Convexifying multilinear sets with cardinality constraints: Structural properties, nested case and extensions

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED
Rui Chen , Sanjeeb Dash , Oktay Günlük
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引用次数: 2

Abstract

The problem of minimizing a multilinear function of binary variables is a well-studied NP-hard problem. The set of solutions of the standard linearization of this problem is called the multilinear set. We study a cardinality constrained version of it with upper and lower bounds on the number of nonzero variables. We call the set of solutions of the standard linearization of this problem a multilinear set with cardinality constraints. We characterize a set of conditions on these multilinear terms (called properness) and observe that under these conditions the convex hull description of the set is tractable via an extended formulation. We then give an explicit polyhedral description of the convex hull when the multilinear terms have a nested structure. Our description has an exponential number of inequalities which can be separated in polynomial time. Finally, we generalize these inequalities to obtain valid inequalities for the general case.

具有基数约束的凸化多线性集:结构性质,嵌套情况和扩展
二元变量的多重线性函数的最小化问题是一个研究得很好的NP难问题。这个问题的标准线性化的解集称为多线性集。我们研究了它的基数约束版本,它具有非零变量数量的上界和下界。我们把这个问题的标准线性化的解集称为具有基数约束的多线性集。我们刻画了这些多线性项上的一组条件(称为适当性),并观察到在这些条件下,该集的凸包描述是可通过扩展公式处理的。然后,当多线性项具有嵌套结构时,我们给出了凸包的显式多面体描述。我们的描述具有指数数量的不等式,这些不等式可以在多项式时间内分离。最后,我们将这些不等式推广到一般情况下的有效不等式。
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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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