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dc.contributor.authorSamer, Phillippe
dc.contributor.authorHaugland, Dag
dc.date.accessioned2022-12-19T12:05:54Z
dc.date.available2022-12-19T12:05:54Z
dc.date.created2022-11-28T11:49:21Z
dc.date.issued2023
dc.identifier.issn1862-4472
dc.identifier.urihttps://hdl.handle.net/11250/3038498
dc.description.abstractGiven a graph G=(V,E) and a set C of unordered pairs of edges regarded as being in conflict, a stable spanning tree in G is a set of edges T inducing a spanning tree in G, such that for each {ei,ej}∈C, at most one of the edges ei and ej is in T. The existing work on Lagrangean algorithms to the NP-hard problem of finding minimum weight stable spanning trees is limited to relaxations with the integrality property. We exploit a new relaxation of this problem: fixed cardinality stable sets in the underlying conflict graph H=(E,C). We find interesting properties of the corresponding polytope, and determine stronger dual bounds in a Lagrangean decomposition framework, optimizing over the spanning tree polytope of G and the fixed cardinality stable set polytope of H in the subproblems. This is equivalent to dualizing exponentially many subtour elimination constraints, while limiting the number of multipliers in the dual problem to |E|. It is also a proof of concept for combining Lagrangean relaxation with the power of integer programming solvers over strongly NP-hard subproblems. We present encouraging computational results using a dual method that comprises the Volume Algorithm, initialized with multipliers determined by Lagrangean dual-ascent. In particular, the bound is within 5.5% of the optimum in 146 out of 200 benchmark instances; it actually matches the optimum in 75 cases. All of the implementation is made available in a free, open-source repository.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePolyhedral results and stronger Lagrangean bounds for stable spanning treesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1007/s11590-022-01949-8
dc.identifier.cristin2082416
dc.source.journalOptimization Lettersen_US
dc.source.pagenumber1317-1335
dc.identifier.citationOptimization Letters. 2023, 17, 1317-1335.en_US
dc.source.volume17


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