NLPModelsIpopt options not registering when rebuilding backend #26
Description
I'm encountering an issue where previously set solver options do not apply to rebuilt backends.
Here's an MRE where both set_silent and print_timing_statistics did not apply in the second solve:
using InfiniteOpt, InfiniteExaModels, NLPModelsIpopt, Interpolations
m = InfiniteModel(ExaTranscriptionBackend(NLPModelsIpopt.IpoptSolver))
@infinite_parameter(m, t in [0, 1], num_supports = 5)
@infinite_parameter(m, x in [-1, 1], num_supports = 5)
@variable(m, y >= 0, Infinite(t, x), start = 1.0)
@finite_parameter(m, z == 10)
@objective(m, Min, ∫(∫(y^2, t) + 2z, x))
@constraint(m, y + z <= 42 + t)
set_silent(m)
set_optimizer_attribute(m, "print_timing_statistics", "yes")
optimize!(m)
set_start_values(m)
optimize!(m)
and the REPL output:
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.8.1.
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 25
Number of nonzeros in Lagrangian Hessian.............: 25
Total number of variables............................: 25
variables with only lower bounds: 25
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 25
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 25
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 4.0000200e+01 0.00e+00 1.25e-03 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 4.0024081e+01 0.00e+00 3.94e-03 -1.0 9.99e-02 - 9.96e-01 1.00e+00f 1
2 4.0529845e+01 0.00e+00 2.00e-07 -1.7 8.08e-01 - 1.00e+00 1.00e+00f 1
3 4.0151809e+01 0.00e+00 2.83e-08 -2.5 4.32e-01 - 1.00e+00 1.00e+00f 1
4 4.0038967e+01 0.00e+00 1.50e-09 -3.8 2.40e-01 - 1.00e+00 1.00e+00f 1
5 4.0010702e+01 0.00e+00 1.50e-09 -3.8 1.18e-01 - 1.00e+00 1.00e+00f 1
6 4.0002688e+01 0.00e+00 1.84e-11 -5.7 6.38e-02 - 1.00e+00 1.00e+00f 1
7 4.0000684e+01 0.00e+00 1.84e-11 -5.7 3.18e-02 - 1.00e+00 1.00e+00f 1
8 4.0000183e+01 0.00e+00 1.84e-11 -5.7 1.57e-02 - 1.00e+00 1.00e+00f 1
9 4.0000046e+01 0.00e+00 2.51e-14 -8.6 8.29e-03 - 1.00e+00 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 4.0000011e+01 0.00e+00 2.51e-14 -8.6 4.14e-03 - 1.00e+00 1.00e+00f 1
11 4.0000003e+01 0.00e+00 2.51e-14 -8.6 2.07e-03 - 1.00e+00 1.00e+00f 1
12 4.0000001e+01 0.00e+00 2.51e-14 -8.6 1.03e-03 - 1.00e+00 1.00e+00f 1
13 4.0000000e+01 0.00e+00 2.51e-14 -8.6 5.05e-04 - 1.00e+00 1.00e+00f 1
14 4.0000000e+01 0.00e+00 9.09e-15 -9.0 2.58e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 4.0000000055738361e+01 4.0000000055738361e+01
Dual infeasibility......: 9.0909132435465501e-15 9.0909132435465501e-15
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 5.0825507409666755e-09 5.0825507409666755e-09
Overall NLP error.......: 5.0825507409666755e-09 5.0825507409666755e-09
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.129
EXIT: Optimal Solution Found.