通常當我嘗試使用IPOPT解決問題時，即使問題不可行，IPOPT也會顯示運行過程，例如顯示問題有多少約束以及問題的其他一般信息，但這次我遇到了一個奇怪的問題 . 我第一次解決它沒有顯示的問題，但第二次解決它，IPOPT返回最佳解決方案 . 像這樣：

-> ampl.eval('option solver ipopt ;');

-> ampl.solve()

(它在這里什么也沒有回復!!!!)

-> solve_result = ampl.getValue('solve_result_num')

solve_result = -1

所以在這里，我再次運行代碼：

-> ampl.solve()

Ipopt 3.12.4:

******************************************************************************

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 http://projects.coin-or.org/Ipopt

******************************************************************************

This is Ipopt version 3.12.4, running with linear solver mumps.

NOTE: Other linear solvers might be more efficient (see Ipopt documentation).

Number of nonzeros in equality constraint Jacobian...: 4439

Number of nonzeros in inequality constraint Jacobian.: 7888

Number of nonzeros in Lagrangian Hessian.............: 50

Total number of variables............................: 1610

variables with only lower bounds: 0

variables with lower and upper bounds: 70

variables with only upper bounds: 0

Total number of equality constraints.................: 1360

Total number of inequality constraints...............: 2585

inequality constraints with only lower bounds: 0

inequality constraints with lower and upper bounds: 2585

inequality constraints with only upper bounds: 0

iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls

0 5.0001000e+03 1.31e+00 8.32e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0

1 5.0079602e+03 1.31e+00 2.71e+01 -1.0 1.68e+01 - 1.05e-02 7.73e-04f 1

2 3.9278729e+03 1.17e+00 9.17e+00 -1.0 1.24e+00 - 3.65e-02 9.32e-02f 1

3 2.7624370e+03 9.13e-01 2.49e+01 -1.0 4.52e-01 - 7.04e-01 1.99e-01f 1

4 1.6350590e+03 7.87e-02 2.17e+01 -1.0 6.17e-01 - 2.98e-01 7.82e-01f 1

5 8.6271513e+02 3.73e-13 2.34e-01 -1.0 1.53e-01 - 9.90e-01 1.00e+00f 1

6 1.0292941e+03 1.98e-13 8.49e-13 -1.0 1.03e-01 - 1.00e+00 1.00e+00f 1

7 1.0259140e+03 1.86e-13 6.85e-13 -2.5 6.57e-04 - 1.00e+00 1.00e+00f 1

8 9.1964915e+02 2.02e-13 7.11e-04 -3.8 1.84e-02 - 9.96e-01 1.00e+00f 1

9 3.5639825e+02 2.30e-13 4.23e-02 -5.7 9.45e-02 - 7.68e-01 1.00e+00f 1

iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls

10 7.9479322e+01 2.23e-13 9.26e-03 -5.7 1.85e-01 - 8.95e-01 1.00e+00f 1

11 1.9247063e+01 2.72e-13 2.80e-14 -5.7 1.19e-01 - 1.00e+00 1.00e+00f 1

12 6.9889052e+00 3.55e-13 9.99e-05 -8.6 4.59e-02 - 9.33e-01 1.00e+00f 1

13 1.2789711e+00 4.20e-13 4.22e-04 -8.6 2.96e-01 - 4.85e-01 8.98e-01f 1

14 2.9354384e-01 2.00e-13 6.84e-06 -8.6 4.49e-02 - 9.92e-01 1.00e+00f 1

15 1.0626050e-01 2.41e-13 7.17e-14 -8.6 9.55e-03 - 1.00e+00 1.00e+00h 1

16 6.3028082e-02 3.77e-13 5.27e-14 -8.6 1.37e-03 - 1.00e+00 1.00e+00h 1

17 4.3933480e-03 3.27e-13 6.90e-08 -11.0 2.32e-03 - 9.99e-01 1.00e+00h 1

18 1.1280540e-03 2.81e-13 8.08e-14 -11.0 1.02e-04 - 1.00e+00 1.00e+00h 1

19 3.9735544e-04 3.93e-13 5.46e-14 -11.0 5.59e-05 - 1.00e+00 1.00e+00h 1

iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls

20 2.2989758e-04 3.19e-13 1.68e-14 -11.0 2.72e-05 - 1.00e+00 1.00e+00h 1

Number of Iterations....: 20

(scaled) (unscaled)

Objective...............: 2.4678359201693643e-10 2.2989757642660348e-04

Dual infeasibility......: 1.6794654637445761e-14 1.5645490717250029e-08

Constraint violation....: 2.7491418973006631e-15 3.1883523599063324e-13

Complementarity.........: 5.3838886983188674e-11 5.0154994235157414e-05

Overall NLP error.......: 5.3838886983188674e-11 5.0154994235157414e-05

Number of objective function evaluations = 21

Number of objective gradient evaluations = 21

Number of equality constraint evaluations = 21

Number of inequality constraint evaluations = 21

Number of equality constraint Jacobian evaluations = 21

Number of inequality constraint Jacobian evaluations = 21

Number of Lagrangian Hessian evaluations = 20

Total CPU secs in IPOPT (w/o function evaluations) = 0.295

Total CPU secs in NLP function evaluations = 0.004

EXIT: Optimal So

Ipopt 3.12.4: Optimal Solution Found

suffix ipopt_zU_out OUT;

suffix ipopt_zL_out OUT;

-> solve_result = ampl.getValue('solve_result_num')

solve_result = 0

所以任何人都可以知道為什么會發生這種情況，這是什么意思？

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