5 research outputs found
Local Rapid Learning for Integer Programs
Conflict learning algorithms are an important component of modern MIP and CP
solvers. But strong conflict information is typically gained by depth-first
search. While this is the natural mode for CP solving, it is not for MIP
solving. Rapid Learning is a hybrid CP/MIP approach where CP search is applied
at the root to learn information to support the remaining MIP solve. This has
been demonstrated to be beneficial for binary programs. In this paper, we
extend the idea of Rapid Learning to integer programs, where not all variables
are restricted to the domain {0,1}, and rather than just running a rapid CP
search at the root, we will apply it repeatedly at local search nodes within
the MIP search tree. To do so efficiently, we present six heuristic criteria to
predict the chance for local \rapidlearning to be successful. Our computational
experiments indicate that our extended Rapid Learning algorithm significantly
speeds up MIP search and is particularly beneficial on highly dual degenerate
problems