Neighborhood Persistency of the Linear Optimization Relaxation of Integer Linear Optimization

Kei Kimura, Kotaro Nakayama

研究成果: 書籍/レポート タイプへの寄稿会議への寄与


For an integer linear optimization (ILO) problem, persistency of its linear optimization (LO) relaxation is a property that for every optimal solution of the relaxation that assigns integer values to some variables, there exists an optimal solution of the ILO problem in which these variables retain the same values. Although persistency has been used to develop heuristic, approximation, and fixed-parameter algorithms for special cases of ILO, its applicability remains unknown in the literature. In this paper, we propose a stronger property called neighborhood persistency and show that the LO relaxation of ILO on unit-two-variable-per-inequality (UTVPI) systems is a maximal class of ILO such that its LO relaxation has (neighborhood) persistency. Our result on neighborhood persistency generalizes the previous results of Nemhauser and Trotter, Hochbaum et al., and Fiorini et al., and implies fixed-parameter tractability and two-approximability for ILO on UTVPI systems where the objective function and the variables are non-negative.

ホスト出版物のタイトルCombinatorial Optimization - 7th International Symposium, ISCO 2022, Revised Selected Papers
編集者Ivana Ljubić, Francisco Barahona, Santanu S. Dey, A. Ridha Mahjoub
出版社Springer Science and Business Media Deutschland GmbH
出版ステータス出版済み - 2022
イベント7th International Symposium on Combinatorial Optimization, ISCO 2022 - Virtual, Online
継続期間: 5月 18 20225月 20 2022


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13526 LNCS


会議7th International Symposium on Combinatorial Optimization, ISCO 2022
CityVirtual, Online

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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