块结构整数程序的紧下界。

IF 2.1 2区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Mathematical Programming Pub Date : 2026-01-01 Epub Date: 2025-10-27 DOI:10.1007/s10107-025-02296-z
Christoph Hunkenschröder, Kim-Manuel Klein, Martin Koutecký, Alexandra Lassota, Asaf Levin
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引用次数: 0

摘要

我们研究基本的块结构整数程序,称为树形和多阶段ip。树形ip有一个约束矩阵,由几个独立的块以递归模式连接在一起。该约束矩阵的转置得到了多阶段ip的约束矩阵。解决这些ip的最先进算法在运行时间上存在指数差距,因此人们很自然地会问这种差距是否是固有的。我们对这个问题的回答是肯定的。在指数时间假设下,我们证明了下界,表明指数差是必要的。这也证明了已知算法本质上是最优的。此外,我们证明了Graver基元素大小的无条件下界,这是解决这些ip的所有已知算法的基本构建块。这表明,目前的任何一种方法都不能无条件地超越这个界限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tight lower bounds for block-structured integer programs.

We study fundamental block-structured integer programs called tree-fold and multi-stage IPs. Tree-fold IPs have a constraint matrix with independent blocks linked together by few constraints in a recursive pattern. Transposing this constraint matrix yields the constraint matrix of multi-stage IPs. The state-of-the-art algorithms to solve these IPs have an exponential gap in their running times, making it natural to ask whether this gap is inherent. We answer this question in the affirmative. Assuming the Exponential Time Hypothesis, we prove lower bounds showing that the exponential difference is necessary. This also proves that the known algorithms are essentially optimal. Moreover, we prove unconditional lower bounds on the size of the Graver basis elements, a fundamental building block of all known algorithms to solve these IPs. This shows that none of the current approaches can be improved beyond this bound unconditionally.

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来源期刊
Mathematical Programming
Mathematical Programming 数学-计算机:软件工程
CiteScore
5.70
自引率
11.10%
发文量
160
审稿时长
4-8 weeks
期刊介绍: Mathematical Programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variables, often subject to a set of constraints. This involves theoretical and computational issues as well as application studies. Included, along with the standard topics of linear, nonlinear, integer, conic, stochastic and combinatorial optimization, are techniques for formulating and applying mathematical programming models, convex, nonsmooth and variational analysis, the theory of polyhedra, variational inequalities, and control and game theory viewed from the perspective of mathematical programming.
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