Advances on strictly Δ -modular IPs.

IF 2.2 2区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Mathematical Programming Pub Date : 2025-01-01 Epub Date: 2024-10-30 DOI:10.1007/s10107-024-02148-2
Martin Nägele, Christian Nöbel, Richard Santiago, Rico Zenklusen
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引用次数: 0

Abstract

There has been significant work recently on integer programs (IPs) min { c x : A x b , x Z n } with a constraint marix A with bounded subdeterminants. This is motivated by a well-known conjecture claiming that, for any constant Δ Z > 0 , Δ -modular IPs are efficiently solvable, which are IPs where the constraint matrix A Z m × n has full column rank and all n × n minors of A are within { - Δ , , Δ } . Previous progress on this question, in particular for Δ = 2 , relies on algorithms that solve an important special case, namely strictly Δ -modular IPs, which further restrict the n × n minors of A to be within { - Δ , 0 , Δ } . Even for Δ = 2 , such problems include well-known combinatorial optimization problems like the minimum odd/even cut problem. The conjecture remains open even for strictly Δ -modular IPs. Prior advances were restricted to prime Δ , which allows for employing strong number-theoretic results. In this work, we make first progress beyond the prime case by presenting techniques not relying on such strong number-theoretic prime results. In particular, our approach implies that there is a randomized algorithm to check feasibility of strictly Δ -modular IPs in strongly polynomial time if Δ 4 .

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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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