{"title":"Parallelization of a branch-and-bound algorithm for the maximum weight clique problem","authors":"Satoshi Shimizu, Kazuaki Yamaguchi, Sumio Masuda","doi":"10.1016/j.disopt.2021.100646","DOIUrl":null,"url":null,"abstract":"<div><p><span>In this paper, parallelization techniques are proposed for the branch-and-bound algorithm OTClique for the maximum weight </span>clique<span> problem. OTClique consists of the precomputation phase and the branch-and-bound phase. The proposed algorithm parallelizes both of them. In the precomputation phase, the construction of optimal tables is parallelized. In the branch-and-bound phase, the proposed algorithm generates small subproblems<span> and assigns them to threads. A technique to share lower and upper bounds is also proposed. Experiments using some benchmarks show that the proposed parallelization techniques improve the performance of OTClique. With an 8-core CPU, the computation time of OTClique becomes 6.91 times shorter on random graphs and 5.38 times on DIMACS benchmarks on average.</span></span></p></div>","PeriodicalId":50571,"journal":{"name":"Discrete Optimization","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.disopt.2021.100646","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discrete Optimization","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1572528621000256","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, parallelization techniques are proposed for the branch-and-bound algorithm OTClique for the maximum weight clique problem. OTClique consists of the precomputation phase and the branch-and-bound phase. The proposed algorithm parallelizes both of them. In the precomputation phase, the construction of optimal tables is parallelized. In the branch-and-bound phase, the proposed algorithm generates small subproblems and assigns them to threads. A technique to share lower and upper bounds is also proposed. Experiments using some benchmarks show that the proposed parallelization techniques improve the performance of OTClique. With an 8-core CPU, the computation time of OTClique becomes 6.91 times shorter on random graphs and 5.38 times on DIMACS benchmarks on average.
期刊介绍:
Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.