 1 1 1 }
1.9034400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 287
------------------------------
Parameters for function evaluation 287:
1.2003804608e+01 w_top
5.6093605781e+01 hw
1.2017677504e+01 w_bot
8.1361662662e-01 t_top
4.4084561858e-01 tw
8.0036871675e-01 t_bot
(./SBOdrive /tmp/fileIJ0XKu /tmp/fileKkABw5)
Active response data for function evaluation 287:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 289
------------------------------
Parameters for function evaluation 289:
1.2004018178e+01 w_top
5.6108566164e+01 hw
1.2003425666e+01 w_bot
8.1395889646e-01 t_top
4.4155420002e-01 tw
8.0050695156e-01 t_bot
(./SBOdrive /tmp/filegFdeGR /tmp/fileiiW98q)
Active response data for function evaluation 289:
Active set vector = { 1 1 1 }
1.9034800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 291
------------------------------
Parameters for function evaluation 291:
1.2000000000e+01 w_top
5.6085747754e+01 hw
1.2001431159e+01 w_bot
8.1421386719e-01 t_top
4.4120965921e-01 tw
7.9968065105e-01 t_bot
(./SBOdrive /tmp/fileQHPd1g /tmp/fileqcLmKW)
Active response data for function evaluation 291:
Active set vector = { 1 1 1 }
1.9034200000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio = -3.7936707737e-04:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
*********************************************
Begin SBO Iteration Number 11
Current Trust Region Lower Bounds (truncated)
1.2000000000e+01
5.6076948050e+01
1.2006704597e+01
8.1322509766e-01
4.4040222168e-01
8.0001024090e-01
Current Trust Region Upper Bounds
1.2003515625e+01
5.6103315237e+01
1.2017251472e+01
8.1388427734e-01
4.4133605957e-01
8.0066942058e-01
*********************************************
<<<<< Building global approximation.
DACE method = lhs Samples = 28 Symbols = 28 Seed not reset from previous DACE execution
------------------------------
Begin Function Evaluation 293
------------------------------
Parameters for function evaluation 293:
1.2002153696e+01 w_top
5.6096778874e+01 hw
1.2011096949e+01 w_bot
8.1361359928e-01 t_top
4.4066527881e-01 tw
8.0041113366e-01 t_bot
(./SBOdrive /tmp/fileQf5ccT /tmp/fileeQiUJx)
Active response data for function evaluation 293:
Active set vector = { 1 1 1 }
2.0220300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 295
------------------------------
Parameters for function evaluation 295:
1.2000844132e+01 w_top
5.6080109895e+01 hw
1.2013200729e+01 w_bot
8.1371823835e-01 t_top
4.4070429097e-01 tw
8.0011928969e-01 t_bot
(./SBOdrive /tmp/file000RME /tmp/file4cNIQm)
Active response data for function evaluation 295:
Active set vector = { 1 1 1 }
2.0111300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2------------------------------
Begin Function Evaluation 297
------------------------------
Parameters for function evaluation 297:
1.2003251160e+01 w_top
5.6084150458e+01 hw
1.2015976239e+01 w_bot
8.1386412435e-01 t_top
4.4123640288e-01 tw
8.0038654071e-01 t_bot
(./SBOdrive /tmp/fileIHt72p /tmp/fileItzuP6)
Active response data for function evaluation 297:
Active set vector = { 1 1 1 }
1.9034300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 299
------------------------------
Parameters for function evaluation 299:
1.2000265996e+01 w_top
5.6092895759e+01 hw
1.2017001020e+01 w_bot
8.1345551207e-01 t_top
4.4061926688e-01 tw
8.0049187437e-01 t_bot
(./SBOdrive /tmp/filek3UmDk /tmp/filewxsIY4)
Active response data for function evaluation 299:
Active set vector = { 1 1 1 }
2.0266400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 301
------------------------------
Parameters for function evaluation 301:
1.2001947441e+01 w_top
5.6081315252e+01 hw
1.2008649901e+01 w_bot
8.1351429127e-01 t_top
4.4108530161e-01 tw
8.0056996865e-01 t_bot
(./SBOdrive /tmp/filemCymef /tmp/file6vuuhY)
Active response data for function evaluation 301:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 303
------------------------------
Parameters for function evaluation 303:
1.2003475774e+01 w_top
5.6096042502e+01 hw
1.2008455917e+01 w_bot
8.1343328348e-01 t_top
4.4096657803e-01 tw
8.0008244353e-01 t_bot
(./SBOdrive /tmp/fileyKjEce /tmp/fileaGE3M0)
Active response data for function evaluation 303:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 305
------------------------------
Parameters for function evaluation 305:
1.2000930707e+01 w_top
5.6085937908e+01 hw
1.2015334987e+01 w_bot
8.1380588054e-01 t_top
4.4089380790e-01 tw
8.0021503793e-01 t_bot
(./SBOdrive /tmp/file8OeD6h /tmp/fileOMD2v3)
Active response data for function evaluation 305:
Active set vector = { 1 1 1 }
1.9032900000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 307
------------------------------
Parameters for function evaluation 307:
1.2002482850e+01 w_top
5.6099258022e+01 hw
1.2014547133e+01 w_bot
8.1374370904e-01 t_top
4.4051430566e-01 tw
8.0014792965e-01 t_bot
(./SBOdrive /tmp/fileAf9Mpq /tmp/fileKyvBlf)
Active response data for function evaluation 307:
Active set vector = { 1 1 1 }
2.0424000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 309
------------------------------
Parameters for function evaluation 309:
1.2001635481e+01 w_top
5.6097909887e+01 hw
1.2006877365e+01 w_bot
8.1340542223e-01 t_top
4.4097677248e-01 tw
8.0001438087e-01 t_bot
(./SBOdrive /tmp/fileodHbdI /tmp/files9KR0v)
Active response data for function evaluation 309:
Active set vector = { 1 1 1 }
1.9032500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 311
------------------------------
Parameters for function evaluation 311:
1.2000210535e+01 w_top
5.6094655930e+01 hw
1.2016126153e+01 w_bot
8.1349364931e-01 t_top
4.4101217123e-01 tw
8.0031074026e-01 t_bot
(./SBOdrive /tmp/file2pjnJZ /tmp/fileacrYVQ)
Active response data for function evaluation 311:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 313
------------------------------
Parameters for function evaluation 313:
1.2001820747e+01 w_top
5.6090046136e+01 hw
1.2007412454e+01 w_bot
8.1365112051e-01 t_top
4.4055053975e-01 tw
8.0023028766e-01 t_bot
(./SBOdrive /tmp/fileQJc9zq /tmp/fileMV62Dg)
Active response data for function evaluation 313:
Active set vector = { 1 1 1 }
2.0343400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 315
------------------------------
Parameters for function evaluation 315:
1.2001403875e+01 w_top
5.6089165240e+01 hw
1.2015475852e+01 w_bot
8.1372657715e-01 t_top
4.4040972153e-01 tw
8.0004348096e-01 t_bot
(./SBOdrive /tmp/fileOqrPER /tmp/file0OpucL)
Active response data for function evaluation 315:
Active set vector = { 1 1 1 }
2.0523000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 317
------------------------------
Parameters for function evaluation 317:
1.2002778041e+01 w_top
5.6100996443e+01 hw
1.2009182623e+01 w_bot
8.1327323693e-01 t_top
4.4129675027e-01 tw
8.0033456425e-01 t_bot
(./SBOdrive /tmp/file6LZNTr /tmp/fileqmG1gk)
Active response data for function evaluation 317:
Active set vector = { 1 1 1 }
1.9033500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 319
------------------------------
Parameters for function evaluation 319:
1.2002613191e+01 w_top
5.6085110376e+01 hw
1.2008039575e+01 w_bot
8.1346875804e-01 t_top
4.4074454807e-01 tw
8.0016155207e-01 t_bot
(./SBOdrive /tmp/fileklgjo2 /tmp/fileYlhVoY)
Active response data for function evaluation 319:
Active set vector = { 1 1 1 }
2.0075400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
Building global approximation(s) with 28 new samples and 0 database samples.
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
building quadratic polynomial approximation using 28 points
quadratic polynomial build completed
<<<<< Global approximation build completed.
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
Adding a point and recalculating quadratic polynomial approximation
quadratic polynomial add and rebuild completed
<<<<< Evaluating approximation at trust region center.
<<<<< Starting approximate optimization cycle.
1
* * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* C O N M I N *
* *
* FORTRAN PROGRAM FOR *
* *
* CONSTRAINED FUNCTION MINIMIZATION *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * *
CONSTRAINED FUNCTION MINIMIZATION
CONTROL PARAMETERS
IPRINT NDV ITMAX NCON NSIDE ICNDIR NSCAL NFDG
2 6 50 2 1 7 0 1
LINOBJ ITRM N1 N2 N3 N4 N5
0 3 8 14 9 9 18
CT CTMIN CTL CTLMIN
-0.10000E+00 0.10000E-02 -0.10000E-01 0.10000E-02
THETA PHI DELFUN DABFUN
0.10000E+01 0.50000E+01 0.10000E-03 0.10000E-03
FDCH FDCHM ALPHAX ABOBJ1
0.10000E-04 0.10000E-04 0.10000E+00 0.10000E+00
LOWER BOUNDS ON DECISION VARIABLES (VLB)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81323E+00 0.44040E+00 0.80001E+00
UPPER BOUNDS ON DECISION VARIABLES (VUB)
1) 0.12004E+02 0.56103E+02 0.12017E+02 0.81388E+00 0.44134E+00 0.80067E+00
ALL CONSTRAINTS ARE NON-LINEAR
INITIAL FUNCTION INFORMATION
OBJ = 0.188945E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81355E+00 0.44087E+00 0.80034E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ -2.0966195679e+06 3.8532102852e+06 3.7413144638e+06 -1.2726419900e+08
-1.9867839393e+08 -5.4596188786e+07 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 1 OBJ = -0.60250E+05
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81385E+00 0.44134E+00 0.80047E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.0494451747e+07 1.1629886528e+07 1.0676594526e+07 -2.7036649665e+08
-5.2156438558e+08 -2.6029931795e+08 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 2 OBJ = -0.12163E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56090E+02 0.12012E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 7.6815876967e+07 1.2332803161e+07 8.1895213922e+06 -3.2984580011e+08
-5.8726971207e+08 -2.4061790074e+08 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 3 OBJ = -0.27202E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56082E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.1280860714e+08 1.2219433938e+07 1.4088058756e+07 -5.6650879914e+08
-6.6603083974e+08 -1.6721060365e+08 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 4 OBJ = -0.32745E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.2271547238e+08 1.0613201556e+07 1.6393696624e+07 -6.8293195440e+08
-6.7880174219e+08 -1.6787793464e+08 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 5 OBJ = -0.32745E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
ITER = 6 OBJ = -0.32745E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response
augmented with data requirements for differencing:
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
>>>>> Dakota finite difference gradient evaluation for x[3] + h:
>>>>> Dakota finite difference gradient evaluation for x[4] + h:
>>>>> Dakota finite difference gradient evaluation for x[5] + h:
>>>>> Dakota finite difference gradient evaluation for x[6] + h:
>>>>> Total response returned to iterator:
Active set vector = { 2 2 2 }
[ 1.2271547238e+08 1.0613201556e+07 1.6393696624e+07 -6.8293195440e+08
-6.7880174219e+08 -1.6787793464e+08 ] obj_fn gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con1 gradient
[ 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
0.0000000000e+00 0.0000000000e+00 ] nln_ineq_con2 gradient
** CONSTRAINT 1 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
** CONSTRAINT 2 HAS ZERO GRADIENT
DELETED FROM ACTIVE SET
ITER = 7 OBJ = -0.32745E+06 NO CHANGE IN OBJ
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
1
FINAL OPTIMIZATION INFORMATION
OBJ = -0.327447E+06
DECISION VARIABLES (X-VECTOR)
1) 0.12000E+02 0.56077E+02 0.12007E+02 0.81388E+00 0.44134E+00 0.80067E+00
CONSTRAINT VALUES (G-VECTOR)
1) 0.00000E+00 0.00000E+00
THERE ARE 2 ACTIVE CONSTRAINTS
CONSTRAINT NUMBERS ARE
1 2
THERE ARE 0 VIOLATED CONSTRAINTS
THERE ARE 6 ACTIVE SIDE CONSTRAINTS
DECISION VARIABLES AT LOWER OR UPPER BOUNDS (MINUS INDICATES LOWER BOUND)
-1 -2 -3 4 5 6
TERMINATION CRITERION
ABS(1-OBJ(I-1)/OBJ(I)) LESS THAN DELFUN FOR 3 ITERATIONS
ABS(OBJ(I)-OBJ(I-1)) LESS THAN DABFUN FOR 3 ITERATIONS
NUMBER OF ITERATIONS = 7
OBJECTIVE FUNCTION WAS EVALUATED 14 TIMES
CONSTRAINT FUNCTIONS WERE EVALUATED 14 TIMES
GRADIENT OF OBJECTIVE WAS CALCULATED 6 TIMES
GRADIENTS OF CONSTRAINTS WERE CALCULATED 6 TIMES
<<<<< Approximate optimization cycle completed.
<<<<< Evaluating approximate solution with actual model.
------------------------------
Begin Function Evaluation 321
------------------------------
Parameters for function evaluation 321:
1.2000156231e+01 w_top
5.6089959355e+01 hw
1.2012325375e+01 w_bot
8.1341297345e-01 t_top
4.4078547744e-01 tw
8.0047868271e-01 t_bot
(./SBOdrive /tmp/fileGGZDdJ /tmp/file2PPxCE)
Active response data for function evaluation 321:
Active set vector = { 1 1 1 }
2.0040600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 323
------------------------------
Parameters for function evaluation 323:
1.2000363466e+01 w_top
5.6087724241e+01 hw
1.2014131339e+01 w_bot
8.1357127876e-01 t_top
4.4093474775e-01 tw
8.0032033932e-01 t_bot
(./SBOdrive /tmp/fileCKm6Zx /tmp/fileGactFu)
Active response data for function evaluation 323:
Active set vector = { 1 1 1 }
1.9032700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 325
------------------------------
Parameters for function evaluation 325:
1.2001163695e+01 w_top
5.6083931664e+01 hw
1.2013676145e+01 w_bot
8.1341593606e-01 t_top
4.4095370718e-01 tw
8.0025541743e-01 t_bot
(./SBOdrive /tmp/fileAp4rEm /tmp/fileOQ36nk)
Active response data for function evaluation 325:
Active set vector = { 1 1 1 }
1.9032800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 327
------------------------------
Parameters for function evaluation 327:
1.2001324660e+01 w_top
5.6085609751e+01 hw
1.2013998163e+01 w_bot
8.1352011340e-01 t_top
4.4106851375e-01 tw
8.0021648679e-01 t_bot
(./SBOdrive /tmp/fileqibW2m /tmp/fileoFCGko)
Active response data for function evaluation 327:
Active set vector = { 1 1 1 }
1.9033300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 329
------------------------------
Parameters for function evaluation 329:
1.2001413444e+01 w_top
5.6095066558e+01 hw
1.2011473958e+01 w_bot
8.1365101618e-01 t_top
4.4098442572e-01 tw
8.0023824502e-01 t_bot
(./SBOdrive /tmp/file4YuU6m /tmp/fileoR0r5m)
Active response data for function evaluation 329:
Active set vector = { 1 1 1 }
1.9032800000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 331
------------------------------
Parameters for function evaluation 331:
1.2000787167e+01 w_top
5.6091272482e+01 hw
1.2010362966e+01 w_bot
8.1368530117e-01 t_top
4.4088419036e-01 tw
8.0020954385e-01 t_bot
(./SBOdrive /tmp/file4KfKsw /tmp/filemitrYz)
Active response data for function evaluation 331:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 333
------------------------------
Parameters for function evaluation 333:
1.2000980300e+01 w_top
5.6094507566e+01 hw
1.2014559241e+01 w_bot
8.1370001433e-01 t_top
4.4109765058e-01 tw
8.0023120971e-01 t_bot
(./SBOdrive /tmp/filem6VYTF /tmp/fileGsXyhI)
Active response data for function evaluation 333:
Active set vector = { 1 1 1 }
1.9033300000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 335
------------------------------
Parameters for function evaluation 335:
1.2001471240e+01 w_top
5.6085183322e+01 hw
1.2014334691e+01 w_bot
8.1346097257e-01 t_top
4.4083591913e-01 tw
8.0027420670e-01 t_bot
(./SBOdrive /tmp/filewlJDqY /tmp/fileUXbsc4)
Active response data for function evaluation 335:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 337
------------------------------
Parameters for function evaluation 337:
1.2000528901e+01 w_top
5.6089463835e+01 hw
1.2012068068e+01 w_bot
8.1347438047e-01 t_top
4.4072021137e-01 tw
8.0026552994e-01 t_bot
(./SBOdrive /tmp/filei0XYSg /tmp/file8wekwl)
Active response data for function evaluation 337:
Active set vector = { 1 1 1 }
2.0122700000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 339
------------------------------
Parameters for function evaluation 339:
1.2000880636e+01 w_top
5.6090239452e+01 hw
1.2012527289e+01 w_bot
8.1355100995e-01 t_top
4.4075301302e-01 tw
8.0041364331e-01 t_bot
(./SBOdrive /tmp/fileUxDAQI /tmp/file4cxA0Q)
Active response data for function evaluation 339:
Active set vector = { 1 1 1 }
2.0084000000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 341
------------------------------
Parameters for function evaluation 341:
1.2000625203e+01 w_top
5.6095336986e+01 hw
1.2012802966e+01 w_bot
8.1354262559e-01 t_top
4.4073872874e-01 tw
8.0018286887e-01 t_bot
(./SBOdrive /tmp/fileQTZpra /tmp/filesjW2ih)
Active response data for function evaluation 341:
Active set vector = { 1 1 1 }
2.0119500000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 343
------------------------------
Parameters for function evaluation 343:
1.2001673810e+01 w_top
5.6084026752e+01 hw
1.2009866637e+01 w_bot
8.1345318277e-01 t_top
4.4065831695e-01 tw
8.0038955375e-01 t_bot
(./SBOdrive /tmp/filea4PClL /tmp/fileaYl7LV)
Active response data for function evaluation 343:
Active set vector = { 1 1 1 }
2.0183600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 345
------------------------------
Parameters for function evaluation 345:
1.2000841041e+01 w_top
5.6090754721e+01 hw
1.2010493986e+01 w_bot
8.1362875983e-01 t_top
4.4088753376e-01 tw
8.0050444884e-01 t_bot
(./SBOdrive /tmp/filePPM3A0 /tmp/fileYfnxB2)
Active response data for function evaluation 345:
Active set vector = { 1 1 1 }
1.9032600000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 347
------------------------------
Parameters for function evaluation 347:
1.2001217607e+01 w_top
5.6088267432e+01 hw
1.2009606862e+01 w_bot
8.1349336557e-01 t_top
4.4083141661e-01 tw
8.0044035551e-01 t_bot
(./SBOdrive /tmp/filejRuf13 /tmp/filea6BqT4)
Active response data for function evaluation 347:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
------------------------------
Begin Function Evaluation 349
------------------------------
Parameters for function evaluation 349:
1.2000011138e+01 w_top
5.6090137954e+01 hw
1.2011943368e+01 w_bot
8.1371948242e-01 t_top
4.4081897525e-01 tw
8.0050462566e-01 t_bot
(./SBOdrive /tmp/fileblrYUe /tmp/fileeKW6lh)
Active response data for function evaluation 349:
Active set vector = { 1 1 1 }
1.9032400000e+05 obj_fn
0.0000000000e+00 nln_ineq_con1
0.0000000000e+00 nln_ineq_con2
<<<<< Trust Region Ratio Numerator = 0.0000000000e+00:
<<<<< No Progress, Reject Step, REDUCE Trust Region Size
Optimization Complete - Soft Convergence Tolerance Reached
Progress Between 5 Successive Iterations <= Convergence Tolerance
******************************************
Surrogate-Based Optimization (SBO) Results
******************************************
SBO Iterations = 12
Surrogate Model Evaluations = 2491 (2491 new, 0 duplicate)
Truth Model Evaluations = 349 (349 new, 0 duplicate)
SBO Final Design Variables
w_top = 1.2000000000e+01
hw = 5.6090131643e+01
w_bot = 1.2011978034e+01
t_top = 8.1355468750e-01
tw = 4.4086914063e-01
t_bot = 8.0033983074e-01
SBO Final Truth Response Values
Objective Function = 1.9032400000e+05
Ineq Constraint 1 = 0.0000000000e+00
Ineq Constraint 2 = 0.0000000000e+00
*******************************************