[Ipopt] IPOPT options for reducing constraint violation after fewer iterations
Austin Herrema
aherrema at iastate.edu
Wed May 10 11:51:11 EDT 2017
Hello all,
I am using IPOPT implemented through OpenMDAO and am having some trouble
understanding and controlling the stopping criteria.
Here is what I'm experiencing specifically: Initially, IPOPT is able to
find a solution that appears to be much better, although constraints are
violated slightly (intuition tells me that adjusting a few parameters would
likely bring it into the feasible region). From this stackoverflow
discussion
<http://stackoverflow.com/questions/36907064/why-does-ipopt-evaluate-objective-function-despite-breaching-constraints>
I
understand that "linear or nonlinear equality or inequality constraint will
not necessarily be satisfied until the solver has finished converging at
the final iteration," so I would like to know if I can change tolerances
such that the solver will begin to completely satisfy constraints sooner.
Currently, nearly all evaluations are in the infeasible regime.
I realize that this approach would result in a less optimal solution, but
my function evaluations are quite computationally expensive so I'd like to
be able to have some kind of control over exiting earlier but with feasible
results. It is not clear to me when looking at IPOPT termination
documentation <https://www.coin-or.org/Ipopt/documentation/node42.html> how
this might be done. (dual_inf_tol?)
Here is some output of a not-yet-converged optimization in case that is
helpful. Each of my parameters is on the order of approximately -30 to +30
and my constraints all have an upper bound of 1.0.
This is Ipopt version 3.11.7, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 144
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 12
variables with only lower bounds: 0
variables with lower and upper bounds: 12
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.2773950e-10 4.72e-02 4.31e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 -4.9176078e-05 4.70e-02 9.91e-02 -4.8 9.65e-02 - 9.86e-01 1.00e+00h 1
2 2.1621729e-03 4.03e-02 1.12e-02 -2.8 8.31e-02 - 9.93e-01 1.00e+00h 1
3 2.4150351e-03 3.95e-02 7.40e-01 -3.3 5.27e-02 - 1.00e+00 1.86e-01h 1
4 1.3194689e-02 5.61e-03 3.60e-01 -3.4 4.16e-01 - 1.00e+00 1.00e+00h 1
5 1.4923797e-02 5.70e-04 2.12e+00 -4.8 7.84e-02 - 1.00e+00 1.00e+00h 1
6 1.1292725e-02 1.03e-02 6.11e-01 -4.3 1.28e-01 - 9.91e-01 1.00e+00h 1
7 -3.0932752e-02 1.78e-01 2.37e-02 -2.7 4.93e+01 - 9.77e-01 3.04e-02f 1
8 -1.0919263e-01 1.41e-01 1.83e-02 -3.7 6.55e+01 - 1.97e-01 2.13e-01h 1
9 -1.0200310e-02 6.29e-02 2.12e-01 -1.0 8.84e+02 - 1.91e-01 3.11e-02f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.4379416e-02 7.83e-02 6.69e-02 -1.8 2.72e+01 - 5.78e-01 4.76e-01h 1
11 -6.2485434e-02 3.98e-02 4.35e-02 -1.8 2.23e+01 - 6.03e-01 1.00e+00h 1
12 -1.2862241e-01 1.22e-01 3.52e-03 -2.4 8.08e+00 - 1.00e+00 9.90e-01h 1
13 -1.4931148e-01 1.03e-01 1.28e-01 -3.5 7.31e+00 - 8.14e-01 1.00e+00h 1
14 -1.5628632e-01 1.73e-01 6.53e-02 -2.3 1.62e+01 - 1.00e+00 9.20e-01f 1
15 -1.4969877e-01 2.81e-02 5.75e-02 -2.4 1.44e+01 - 1.00e+00 9.86e-01h 1
16 -1.5014809e-01 1.13e-01 3.08e-02 -2.6 5.97e+00 - 9.57e-01 1.00e+00h 1
17 -1.5492389e-01 1.97e-02 6.94e+00 -3.3 3.98e+00 - 9.91e-01 1.00e+00h 1
18 -1.6660309e-01 5.33e-02 1.37e-02 -3.0 4.11e+00 - 9.90e-01 1.00e+00h 1
19 -1.6258901e-01 2.00e-01 7.76e-02 -2.7 5.95e+01 - 1.00e+00 1.72e-01h 2
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 -1.8526459e-01 3.63e-01 3.54e-02 -2.7 2.48e+01 - 4.07e-01 1.00e+00h 1
21 -1.7711436e-01 7.11e-02 1.58e-02 -3.1 1.31e+01 - 9.35e-01 1.00e+00h 1
22 -1.7548211e-01 4.64e-02 1.90e-01 -3.1 6.76e+00 - 5.89e-01 1.00e+00h 1
23 -1.8872718e-01 5.14e-01 5.11e-02 -2.7 8.01e+00 - 1.00e+00 9.03e-01h 1
24 -2.2657415e-01 1.51e+00 4.27e-03 -2.8 4.58e+01 - 4.16e-01 7.23e-01h 1
25 -2.1865212e-01 9.90e-01 1.52e+01 -2.8 1.94e+01 - 1.00e+00 4.04e-01h 1
26 -2.1865639e-01 9.90e-01 1.52e+01 -2.2 2.05e+01 - 9.93e-01 3.40e-04h 6
27 -2.1869343e-01 9.92e-01 1.51e+01 -3.3 2.94e+00 - 1.05e-02 1.05e-02s 16
28 -2.1869424e-01 9.91e-01 2.56e+05 -3.1 5.56e+00 - 1.00e+00 1.06e-04h 1
29r-2.1869424e-01 9.91e-01 6.51e+02 0.0 0.00e+00 - 0.00e+00 2.66e-07R 3
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
30r-2.1042636e-01 9.23e-01 1.85e+03 1.6 3.78e+02 - 1.00e+00 7.81e-04f 1
31 -2.1041707e-01 9.23e-01 8.99e+02 -4.8 1.50e+01 - 3.50e-01 3.85e-04h 1
32r-2.1041707e-01 9.23e-01 6.36e+02 1.2 0.00e+00 - 0.00e+00 4.82e-07R 4
33r-8.8862276e-03 4.97e-01 6.37e+02 3.1 1.26e+03 - 1.07e-02 6.68e-03f 1
34r-1.5970781e-02 5.08e-01 5.67e+02 1.4 1.32e+01 - 1.00e+00 4.22e-02f 1
35 8.0787130e-04 9.17e-03 8.87e+01 1.1 1.88e+02 - 4.69e-02 1.25e-01f 1
36 -2.3518550e-02 0.00e+00 1.37e+04 0.4 2.17e+00 - 3.97e-03 1.00e+00f 1
37 -1.8805455e-02 0.00e+00 1.11e+00 0.4 9.51e-01 - 1.00e+00 1.00e+00h 1
38 -1.7449909e-02 0.00e+00 3.81e-01 -0.3 1.72e+00 - 9.86e-01 1.00e+00f 1
39 -2.3978737e-02 0.00e+00 2.15e-02 -1.0 3.61e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
40 -2.4302684e-02 0.00e+00 1.38e-02 -3.1 3.49e-02 - 9.96e-01 1.00e+00h 1
41 -2.5007489e-02 0.00e+00 2.59e-02 -4.3 2.59e-02 - 9.97e-01 1.00e+00h 1
42 -2.5718840e-02 0.00e+00 2.61e-02 -5.9 2.61e-02 - 1.00e+00 1.00e+00h 1
43 -2.6430293e-02 0.00e+00 2.61e-02 -7.3 2.61e-02 - 1.00e+00 1.00e+00h 1
44 -7.4175847e-02 1.51e-02 2.61e-02 -5.3 2.96e+05 - 7.07e-06 5.89e-06f 1
45 -1.3141330e-01 1.27e-01 2.61e-02 -7.4 3.72e+05 - 2.02e-05 1.13e-05f 1
46 -1.5205276e-01 1.58e-01 2.61e-02 -7.4 2.22e+04 - 9.04e-04 1.16e-04f 1
47 -1.7484215e-01 2.50e-01 2.60e-02 -7.4 2.76e+04 - 8.48e-04 2.40e-04f 1
48 -1.8401410e-01 2.76e-01 2.60e-02 -7.4 3.18e+04 - 1.07e-03 6.43e-05f 1
49 -1.9843226e-01 3.54e-01 2.60e-02 -5.1 2.08e+04 - 1.65e-03 2.72e-04f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
50 -2.1122735e-01 9.43e-01 2.60e-02 -5.2 2.08e+04 - 3.03e-03 4.51e-04f 1
51 -2.1122463e-01 9.43e-01 2.59e-02 -5.1 6.90e-01 - 6.08e-04 1.49e-04h 1
52 -1.7577586e-01 5.51e-01 1.67e-01 -5.8 1.34e+00 - 1.00e+00 1.00e+00h 1
53 -1.7055963e-01 5.20e-01 7.44e+00 -4.0 3.27e+00 - 2.92e-01 6.02e-02h 1
54 -7.0194072e-02 2.42e-01 5.35e+03 -4.0 3.62e+00 - 8.83e-02 1.00e+00h 1
55 -7.0281067e-02 2.43e-01 6.22e+00 -4.0 4.94e-02 - 4.30e-01 1.00e+00h 1
56 -7.0328967e-02 2.45e-01 2.62e-02 -4.0 2.02e-03 - 1.00e+00 1.00e+00h 1
57 -7.0328879e-02 2.45e-01 6.00e+00 -4.0 2.39e-01 - 1.00e+00 8.29e-05h 2
58 -6.6871118e-02 2.29e-01 4.76e+00 -4.0 4.28e+00 - 5.76e-02 6.70e-02h 1
59 -6.5418550e-02 2.23e-01 6.38e+00 -4.0 5.06e+00 - 1.00e+00 2.97e-02h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
60 -6.1144197e-02 1.94e-01 5.06e+00 -4.0 5.21e+00 - 1.00e+00 1.09e-01h 1
61 -1.4374533e-01 6.24e-02 5.31e-01 -4.0 6.05e+00 - 1.32e-01 1.00e+00h 1
62 -1.9075327e-01 1.76e-01 1.15e-01 -4.0 1.56e+01 - 7.66e-01 1.80e-01h 1
63 -1.6009455e-01 1.04e-01 2.07e-02 -3.8 3.17e+00 - 1.00e+00 1.00e+00h 1
64 -1.6095612e-01 1.54e-01 4.94e-03 -2.8 6.40e+00 - 1.00e+00 1.00e+00f 1
65 -1.6644868e-01 1.13e-01 2.36e-02 -2.8 5.98e+00 - 1.00e+00 6.06e-01h 1
66 -1.7023044e-01 1.14e-01 1.87e-02 -2.8 7.48e+00 - 1.00e+00 1.00e+00h 1
67 -1.8720782e-01 1.65e-01 1.03e-03 -2.8 4.63e+00 - 1.00e+00 1.00e+00h 1
I am fairly new to IPOPT so feel free to correct me if it is clear I am
misunderstanding anything or if the optimization is obviously not
performing well (I have my suspicions...)
Thank you,
Austin
--
*Austin Herrema*
PhD Student | Graduate Research Assistant | Iowa State University
Wind Energy Science, Engineering, and Policy | Mechanical Engineering
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