moorepants
commented
1 month ago But this works:
conda create -n cyipopt-test cyipopt
conda activate cyipopt-test
python hs071.py
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit https://github.com/coin-or/Ipopt
******************************************************************************
This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.
Number of nonzeros in equality constraint Jacobian...: 4
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 10
Total number of variables............................: 4
variables with only lower bounds: 0
variables with lower and upper bounds: 4
variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 1
inequality constraints with only lower bounds: 1
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
Objective value at iteration #0 is - 16.1097
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.6109693e+01 1.12e+01 1.02e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
Objective value at iteration #1 is - 17.3461
1 1.7346108e+01 7.50e-01 2.37e+01 -0.3 7.22e-01 - 3.51e-01 1.00e+00f 1
Objective value at iteration #2 is - 17.9222
2 1.7922201e+01 7.70e-03 5.29e+00 -0.2 6.00e-02 2.0 9.96e-01 1.00e+00h 1
Objective value at iteration #3 is - 17.1673
3 1.7167308e+01 6.72e-02 1.66e+00 -0.8 1.37e+00 - 9.95e-01 6.98e-01f 1
Objective value at iteration #4 is - 16.9893
4 1.6989299e+01 1.02e-01 1.47e-01 -1.2 2.30e-01 - 9.92e-01 1.00e+00f 1
Objective value at iteration #5 is - 16.9944
5 1.6994402e+01 3.39e-02 1.19e-02 -2.3 8.69e-02 - 9.84e-01 1.00e+00h 1
Objective value at iteration #6 is - 17.014
6 1.7013957e+01 2.20e-04 3.14e-04 -3.8 1.15e-02 - 9.99e-01 1.00e+00h 1
Objective value at iteration #7 is - 17.014
7 1.7014017e+01 6.30e-07 1.31e-06 -9.7 3.43e-04 - 9.99e-01 9.99e-01h 1
Objective value at iteration #8 is - 17.014
8 1.7014017e+01 5.68e-13 8.43e-13 -11.0 5.69e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 8
(scaled) (unscaled)
Objective...............: 3.4028034280427242e+01 1.7014017140213621e+01
Dual infeasibility......: 8.4300285623552048e-13 4.2150142811776024e-13
Constraint violation....: 5.6843418860808015e-13 5.6843418860808015e-13
Variable bound violation: 9.9952566223748818e-09 9.9952566223748818e-09
Complementarity.........: 1.1688338036422999e-11 5.8441690182114995e-12
Overall NLP error.......: 1.1688338036422999e-11 5.8441690182114995e-12
Number of objective function evaluations = 9
Number of objective gradient evaluations = 9
Number of equality constraint evaluations = 9
Number of inequality constraint evaluations = 9
Number of equality constraint Jacobian evaluations = 9
Number of inequality constraint Jacobian evaluations = 9
Number of Lagrangian Hessian evaluations = 8
Total seconds in IPOPT = 0.062
EXIT: Optimal Solution Found.
Solution of the primal variables: x=array([0.99999999, 4.74299964, 3.82114998, 1.37940829])
Solution of the dual variables: lambda=array([-0.55229366, 0.16146856])
Objective=17.01401714021362
Solution to issue cannot be found in the documentation.
Issue
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