A local branching heuristic for MINLPs

dc.creatorNannicini, Giacomo
dc.creatorBelotti, Pietro
dc.creatorLiberti, Leo
dc.date2008-12-11
dc.date.accessioned2026-07-07T12:12:03Z
dc.date.available2026-07-07T12:12:03Z
dc.descriptionLocal branching is an improvement heuristic, developed within the context of branch-and-bound algorithms for MILPs, which has proved to be very effective in practice. For the binary case, it is based on defining a neighbourhood of the current incumbent solution by allowing only a few binary variables to flip their value, through the addition of a local branching constraint. The neighbourhood is then explored with a branch-and-bound solver. We propose a local branching scheme for (nonconvex) MINLPs which is based on iteratively solving MILPs and NLPs. Preliminary computational experiments show that this approach is able to improve the incumbent solution on the majority of the test instances, requiring only a short CPU time. Moreover, we provide algorithmic ideas for a primal heuristic whose purpose is to find a first feasible solution, based on the same scheme.
dc.identifierhttps://arxiv.org/abs/0812.2188
dc.identifierhttp://arxiv.org/abs/0812.2188
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210421
dc.subjectCombinatorics
dc.subjectOptimization and Control
dc.subject90C57; 90C59
dc.titleA local branching heuristic for MINLPs
dc.typetext

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