[Coin-ipopt] No optimal variable values in IPOPT+CUTEr output
Carl Damon Laird
claird at andrew.cmu.edu
Fri Nov 11 19:40:22 EST 2005
Hello,
Judging by the path information, I will assume that you are using the
Fortran IPOPT version. I am not as familiar with the Fortran version as
the new C++ version, but I will look into the CUTEr interface.
For now, you can increase the print level for IPOPT. The option in the
Fortran version is "IPRINT". Have a look at the README.IPOPT in the doc
directory. You could also output this information to a file.
Hope this helps,
Carl.
On Fri, 11 Nov 2005, Lihong Zhang wrote:
> Hi All,
>
> I have a question on the output of IPOPT if using the CUTEr
> interface (ie, SIF as the input model file). From the output,
> I only can read the optimal objective function value, while I can't
> find the optimal variable values from the output list. As a matter
> of fact, both the optimal objective function value and the
> optimal variable values are output when I run LANCELOT
> (also using SIF as the input file). Besides, IPOPT + AMPL
> also can output both optimal values. Below I give the output
> results from IPOPT+CUTEr and LANCELOT, respectively,
> if I input the same HS65.SIF.
>
>
> Results from IPOPT+CUTEr:
> ------------------------------------------------------------------------------
> [lihong at frosty]tmp> sdipopt --blas none HS65.SIF
>
> Problem name: HS65
> Double precision version will be formed.
>
> The objective function uses 3 nonlinear groups
>
> There is 1 nonlinear inequality constraint
>
> There are 3 variables bounded from below and above
>
> ld: warning: symbol `evals_' has differing sizes:
> (file
> /homes/lihong/cvs/cuter/CUTEr.large.sun.sol.g77/double/bin/ipoptma
> .o value=0x8; file /homes/lihong/cvs/COIN/Ipopt/lib/libipopt.a(ipopt.o)
> value=0x
> c);
> /homes/lihong/cvs/COIN/Ipopt/lib/libipopt.a(ipopt.o) definition taken
> ******************************************************************************
> This program contains IPOPT, a program for large-scale nonlinear
> optimization.
> IPOPT is released as open source under the Common Public License (CPL).
> For more information visit www.coin-or.org/Ipopt
> ******************************************************************************
>
> Number of variables : 4
> of which are fixed : 0
> Number of constraints : 1
> Number of lower bounds : 4
> Number of upper bounds : 3
> Number of nonzeros in Jacobian: 4
> Number of nonzeros in Hessian : 4
>
> ITER ERR MU ||C|| ||D|| ALFA(X) #LS F Regu
> 0 .200E+02d .100E+00 .830E+01 .000E+00 .000E+00 0 0.11549921E+03
> .000E+00
> 1 .999E+01d .100E+00 .752E+01 .666E+00 .930E-01h 1 0.11693020E+03
> .100E+03
> 2 .100E+02d .100E+00 .746E+01 .256E+01 .759E-02h 1 0.11698136E+03
> .333E+02
> 3 .208E+03d .100E+00 .506E+01 .778E+01 .100E+01f 1 0.99509892E+02
> .111E+02
> 4 .733E+02d .100E+00 .202E+01 .196E+01 .100E+01h 1 0.10934095E+03
> .296E+02
> 5 .743E+02d .100E+00 .132E+01 .115E+01 .100E+01h 1 0.12019884E+03
> .790E+02
> 6 .202E+02d .100E+00 .653E+00 .886E+00 .100E+01f 1 0.11130700E+03
> .000E+00
> 7 .658E+02p .100E+00 .658E+02 .563E+02 .999E+00f 1 0.57224133E+01
> .000E+00
> 8 .995E+01p .100E+00 .995E+01 .668E+02 .932E+00f 1 0.10383396E+01
> .000E+00
> 9 .154E+01p .100E+00 .154E+01 .135E+01 .100E+01h 1 0.16139967E+01
> .000E+00
>
> ITER ERR MU ||C|| ||D|| ALFA(X) #LS F Regu
> 10 .284E+00p .100E+00 .284E+00 .129E+01 .100E+01h 1 0.10375737E+01
> .000E+00
> 11 .352E-01p .200E-01 .352E-01 .132E+01 .894E+00h 1 0.95303357E+00
> .000E+00
> 12 .269E-02c .283E-02 .168E-02 .476E-01 .100E+01h 1 0.95638608E+00
> .000E+00
> 13 .612E-04c .150E-03 .203E-04 .348E-01 .100E+01h 1 0.95366453E+00
> .000E+00
> 14 .880E-07c .184E-05 .256E-07 .166E-02 .100E+01h 1 0.95353066E+00
> .000E+00
> 15 .688E-11c .251E-08 .269E-11 .221E-04 .100E+01h 1 0.95352886E+00
> .000E+00
>
> Number of iterations taken ............. 15
> Final value of objective function is.... 0.9535288576748209E+00
>
> Errors at final point (scaled) (unscaled)
> Final maximal constraint violation is... 0.268651E-11 0.268651E-11
> Final value for dual infeasibility is... 0.574804E-12 0.574804E-12
> Final value of complementarity error is. 0.251278E-08 0.251278E-08
>
> The objective function was evaluated 16 times.
> The constraints were evaluated 16 times.
>
> EXIT: OPTIMAL SOLUTION FOUND
>
> CPU seconds spent in IPOPT and function evaluations = 0.0000
>
> ************************ CUTEr statistics ************************
> Code used : IPOPT
> Problem : HS65 # variables = 3
> # constraints = 1
> # objective functions = 0.3300000E+02
> # objective gradients = 0.1700000E+02
> # objective Hessians = 0.1600000E+02
> # Hessian-vector prdct = 0.0000000E+00
> # constraints functions = 0.3400000E+02
> # constraints gradients = 0.1700000E+02
> # constraints Hessians = 0.1600000E+02
> Exit code = 0
> Final f = 0.9535289E+00
> Set up time = 0.00 seconds
> Solve time = 0.03 seconds
> ******************************************************************
>
>
> Results from LANCELOT:
> ------------------------------------------------------------------------------
> [lihong at frosty]sampleproblems> sdlan HS65
>
> Problem name: HS65
> Double precision version will be formed.
>
> The objective function uses 3 nonlinear groups
>
> There is 1 nonlinear inequality constraint
>
> There are 3 variables bounded from below and above
> There is 1 slack variable
>
> objective function value = 9.53529015445393E-01
>
> X1 3.65046164957023E+00
> X2 3.65046164897452E+00
> X3 4.62041746528368E+00
> C1 0.00000000000000E+00
>
>
>
> Form the above, we may find the output objective
> function values from both IPOPT and LANCELOT
> converge. However, I hope I can also obtain the
> optimal variable values from IPOPT. I guess it is
> not a big deal. I maybe missed some switches. Does
> anybody have ideas and give me any hints? thanks.
>
> Best regards,
>
> Lihong
>
>
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