我正在尝试使用cplex解决LP优化问题。 （在python中，使用cvxpy）
根据我对问题的约束，cplex求解器有时无法找到解决方案。我想要一些有关提供verbose=True时如何读取求解器输出的直觉。
例如，我明白了：
Version identifier: 12.10.0.0 | 2019-11-26 | 843d4de
CPXPARAM_Read_DataCheck 1
CPXPARAM_Preprocessing_QCPDuals 2
Found incumbent of value 0.000000 after 0.00 sec. (0.08 ticks)
Tried aggregator 2 times.
MIP Presolve eliminated 4726 rows and 353 columns.
MIP Presolve modified 1008 coefficients.
Aggregator did 3 substitutions.
Reduced MIP has 1669 rows, 1670 columns, and 4505 nonzeros.
Reduced MIP has 1168 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.04 sec. (5.22 ticks)
Probing fixed 0 vars, tightened 2 bounds.
Probing time = 0.01 sec. (0.85 ticks)
Tried aggregator 1 time.
Detecting symmetries...
MIP Presolve modified 2 coefficients.
Reduced MIP has 1669 rows, 1670 columns, and 4505 nonzeros.
Reduced MIP has 1168 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.03 sec. (3.10 ticks)
Probing time = 0.01 sec. (0.90 ticks)
Clique table members: 2327.
MIP emphasis: balance optimality and feasibility.
MIP search method: dynamic search.
Parallel mode: deterministic, using up to 4 threads.
Root relaxation solution time = 0.01 sec. (5.82 ticks)
Nodes Cuts/
Node Left Objective IInf Best Integer Best Bound ItCnt Gap
* 0+ 0 0.0000 -325889.6000 ---
0 0 -23545.7611 333 0.0000 -23545.7611 723 ---
* 0+ 0 -15240.4400 -23545.7611 54.50%
0 0 -21360.6115 333 -15240.4400 Cuts: 333 1063 40.16%
* 0+ 0 -20698.8800 -21360.6115 3.20%
0 0 -21120.8531 333 -20698.8800 Cuts: 268 1232 2.04%
* 0+ 0 -20751.6200 -21120.8531 1.78%
0 0 -21057.6246 333 -20751.6200 Cuts: 92 1322 1.47%
* 0+ 0 -20843.7200 -21057.6246 1.03%
0 0 -21023.5211 333 -20843.7200 Cuts: 90 1397 0.86%
0 0 -20983.6905 333 -20843.7200 Cuts: 57 1440 0.67%
* 0+ 0 -20877.4400 -20983.6905 0.51%
Detecting symmetries...
0 0 -20972.8355 333 -20877.4400 Cuts: 13 1449 0.46%
* 0+ 0 -20878.2600 -20972.8355 0.45%
0 0 -20970.0341 333 -20878.2600 Cuts: 19 1460 0.44%
0 0 -20969.6020 333 -20878.2600 Cuts: 10 1471 0.44%
0 0 -20969.2988 333 -20878.2600 MIRcuts: 4 1476 0.44%
0 0 -20959.2311 333 -20878.2600 Cuts: 9 1483 0.39%
* 0+ 0 -20935.7200 -20959.2311 0.11%
0 0 -20958.0881 333 -20935.7200 Cuts: 15 1500 0.11%
* 0+ 0 -20935.7200 -20958.0881 0.11%
Detecting symmetries...
Repeating presolve.
Tried aggregator 2 times.
MIP Presolve eliminated 1039 rows and 925 columns.
MIP Presolve modified 62 coefficients.
Aggregator did 102 substitutions.
Reduced MIP has 528 rows, 639 columns, and 1468 nonzeros.
Reduced MIP has 526 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.04 sec. (2.78 ticks)
Probing fixed 0 vars, tightened 18 bounds.
Probing time = 0.00 sec. (0.36 ticks)
Tried aggregator 2 times.
MIP Presolve eliminated 241 rows and 308 columns.
MIP Presolve modified 23 coefficients.
Aggregator did 2 substitutions.
Reduced MIP has 285 rows, 329 columns, and 792 nonzeros.
Reduced MIP has 261 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.01 sec. (0.68 ticks)
Probing fixed 0 vars, tightened 1 bounds.
Probing time = 0.01 sec. (0.18 ticks)
Tried aggregator 1 time.
Detecting symmetries...
MIP Presolve modified 1 coefficients.
Reduced MIP has 285 rows, 329 columns, and 792 nonzeros.
Reduced MIP has 261 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.03 sec. (0.58 ticks)
Represolve time = 0.18 sec. (19.83 ticks)
Probing time = 0.00 sec. (0.18 ticks)
Clique table members: 547.
MIP emphasis: balance optimality and feasibility.
MIP search method: dynamic search.
Parallel mode: deterministic, using up to 4 threads.
Root relaxation solution time = 0.00 sec. (1.32 ticks)
Nodes Cuts/
Node Left Objective IInf Best Integer Best Bound ItCnt Gap
* 0+ 0 -20935.7200 -20949.2232 0.06%
0 0 -20950.6138 31 -20935.7200 -20949.2232 1635 0.06%
0 0 -20946.9943 31 -20935.7200 Cuts: 27 1650 0.05%
0 0 -20946.9195 31 -20935.7200 MIRcuts: 4 1653 0.05%
0 0 -20945.8979 31 -20935.7200 Cuts: 11 1658 0.05%
0 0 -20945.8979 31 -20935.7200 Flowcuts: 2 1659 0.05%
0 0 -20945.8979 31 -20935.7200 Flowcuts: 1 1663 0.05%
Detecting symmetries...
Clique cuts applied: 10
Implied bound cuts applied: 3
Flow cuts applied: 35
Mixed integer rounding cuts applied: 37
Lift and project cuts applied: 3
Gomory fractional cuts applied: 1
Root node processing (before b&c):
Real time = 1.34 sec. (232.86 ticks)
Parallel b&c, 4 threads:
Real time = 0.00 sec. (0.00 ticks)
Sync time (average) = 0.00 sec.
Wait time (average) = 0.00 sec.
------------
Total (root+branch&cut) = 1.34 sec. (232.86 ticks)
从给定的运行。我从this知道如何将参数通过cvxpy传递到cplex，但是读取求解器的输出并不能帮助我确定求解器是否由于内存问题，数值问题或这样的事情，并因此调整参数。我还要指出的是，我使用的约束集很大（每个数据点可以达到34个约束），但求解器在很小的数据帧（仅24个点）上仍然会失败]
有什么建议/材料可以帮助我吗？
非常感谢！
您可以使用状态。让我使用bus example。
# Import packages.
import cvxpy as cp
# Define and solve the CVXPY problem.
nbBus40 = cp.Variable(integer=True)
nbBus30 = cp.Variable( integer=True)
cost = 500*nbBus40+400*nbBus30
prob = cp.Problem(cp.Minimize(cost),[40*nbBus40+30*nbBus30>=300,
nbBus40>=0,nbBus30>=0
])
prob.solve(solver=cp.CPLEX,verbose=True)
print("status = ",prob.status)
# Print result.
print("\nThe minimal cost is", prob.value)
print("number buses 40 seats = ",nbBus40.value)
print("number buses 30 seats = ",nbBus30.value)
给予
status = optimal
The minimal cost is 3800.0
number buses 40 seats = 6.0
number buses 30 seats = 2.0
而我们将约束更改为
prob = cp.Problem(cp.Minimize(cost),[40*nbBus40+30*nbBus30>=300,
nbBus40>=0,nbBus30>=0,nbBus40<=2,nbBus30<=2
])
然后我们得到
status = infeasible
The minimal cost is inf
number buses 40 seats = None
number buses 30 seats = None