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arXiv 2601.00560cs.CC
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寻找局部极值的参数复杂度框架

A Parameterized-Complexity Framework for Finding Local Optima

TU Wien(维也纳工业大学);Friedrich Schiller University Jena(耶拿弗里德里希·席勒大学);Université de Bordeaux(波尔多大学)

Robert Ganian, Hung P. Hoang, Christian Komusiewicz, Nils Morawietz

AI总结:

本文提出了一种新的参数复杂度框架,用于寻找局部最优解,通过结合参数复杂度范式,实现了强下界和可处理性结果。

摘要:

局部搜索是一种基础的优化技术,它在实践中被广泛使用,在理论中也受到深入研究,但其计算复杂性仍缺乏充分理解。传统的框架,即PLS和标准算法问题,由Johnson、Papadimitriou和Yannakakis(1988)提出,未能捕捉到局部搜索算法的方法:PLS关注于找到局部最优解,而不是使用局部搜索,而标准算法问题限制每个改进步骤必须遵循固定的旋转规则。在本工作中,我们引入了一种新的局部搜索形式,它在这些模型之间提供了一个中间点。特别是,任务是输出不仅一个局部最优解,还有导致它的局部改进链。通过这个框架,我们旨在捕捉设计良好旋转规则的挑战。特别是,当结合参数复杂度范式时,它能够同时提供强的下界和有意义的可处理性结果。与以往将参数复杂度与局部搜索结合的工作不同,我们的框架瞄准整个寻找局部最优解的任务,而不仅仅是单个改进步骤。聚焦于两个代表性的元问题——具有c-交换邻域的子集加权优化问题和具有翻转邻域的加权电路问题——我们建立了与不同权重数量相关的固定参数可处理性结果,同时通过一种新的类型减少排除了当参数化为最近最优解的距离时的类似结果。

英文摘要:

Local search is a fundamental optimization technique that is both widely used in practice and deeply studied in theory, yet its computational complexity remains poorly understood. The traditional frameworks, PLS and the standard algorithm problem, introduced by Johnson, Papadimitriou, and Yannakakis (1988) fail to capture the methodology of local search algorithms: PLS is concerned with finding a local optimum and not with using local search, while the standard algorithm problem restricts each improvement step to follow a fixed pivoting rule. In this work, we introduce a novel formulation of local search which provides a middle ground between these models. In particular, the task is to output not only a local optimum but also a chain of local improvements leading to it. With this framework, we aim to capture the challenge in designing a good pivoting rule. Especially, when combined with the parameterized complexity paradigm, it enables both strong lower bounds and meaningful tractability results. Unlike previous works that combined parameterized complexity with local search, our framework targets the whole task of finding a local optimum and not only a single improvement step. Focusing on two representative meta-problems -- Subset Weight Optimization Problem with the $c$-swap neighborhood and Weighted Circuit with the flip neighborhood -- we establish fixed-parameter tractability results related to the number of distinct weights, while ruling out an analogous result when parameterized by the distance to the nearest optimum via a new type of reduction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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