亚模态函数与完美图形

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED Mathematics of Operations Research Pub Date : 2024-02-15 DOI:10.1287/moor.2021.0302
Tara Abrishami, Maria Chudnovsky, Cemil Dibek, Kristina Vušković
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引用次数: 0

摘要

我们给出了一种组合多项式时间算法,可以在不包含长度为四的棱或洞作为诱导子图的有界度完美图中找到最大权重独立集。图中的偶数对是指一对顶点之间的所有诱导路径都是偶数。偶数集是每两个顶点都是偶数对的顶点集。我们证明,每一个不包含棱柱或长度为四的洞的完美图的诱导子图都有一个平衡分隔符,它是有界数的偶数集的联合,而界数只取决于图的最大度。这样,我们就可以使用著名的子模函数最小化算法来解决最大权重独立集问题:本研究得到了工程与物理科学研究委员会 [EP/V002813/1 号基金]、美国国家科学基金会 [DMS-1763817 号基金、DMS-2120644 号基金和 DMS-2303251 号基金] 以及 Alexander von Humboldt-Stiftung 的资助。
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Submodular Functions and Perfect Graphs
We give a combinatorial polynomial-time algorithm to find a maximum weight independent set in perfect graphs of bounded degree that do not contain a prism or a hole of length four as an induced subgraph. An even pair in a graph is a pair of vertices all induced paths between which are even. An even set is a set of vertices every two of which are an even pair. We show that every perfect graph that does not contain a prism or a hole of length four as an induced subgraph has a balanced separator which is the union of a bounded number of even sets, where the bound depends only on the maximum degree of the graph. This allows us to solve the maximum weight independent set problem using the well-known submodular function minimization algorithm.Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/V002813/1]; the National Science Foundation [Grants DMS-1763817, DMS-2120644, and DMS-2303251]; and Alexander von Humboldt-Stiftung.
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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