Towards learning integral strategy of branch and bound

TitreTowards learning integral strategy of branch and bound
Publication TypeConference Paper
Year of Publication2017
AuthorsKabbaj, MM, A. Afia, E
Conference NameInternational Conference on Multimedia Computing and Systems -Proceedings
Abstract

Branch and bound is the preferred algorithm used for solving MILP problems. It involves two fundamental strategies that are node selection strategy and branching strategy. Whereas the learning literature has been focused in dealing with just one strategy on the same time, we design a two-in-one strategy of branch and bound algorithm regarding the fact that are intuitively dependent. To do so, we apply the well-known SVM algorithm to the well-known set of problems MIPLIP. © 2016 IEEE.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85019169526&doi=10.1109%2fICMCS.2016.7905626&partnerID=40&md5=8e62cf880e3eed29131a425775a7fa21
DOI10.1109/ICMCS.2016.7905626
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