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Local Rapid Learning for Integer Programs

Timo Berthold(timoberthold***at***fico.com)
Peter J. Stuckey(Peter.Stuckey***at***monash.edu)
Jakob Witzig(witzig***at***zib.de)

Abstract: 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 Rapid Learning 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.

Keywords: integer programming, conflict analysis, no-good learning, hybrid solver, dual ray

Category 1: Integer Programming

Citation: ZR-18-56, ZIB, Takustr. 7, 14195 Berlin, 2018

Download: [PDF]

Entry Submitted: 01/18/2019
Entry Accepted: 01/18/2019
Entry Last Modified: 01/18/2019

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