[Coin-ipopt] No optimal variable values in IPOPT+CUTEr output

Fri Nov 11 19:27:21 EST 2005

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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