Analysis of the two-for-one swap heuristic for approximating the maximum independent set in a k-polymatroid

IF 0.9 4区 管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Letters Pub Date : 2024-12-03 DOI:10.1016/j.orl.2024.107217
Adrian Calinescu , Gruia Călinescu
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Abstract

Let f:2NZ+ be a polymatroid (an integer-valued non-decreasing submodular set function with f()=0). A k-polymatroid satisfies that f(e)k for all eN. We call SN independent if f(S)=eSf(e) and f(e)>0 for all eS. Such a set was also called a matching. Finding a maximum-size independent set in a 2-polymatroid has been studied and polynomial-time algorithms are known for linear polymatroids. For k3, the problem is NP-hard, and a ((2/k)ϵ)-approximation is known and is obtained by swapping as long as possible a subset of up to (1/ϵ)logk1(2k+1) elements from the current solution by a set with one more element.
Here we give a simple analysis of the more particular two-for-one repeated swapping heuristic, obtaining a tight (weaker) (2/(k+1))-approximation.
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近似k-多边形中最大独立集的二对一交换启发式分析
设f:2N→Z+为一个多矩阵(f(∅)=0的整数非递减子模集函数)。对于所有e∈N, k-多阵满足f(e)≤k。如果f(S)=∑e∈Sf(e),且对于所有e∈S, f(e)>0,则称S是独立的。这样的组合也被称为配对。在2-多拟体中寻找最大大小的独立集已经被研究过,对于线性多拟体,多项式时间算法是已知的。对于k≥3,问题是np困难的,并且(2/k)−λ)近似是已知的,并且通过尽可能长时间地将最多(1/ λ)logk−1 (2k+1)个元素的子集与当前解交换到一个多一个元素的集合来获得。在这里,我们对更特殊的二对一重复交换启发式进行了简单的分析,获得了一个紧密(较弱)的(2/(k+1))近似。
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来源期刊
Operations Research Letters
Operations Research Letters 管理科学-运筹学与管理科学
CiteScore
2.10
自引率
9.10%
发文量
111
审稿时长
83 days
期刊介绍: Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.
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