[Ipopt] is this a bug? bad evaluation of the objective function at the optimum
Tony Kelman
kelman at berkeley.edu
Tue May 5 11:04:48 EDT 2015
I don't see anything unexpected here. Are you sure there's a primal-dual
feasible solution with objective value of -9.0328125e+01? At iteration 16
the primal infeasibility inf_pr was zero, but the dual infeasibility inf_du
was nonzero. It looks like it maybe should have converged after iteration 6,
but maybe the Newton step wasn't quite small enough yet. From iteration 7
until 16 the dual infeasibility was increasing, which is a bit strange.
Check your derivatives as Stefan said.
-Tony
-----Original Message-----
From: Stefan Vigerske
Sent: Tuesday, May 05, 2015 7:56 AM
To: Pedro C. Alvarez ; ipopt at list.coin-or.org
Subject: Re: [Ipopt] is this a bug? bad evaluation of the objective function
at the optimum
Hi,
bugreports should probably better go into the bugtracking system. There,
you could also attach your R code to reproduce the issue.
I don't know if anyone would actually look at it in the near future, though.
If you want to investigate by yourself, then enabling the derivative
checker is usually a good first iteration.
Stefan
On 05/04/2015 05:56 PM, Pedro C. Alvarez wrote:
> Hi all,
>
> I am starting to use Ipopt (through R interface), and I found this
> surprising
> 'bug'(?) related to the value of the objective function at the optimum.
> Ipopt
> finds correctly the optimum, but the value of the objective at the optimum
> is
> incorrect.
> I paste below the output of IPopt. Observe that at iteration #17 the value
> of
> the objective is worst that at #16 (which is approx the good value of the
> objective at the optimum).
>
> The problem I am trying to solve is a very easy convex problem with linear
> constraints.
>
> Any clue would be appreciated?
>
> thank very much,
> Pedro.
>
> ------------------------------------------------------------------------------------------------
> This is Ipopt version 3.12.0, running with linear solver mumps.
> NOTE: Other linear solvers might be more efficient (see Ipopt
> documentation).
>
> Number of nonzeros in equality constraint Jacobian...: 0
> Number of nonzeros in inequality constraint Jacobian.: 8
> Number of nonzeros in Lagrangian Hessian.............: 4
>
> Total number of variables............................: 4
> variables with only lower bounds: 0
> variables with lower and upper bounds: 0
> variables with only upper bounds: 0
> Total number of equality constraints.................: 0
> Total number of inequality constraints...............: 5
> inequality constraints with only lower bounds: 0
> inequality constraints with lower and upper bounds: 5
> inequality constraints with only upper bounds: 0
>
> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
> alpha_pr
> ls
> 0 -7.3757500e+01 0.00e+00 3.23e+01 -1.0 0.00e+00 - 0.00e+00
> 0.00e+00
> 0
> 1 -8.9257486e+01 0.00e+00 3.20e+01 -1.0 1.70e+00 - 3.01e-02
> 1.10e-01f
> 1
> 2 -8.9941241e+01 0.00e+00 4.12e+01 -1.0 2.66e-01 - 9.10e-01
> 4.03e-02f
> 1
> 3 -8.9814517e+01 0.00e+00 1.42e-14 -1.0 3.57e-03 - 1.00e+00
> 1.00e+00f
> 1
> 4 -9.0308405e+01 0.00e+00 1.42e-14 -2.5 6.09e-03 - 1.00e+00
> 1.00e+00f
> 1
> 5 -9.0327373e+01 0.00e+00 1.42e-14 -3.8 2.34e-04 - 1.00e+00
> 1.00e+00f
> 1
> 6 -9.0328119e+01 0.00e+00 1.42e-14 -5.7 9.21e-06 - 1.00e+00
> 1.00e+00f
> 1
> 7 -9.0328122e+01 0.00e+00 2.11e+00 -8.6 1.13e-07 - 1.00e+00
> 3.95e-01f
> 2
> 8 -9.0328124e+01 0.00e+00 6.53e+00 -8.6 6.53e-08 - 1.00e+00
> 3.40e-01f
> 2
> 9 -9.0328125e+01 0.00e+00 1.60e+01 -8.6 3.70e-08 - 1.00e+00
> 3.00e-01f
> 2
> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
> alpha_pr
> ls
> 10 -9.0328125e+01 0.00e+00 3.75e+01 -8.6 1.74e-08 - 1.00e+00
> 3.99e-02f
> 5
> 11 -9.0328125e+01 0.00e+00 4.80e+01 -8.6 1.23e-08 - 1.00e+00
> 2.64e-02f
> 6
> 12 -9.0328125e+01 0.00e+00 5.15e+01 -8.6 1.06e-08 - 1.00e+00
> 7.46e-03f
> 8
> 13 -9.0328125e+01 0.00e+00 5.25e+01 -8.6 1.01e-08 - 1.00e+00
> 4.84e-04f
> 12
> 14 -9.0328125e+01 0.00e+00 5.27e+01 -8.6 1.00e-08 - 1.00e+00
> 2.44e-04f
> 13
> 15 -9.0328125e+01 0.00e+00 5.28e+01 -8.6 9.98e-09 - 1.00e+00
> 1.22e-04h
> 14
> 16 -9.0328125e+01 0.00e+00 5.28e+01 -8.6 9.97e-09 - 1.00e+00
> 6.10e-05h
> 15
> 17 -8.1203126e+01 0.00e+00 1.42e-14 -8.6 9.97e-09 - 1.00e+00
> 1.00e+00w
> 1
>
> Number of Iterations....: 17
>
> (scaled) (unscaled)
>
> jective...............: -8.1203125989310649e+01 -8.1203125989310649e+01
> Dual infeasibility......: 1.4210854715202004e-14
> 1.4210854715202004e-14
> Constraint violation....: 0.0000000000000000e+00
> 0.0000000000000000e+00
> Complementarity.........: 2.5161412838377742e-09
> 2.5161412838377742e-09
> Overall NLP error.......: 2.5161412838377742e-09
> 2.5161412838377742e-09
>
>
> Number of objective function evaluations = 91
> Number of objective gradient evaluations = 18
> Number of equality constraint evaluations = 0
> Number of inequality constraint evaluations = 91
> Number of equality constraint Jacobian evaluations = 0
> Number of inequality constraint Jacobian evaluations = 18
> Number of Lagrangian Hessian evaluations = 17
> Total CPU secs in IPOPT (w/o function evaluations) = 0.009
> Total CPU secs in NLP function evaluations = 0.024
>
> EXIT: Optimal Solution Found.
>
>
>
>
>
>
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