我和GUROBI一起在python中使用“纸浆”来解决一些优化问题。例如,GUROBI的计算日志是:
Optimize a model with 12 rows, 25 columns and 39 nonzeros
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [1e+00, 1e+00]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 1e+00]
Found heuristic solution: objective 12
Presolve removed 3 rows and 12 columns
Presolve time: 0.00s
Presolved: 9 rows, 13 columns, 27 nonzeros
Variable types: 0 continuous, 13 integer (13 binary)
Root relaxation: objective 7.000000e+00, 11 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 7.0000000 7.00000 0.00% - 0s
Explored 0 nodes (11 simplex iterations) in 0.00 seconds
Thread count was 4 (of 4 available processors)
Optimal solution found (tolerance 1.00e-04)
Best objective 7.000000000000e+00, best bound 7.000000000000e+00, gap 0.0%
('Gurobi status=', 2)
我想禁用此输出,因为我将解决800k优化问题并在输出中写入这些日志会使我的代码太慢。是否有任何禁用这些日志的想法?
答案 0 :(得分:1)
我刚刚发现我们如何做到这一点: 而不是默认的调用Gurobi的方式,即:
pulp.GUROBI().solve(prob)
我们需要写:
pulp.GUROBI(msg=0).solve(prob)
答案 1 :(得分:0)
m0_as的回答是正确的。只是想分享更多细节。在PuLP库中,每个求解器类都派生自相同的基类LpSolver。
class LpSolver:
"""A generic LP Solver"""
def __init__(self, mip = True, msg = True, options = [], *args, **kwargs):
self.mip = mip
self.msg = msg
self.options = options
在构造函数中,这些是msg参数,它定义是否要将信息从求解器回显到stdout。这是class GUROBI
的实现见第1774行
@param msg: displays information from the solver to stdout
您可以非常轻松地在此源文件中轻松找到其他求解器特定参数。
答案 2 :(得分:0)
您需要显式声明一个求解器,因此请替换此行
p.solve()
与此
p.solve(PULP_CBC_CMD(msg=0))
或用您正在使用的任何求解器替换`PULP_CBC_CMD。