I am using CPLEX with Julia using the package JuMP to solve a MIP problem. In a small instance, I have tested my problem but, after 10 minutes, nothing happens. I have defined the following parameters in CPLEX:
set_optimizer_attribute(modelo,"CPX_PARAM_ZEROHALFCUTS",2)-->> Generate zero-half cuts aggressively
set_optimizer_attribute(modelo,"CPXPARAM_MIP_Cuts_Covers",2)-->> Generate cover cuts aggressively
set_optimizer_attribute(modelo,"CPXPARAM_MIP_Strategy_SubAlgorithm",1)-->> Emphasize feasibility over optimality
The branch-and-bound does not even begin. Look the CPLEX report:
CPXPARAM_TimeLimit 600 CPXPARAM_MIP_Cuts_Covers 2 CPXPARAM_MIP_Tolerances_MIPGap 0.01 CPXPARAM_MIP_Strategy_SubAlgorithm 1 CPXPARAM_MIP_Cuts_ZeroHalfCut 2 Warning: Non-integral bounds for integer variables rounded. Warning: No solution found from 1 MIP starts. Retaining values of one MIP start for possible repair. Root node processing (before b&c): Real time = 600.11 sec. (206099.96 ticks) Parallel b&c, 12 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) = 600.11 sec. (206099.96 ticks)
In another test, I used all of the default parameters but the result was the same.
My problem does not have any big-$M$ usage in the formulation.
Is there a combination of CPLEX parameters that accelerate obtaining an integer feasible solution?