[SCIP] maximizing the compute time spent on heuristics for a MINLP problem

[Marcus Daniels]

Hi all,

I have a nonconvex MINLP problem where I'd like to combine the mutation heuristic with either the multistart heuristic or the subnlp heuristic.   I know that IPOPT can solve this problem to local optimality very easily.  The integer part of the problem, if any, can be thought of the high-order bits of the bounded variables in the problem.   So by using with mutation with those, my intuition is that I'll get something like the multistart heuristic.

The problem has many nonlinear constraints and a smaller number of linear constraints.   The lower bound (minimization) is poor, so I'd like to get a value for the root node, and then forget about LP solves.    What I observe is that a huge amount of time goes into separation cuts and then it looks like (from running in GDB) that there is a lot of time that goes into constraint enforcement and propagation.  What I'd like to do is run parascip with something like the multistart heuristic on many cores, and benefit from the efficiency of IPOPT.    I can't find a way to disable the cut generation without setting the LP solverfreq to -1.   I don't believe there is any simple way to improve the lower bound.

Any suggestions on how to configure SCIP settings to make most of the compute time go into IPOPT?  (Or if I am thinking about this wrong, that would be useful too.)

Thanks!

Marcus


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