I have a very large problem (1m variables, 3m constraints) that I'm trying to solve using CPLEX in Python. Obviously, solving it optimally is out of the question, so I have implemented a time limit so I could get the best solution within that time.
c.timelimit.set(7200)
However, when the time was up, it hadn't even finished processing the root node:
Nodes Cuts/
Node Left Objective IInf Best Integer Best Bound ItCnt Gap
0 0 10.0070 2177 10.0070 65714
0 0 10.0070 1793 Cuts: 29 80275
0 0 10.0070 2427 Cuts: 6928 101277
0 0 10.0070 2061 Cuts: 4737 122524
Implied bound cuts applied: 485
Flow cuts applied: 68
Mixed integer rounding cuts applied: 12019
Zero-half cuts applied: 193
Root node processing (before b&c):
Real time = 7200.47 sec. (2762999.79 ticks)
Parallel b&c, 112 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) = 7200.47 sec. (2762999.79 ticks)
Solution status = 108 :
MIP_time_limit_infeasible
Exception raised during solve
Is there any way to limit the time spent on the root node so it at least starts the b&c method? Would that even be advantageous at all?