[Coin-ipopt] No optimal variable values in IPOPT+CUTEr output
Andreas Waechter
andreasw at watson.ibm.com
Mon Nov 14 09:43:32 EST 2005
Hi Lihong,
You also need to use the option
IFILE 1
to tell Ipopt to write an output file. The parameter IPRINT essentially
determines how much output is written into that file, but by default, no
output file is created.
Hope this helps,
Andreas
On Sun, 13 Nov 2005, Lihong Zhang wrote:
> Hi Carl,
>
> I did use the Fortran version of IPOPT. Based on your
> suggestions, I looked into the README.IPOPT file
> and tried to figure out how to use the option (IPRINT).
> However, I didn't succeed. I created a file called "PARAMS.DAT"
> with the following contents:
> ******************************
> # This is a comment
>
> # Turn on IPRINT
> IPRINT 5
>
> ******************************
>
> I put the "PARAMS.DAT" at the SIF-file running directory or
> the directory where "ipopt" (the script provided by CUTEr)
> is located. However, the output result didn't change at all.
> Also I checked the "sdipopt" syntax, but I didn't find how
> to include the ipopt-options in the command line.
>
> Could you please tell me how to solve this problem (ie,
> how to include the "IPRINT" option in the command line
> of "sdipopt --blas none HS11.SIF"), using a switch or
> creating an option-file? Thanks a lot.
>
> Best regards,
>
> Lihong
>
>
>
> Carl Damon Laird wrote:
>
> > 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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