A note on fast deterministic algorithms for non-monotone submodular maximization under a knapsack constraint

IF 0.9 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-04-04 DOI:10.1016/j.orl.2025.107295

Cheng Lu

引用次数: 0

Abstract

We present a refined analysis of a variant of the algorithm in the literature for solving the knapsack-constrained submodular maximization problem. By deriving a strong approximation bound for this variant, we reduce the size of the sets requiring enumeration, from two to one, to ensure the final algorithm achieves 1/4-approximation. As a result, we obtain the fastest deterministic algorithm so far which achieves an approximation ratio of 1/4 for the problem.

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背包约束下非单调次模最大化的快速确定性算法

我们提出了一种改进的分析算法的变体，在文献中用于解决背包约束的次模最大化问题。通过推导这种变体的强逼近界，我们将需要枚举的集合的大小从两个减少到一个，以确保最终算法达到1/4逼近。结果，我们得到了迄今为止最快的确定性算法，对该问题达到了1/4的近似比。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.