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 aggressivelyset_optimizer_attribute(modelo,"CPXPARAM_MIP_Cuts_Covers",2)
-->> Generate cover cuts aggressivelyset_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?