Towards learning integral strategy of branch and bound

Titre	Towards learning integral strategy of branch and bound
Publication Type	Conference Paper
Year of Publication	2017
Authors	Kabbaj, MM, A. Afia, E
Conference Name	International 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.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019169526&doi=10.1109%2fICMCS.2016.7905626&partnerID=40&md5=8e62cf880e3eed29131a425775a7fa21
DOI	10.1109/ICMCS.2016.7905626