[Ipopt] Bound-constrained solution worst than initial guess

Thu Jun 5 13:12:54 EDT 2008

Hi all,

I've found a solution for my problem by adjusting the value of the
option bound_mult_init_val from 1 to 0.001.

I'm not sure about the actual meaning of that option but it did the
trick.

Regards.

On Thu, 2008-06-05 at 12:00 -0400, ipopt-request at list.coin-or.org wrote:
> Dear all,
> 
> I'm trying to solve a small bound-constrained problem with Ipopt. I'm
> giving the analytic first-order derivatives and no hessian information.
> 
> The problem is, I have a very good initial estimation of the optimum.
> With my initial estimative, the objective function evaluates to
> -2.6903480e+01. My initial estimate is feasible with respect to the
> bounds and there is no additional constraints. My doubt is: how can
> Ipopt find a solution which is worst ?(-2.6826471985692432e+01) than my
> initial guess, if the initial guess is a feasible solution? Is that
> something related with the tolerances or options?
> 
> Thank you in advance.
> 
> By the way, the derivative checker cannot find any errors on my
> derivatives.
> 
> It follows the output for the problem:
> 
> 
> ******************************************************************************
> This program contains Ipopt, a library for large-scale nonlinear
> optimization.
>  Ipopt is released as open source code under the Common Public License
> (CPL).
>          For more information visit http://projects.coin-or.org/Ipopt
> ******************************************************************************
> 
> NOTE: You are using Ipopt by default with the MUMPS linear solver.
>       Other linear solvers might be more efficient (see Ipopt
> documentation).
> 
> 
> This is Ipopt version 3.4trunk, running with linear solver mumps.
> 
> Number of nonzeros in equality constraint Jacobian...:        0
> Number of nonzeros in inequality constraint Jacobian.:        0
> Number of nonzeros in Lagrangian Hessian.............:        0
> 
> Total number of variables............................:        6
>                      variables with only lower bounds:        0
>                 variables with lower and upper bounds:        6
>                      variables with only upper bounds:        0
> Total number of equality constraints.................:        0
> Total number of inequality constraints...............:        0
>         inequality constraints with only lower bounds:        0
>    inequality constraints with lower and upper bounds:        0
>         inequality constraints with only upper bounds:        0
> 
> iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du
> alpha_pr  ls
>    0 -2.6903480e+01 0.00e+00 1.23e+00   0.0 0.00e+00    -  0.00e+00
> 0.00e+00   0
>    1 -2.6773883e+01 0.00e+00 1.25e+00  -0.8 1.58e-01    -  9.66e-01
> 1.00e+00f  1
>    2 -2.6762138e+01 0.00e+00 5.53e-01  -0.8 4.36e-01    -  1.00e+00
> 1.00e+00f  1
>    3 -2.6765537e+01 0.00e+00 2.97e-02  -1.5 2.85e-02    -  1.00e+00
> 1.00e+00f  1
>    4 -2.6782839e+01 0.00e+00 1.26e-01  -2.3 5.76e-02    -  1.00e+00
> 1.00e+00f  1
>    5 -2.6813428e+01 0.00e+00 9.97e-02  -2.3 1.12e-01    -  1.00e+00
> 1.00e+00f  1
>    6 -2.6818494e+01 0.00e+00 8.18e-02  -2.3 6.85e-02    -  1.00e+00
> 1.00e+00f  1
>    7 -2.6823893e+01 0.00e+00 3.77e-02  -2.3 5.74e-02    -  1.00e+00
> 1.00e+00f  1
>    8 -2.6826132e+01 0.00e+00 3.05e-02  -3.4 9.18e-02    -  9.46e-01
> 1.00e+00f  1
>    9 -2.6826319e+01 0.00e+00 1.36e-02  -3.4 2.51e-02    -  1.00e+00
> 1.00e+00f  1
> iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du
> alpha_pr  ls
>   10 -2.6826439e+01 0.00e+00 2.99e-02  -3.4 1.12e-02    -  1.00e+00
> 1.00e+00f  1
>   11 -2.6826454e+01 0.00e+00 8.72e-03  -3.4 9.73e-03    -  1.00e+00
> 5.00e-01f  2
>   12 -2.6826465e+01 0.00e+00 6.14e-04  -3.4 2.86e-03    -  1.00e+00
> 1.00e+00f  1
>   13 -2.6826472e+01 0.00e+00 3.36e-04  -5.2 2.59e-03    -  1.00e+00
> 1.00e+00f  1
>   14 -2.6826472e+01 0.00e+00 2.78e-04  -5.2 3.43e-04    -  1.00e+00
> 1.00e+00f  1
>   15 -2.6826472e+01 0.00e+00 3.52e-04  -5.2 3.10e-04    -  1.00e+00
> 1.00e+00f  1
>   16 -2.6826472e+01 0.00e+00 9.92e-06  -5.2 1.90e-04    -  1.00e+00
> 1.00e+00f  1
>   17 -2.6826472e+01 0.00e+00 2.95e-06  -7.0 6.05e-05    -  1.00e+00
> 1.00e+00f  1
>   18 -2.6826472e+01 0.00e+00 4.87e-06  -7.0 5.56e-06    -  1.00e+00
> 1.00e+00f  1
>   19 -2.6826472e+01 0.00e+00 4.47e-06  -7.0 3.50e-06    -  1.00e+00
> 1.00e+00f  1
> iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du
> alpha_pr  ls
>   20 -2.6826472e+01 0.00e+00 1.98e-06  -7.0 2.45e-06    -  1.00e+00
> 2.50e-01f  3
>   21 -2.6826472e+01 0.00e+00 7.27e-08  -7.0 6.95e-07    -  1.00e+00
> 1.00e+00f  1
> 
> Number of Iterations....: 21
> 
>                                    (scaled)                 (unscaled)
> Objective...............:  -2.6826471985692432e+01
> -2.6826471985692432e+01
> Dual infeasibility......:   7.2654462898683632e-08
> 7.2654462898683632e-08
> Constraint violation....:   0.0000000000000000e+00
> 0.0000000000000000e+00
> Complementarity.........:   9.0909090910064460e-08
> 9.0909090910064460e-08
> Overall NLP error.......:   9.0909090910064460e-08
> 9.0909090910064460e-08
> 
> 
> Number of objective function evaluations             = 33
> Number of objective gradient evaluations             = 22
> Number of equality constraint evaluations            = 0
> Number of inequality constraint evaluations          = 0
> Number of equality constraint Jacobian evaluations   = 0
> Number of inequality constraint Jacobian evaluations = 0
> Number of Lagrangian Hessian evaluations             = 0
> Total CPU secs in IPOPT (w/o function evaluations)   =      0.676
> Total CPU secs in NLP function evaluations           =      0.000
> 
> EXIT: Optimal Solution Found.