Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Gerald GamrathORCiD, Benjamin HillerORCiD, Jakob WitzigORCiD
Document Type:In Proceedings
Volume:9125
First Page:181
Last Page:192
Series:Experimental Algorithms, Lecture Notes in Computer Science
Publisher:Springer
Year of first publication:2015
Preprint:urn:nbn:de:0297-zib-54375
DOI:https://doi.org/10.1007/978-3-319-20086-6_14
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.