[Coin-ipopt] No optimal variable values in IPOPT+CUTEr output
Lihong Zhang
lihong at ee.washington.edu
Sun Nov 13 23:12:41 EST 2005
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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