我在Python中使用了Gurobi。我在一组节点上迭代,每次迭代时,我都会添加一个约束来求解。求解后,它生成古罗比日志,如下所示:
Optimize a model with 6 rows, 36 columns and 41 nonzeros
Variable types: 0 continuous, 36 integer (36 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+01, 9e+01]
Bounds range [1e+00, 1e+00]
RHS range [2e+00, 2e+00]
MIP start did not produce a new incumbent solution
MIP start violates constraint R5 by 2.000000000
Found heuristic solution: objective 347.281
Presolve removed 2 rows and 21 columns
Presolve time: 0.00s
Presolved: 4 rows, 15 columns, 27 nonzeros
Found heuristic solution: objective 336.2791955
Variable types: 0 continuous, 15 integer (15 binary)
Root relaxation: objective 3.043757e+02, 6 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 304.3757488 304.37575 0.00% - 0s
Explored 0 nodes (6 simplex iterations) in 0.02 seconds
Thread count was 4 (of 4 available processors)
Solution count 3: 304.376 336.279 339.43
Optimal solution found (tolerance 1.00e-04)
Best objective 3.043757488224e+02, best bound 3.043757488224e+02, gap 0.0000%
但经过一定的迭代,我的答案并不是我所期望的。因此,我希望在每次迭代时将我的所有模型细节(目标函数、约束等)打印在Gurobi日志中。我该怎么做?
Minimize
0 x(0,0) + 75.47184905645283 x(0,1) + 57.55866572463264 x(0,2)
+ 33.97057550292606 x(0,3) + 23.3238075793812 x(0,4)
+ 40.80441152620633 x(0,5) + 75.47184905645283 x(1,0) + 0 x(1,1)
+ 32.7566787083184 x(1,2) + 90.60905032059435 x(1,3)
+ 55.71355310873648 x(1,4) + 40.60788100849391 x(1,5)
+ 57.55866572463264 x(2,0) + 32.7566787083184 x(2,1) + 0 x(2,2)
+ 83.36066218546971 x(2,3) + 46.57252408878007 x(2,4)
+ 41.4004830889689 x(2,5) + 33.97057550292606 x(3,0)
+ 90.60905032059435 x(3,1) + 83.36066218546971 x(3,2) + 0 x(3,3)
+ 37.12142238654117 x(3,4) + 50.00999900019995 x(3,5)
+ 23.3238075793812 x(4,0) + 55.71355310873648 x(4,1)
+ 46.57252408878007 x(4,2) + 37.12142238654117 x(4,3) + 0 x(4,4)
+ 17.69180601295413 x(4,5) + 40.80441152620633 x(5,0)
+ 40.60788100849391 x(5,1) + 41.4004830889689 x(5,2)
+ 50.00999900019995 x(5,3) + 17.69180601295413 x(5,4) + 0 x(5,5)
Subject To
R0: x(0,1) + x(0,2) + x(0,3) + x(0,4) + x(0,5) >= 2
R1: x(1,0) + x(1,2) + x(1,3) + x(1,4) + x(1,5) >= 2
R2: x(1,0) + x(1,3) + x(1,4) + x(2,0) + x(2,3) + x(2,4) + x(5,0) +
x(5,3)+ x(5,4) >= 2
R3: x(3,0) + x(3,1) + x(3,2) + x(3,4) + x(3,5) >= 2
R4: x(0,1) + x(0,2) + x(0,5) + x(3,1) + x(3,2) + x(3,5) + x(4,1) +
x(4,2)+ x(4,5) >= 2
R5: x(0,1) + x(0,2) + x(3,1) + x(3,2) + x(4,1) + x(4,2) + x(5,1) +
x(5,2)>= 2
Bounds
Binaries
x(0,0) x(0,1) x(0,2) x(0,3) x(0,4) x(0,5) x(1,0) x(1,1) x(1,2) x(1,3)
x(1,4) x(1,5) x(2,0) x(2,1) x(2,2) x(2,3) x(2,4) x(2,5) x(3,0) x(3,1)
x(3,2) x(3,3) x(3,4) x(3,5) x(4,0) x(4,1) x(4,2) x(4,3) x(4,4) x(4,5)
x(5,0) x(5,1) x(5,2) x(5,3) x(5,4) x(5,5)
End
换言之,确实如此。”
Gurobi callbacks
“允许我们访问模型中可用的所有信息?它将产生什么?
