IPOPT选项可减少迭代次数后减少约束违规 [英] IPOPT options for reducing constraint violation after fewer iterations
问题描述
我正在使用通过OpenMDAO实施的IPOPT，在理解和控制停止标准方面遇到一些麻烦.
I am using IPOPT implemented through OpenMDAO and am having some trouble understanding and controlling the stopping criteria.
这是我具体要经历的事情:最初，IPOPT能够找到一个看起来更好的解决方案，尽管稍微违反了约束条件(直觉告诉我，调整一些参数可能会将其带入可行区域).来自此讨论，我理解线性或非线性等式或不等式约束在求解器在最终迭代中完成收敛之前不一定会得到满足."因此，我想知道是否可以更改容差，以便求解器将更快地完全满足约束(我什至正确地理解了这一点.?).当前，几乎所有评估都处于不可行的状态.
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 discussion 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 (am I even understanding that correctly?). Currently nearly all evaluations are in the infeasible regime.
我意识到这种方法将导致最优解决方案的减少，但是我的函数评估在计算上非常昂贵，因此我希望能够对早期退出进行某种控制，但结果可行.在查看 IPOPT终止文档时，我不清楚可能会完成.( dual_inf_tol ?)
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 how this might be done. (dual_inf_tol?)
如果有帮助，以下是一些尚未收敛的优化输出.我的每个参数大约在-30至+30的范围内，我的约束都具有1.0的上限.
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
我对IPOPT来说还很陌生，因此，如果很明显我误解了任何东西，或者如果优化显然表现不佳(我怀疑...)，请随时纠正我.
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...).
推荐答案
在MATLAB中使用IPOPT时，我遇到了同样的问题.我已将此选项设置为"gamma_theta"，到1e-2.这减小了约束违反的松弛因子.这对我有用.希望我能帮上忙！
I had the same issue with my IPOPT when using it in MATLAB. I have set this option "gamma_theta" to 1e-2. This decreases the relaxation factor of constraint violation. This has worked for me. Hope I helped!
有关该选项的更多信息，请参见下文."http://casadi.sourceforge.net/v2.1.1/api/internal/d7/d2f/classcasadi_1_1IpoptInterface.html"
See below for more info on the option. "http://casadi.sourceforge.net/v2.1.1/api/internal/d7/d2f/classcasadi_1_1IpoptInterface.html"
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