Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/71437
Type: Artigo de periódico
Title: SEMIDEFINITE PROGRAMMING BASED ALGORITHMS FOR THE SPARSEST CUT PROBLEM
Author: Meira, LAA
Miyazawa, FK
Abstract: In this paper we analyze a known relaxation for the Sparsest Cut problem based on positive semidefinite constraints, and we present a branch and bound algorithm and heuristics based on this relaxation. The relaxed formulation and the algorithms were tested on small and moderate sized instances. It leads to values very close to the optimum solution values. The exact algorithm could obtain solutions for small and moderate sized instances, and the best heuristics obtained optimum or near optimum solutions for all tested instances. The semidefinite relaxation gives a lower bound C/W and each heuristic produces a cut S with a ratio c(S)/omega(S) where either cs is at most a factor of C or omega(S) is at least a factor of W. We solved the semidefinite relaxation using a semi-infinite cut generation with a commercial linear programming package adapted to the sparsest cut problem. We showed that the proposed strategy leads to a better performance compared to the use of a. known semidefinite programming solver.
Subject: Semidefinite programming
Sparsest Cut
combinatorics
Country: EUA
Editor: Cambridge Univ Press
Rights: aberto
Identifier DOI: 10.1051/ro/2011104
Date Issue: 2011
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

Files in This Item:
File Description SizeFormat 
WOS000296657000001.pdf669.79 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.