Reoptimization Techniques in MIP Solvers

  • Recently, there have been many successful applications of optimization algorithms that solve a sequence of quite similar mixed-integer programs (MIPs) as subproblems. Traditionally, each problem in the sequence is solved from scratch. In this paper we consider reoptimization techniques that try to benefit from information obtained by solving previous problems of the sequence. We focus on the case that subsequent MIPs differ only in the objective function or that the feasible region is reduced. We propose extensions of the very complex branch-and-bound algorithms employed by general MIP solvers based on the idea to ``warmstart'' using the final search frontier of the preceding solver run. We extend the academic MIP solver SCIP by these techniques to obtain a reoptimizing branch-and-bound solver and report computational results which show the effectiveness of the approach.

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Metadaten
Author:Gerald Gamrath, Benjamin Hiller, Jakob Witzig
Document Type:Book chapter
Volume:9125
First Page:181
Last Page:192
Series:Experimental Algorithms, Lecture Notes in Computer Science
Publisher:Springer
Year of first publication:2015
ISBN:978-3-319-20086-6
Preprint:urn:nbn:de:0297-zib-54375
DOI:http://dx.doi.org/10.1007/978-3-319-20086-6_14

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