我在一个相当复杂的模型上使用Pyomo和Bonmin并且它耗费了12个多小时来计算。
我尝试将参数“bonmin.time_limit”设置为1800（30分钟），但它没有返回任何变量值。
> ==========================================================
> = Solver Results =
> ==========================================================
> ----------------------------------------------------------
> Problem Information
> ----------------------------------------------------------
> Problem:
> - Lower bound: -inf
> Upper bound: inf
> Number of objectives: 1
> Number of constraints: 0
> Number of variables: 0
> Sense: unknown
> ----------------------------------------------------------
> Solver Information
> ----------------------------------------------------------
Solver:
> - Status: warning
> Message: bonmin\x3a Optimization interupted on limit.
> Termination condition: maxIterations
> Id: 410
> Error rc: 0
> Time: 1813.3797194957733
> ----------------------------------------------------------
> Solution Information
> ----------------------------------------------------------
Solution:
> - number of solutions: 0
> number of solutions displayed: 0
它输出多个信息的“块”。
有时，它输出：
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
600 -2.7877808e+005 1.82e-012 6.71e-003 -11.0 2.75e+000 -10.5 1.00e+000 5.56e-001h 1
601 -2.7877808e+005 1.82e-012 6.79e-010 -11.0 1.05e+000 -10.0 1.00e+000 1.00e+000f 1
Number of Iterations....: 601
(scaled) (unscaled)
Objective...............: -7.4080060189689470e+002 -2.7877808250893821e+005
Dual infeasibility......: 6.7945537380025706e-010 2.5569264627135494e-007
Constraint violation....: 1.8189894035458565e-012 1.8189894035458565e-012
Complementarity.........: 8.6460028294267016e-011 3.2536637848060228e-008
Overall NLP error.......: 6.7945537380025706e-010 2.5569264627135494e-007
Number of objective function evaluations = 1075
Number of objective gradient evaluations = 516
Number of equality constraint evaluations = 1075
Number of inequality constraint evaluations = 1075
Number of equality constraint Jacobian evaluations = 604
Number of inequality constraint Jacobian evaluations = 604
Number of Lagrangian Hessian evaluations = 601
Total CPU secs in IPOPT (w/o function evaluations) = 101.103
Total CPU secs in NLP function evaluations = 0.694
EXIT: Optimal Solution Found.
但是，它立即运行另一组计算（这从前一个块继续）：
(Previous block) EXIT: Optimal Solution Found.
This is Ipopt version 3.10.1, running with linear solver mumps.
Number of nonzeros in equality constraint Jacobian...: 6670
Number of nonzeros in inequality constraint Jacobian.: 13580
Number of nonzeros in Lagrangian Hessian.............: 196
Total number of variables............................: 3694
variables with only lower bounds: 2730
variables with lower and upper bounds: 964
variables with only upper bounds: 0
Total number of equality constraints.................: 1238
Total number of inequality constraints...............: 3612
inequality constraints with only lower bounds: 160
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 3452
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -2.7146106e+005 9.00e+000 1.66e+000 0.0 0.00e+000 - 0.00e+000 0.00e+000 0
1 -2.7135545e+005 9.00e+000 3.38e+000 4.8 3.59e+006 - 8.46e-008 2.71e-007f 1
2 -2.7112865e+005 9.00e+000 7.89e+000 4.7 3.14e+006 - 2.80e-007 6.37e-007f 1
3 -2.7083702e+005 9.00e+000 8.43e+000 4.2 8.99e+005 - 2.67e-006 2.79e-006f 1
等等......
有没有办法从第一个街区获得解决方案？
它会永远这样重复。
答案 0 :(得分：0)
它输出多个信息的“块”。
有时，它输出：
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
600 -2.7877808e+005 1.82e-012 6.71e-003 -11.0 2.75e+000 -10.5 1.00e+000 5.56e-001h 1
601 -2.7877808e+005 1.82e-012 6.79e-010 -11.0 1.05e+000 -10.0 1.00e+000 1.00e+000f 1
Number of Iterations....: 601
(scaled) (unscaled)
Objective...............: -7.4080060189689470e+002 -2.7877808250893821e+005
Dual infeasibility......: 6.7945537380025706e-010 2.5569264627135494e-007
Constraint violation....: 1.8189894035458565e-012 1.8189894035458565e-012
Complementarity.........: 8.6460028294267016e-011 3.2536637848060228e-008
Overall NLP error.......: 6.7945537380025706e-010 2.5569264627135494e-007
Number of objective function evaluations = 1075
Number of objective gradient evaluations = 516
Number of equality constraint evaluations = 1075
Number of inequality constraint evaluations = 1075
Number of equality constraint Jacobian evaluations = 604
Number of inequality constraint Jacobian evaluations = 604
Number of Lagrangian Hessian evaluations = 601
Total CPU secs in IPOPT (w/o function evaluations) = 101.103
Total CPU secs in NLP function evaluations = 0.694
EXIT: Optimal Solution Found.
但是，它立即运行另一组计算（这从前一个块继续）：
(Previous block) EXIT: Optimal Solution Found.
This is Ipopt version 3.10.1, running with linear solver mumps.
Number of nonzeros in equality constraint Jacobian...: 6670
Number of nonzeros in inequality constraint Jacobian.: 13580
Number of nonzeros in Lagrangian Hessian.............: 196
Total number of variables............................: 3694
variables with only lower bounds: 2730
variables with lower and upper bounds: 964
variables with only upper bounds: 0
Total number of equality constraints.................: 1238
Total number of inequality constraints...............: 3612
inequality constraints with only lower bounds: 160
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 3452
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -2.7146106e+005 9.00e+000 1.66e+000 0.0 0.00e+000 - 0.00e+000 0.00e+000 0
1 -2.7135545e+005 9.00e+000 3.38e+000 4.8 3.59e+006 - 8.46e-008 2.71e-007f 1
2 -2.7112865e+005 9.00e+000 7.89e+000 4.7 3.14e+006 - 2.80e-007 6.37e-007f 1
3 -2.7083702e+005 9.00e+000 8.43e+000 4.2 8.99e+005 - 2.67e-006 2.79e-006f 1
等等......
有没有办法从第一个街区获得解决方案？
它会永远这样重复。