带指示变量的低域函数的紧凑扩展公式

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED Mathematics of Operations Research Pub Date : 2024-08-12 DOI:10.1287/moor.2021.0281
Shaoning Han, Andrés Gómez
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引用次数: 0

摘要

我们研究了一类特殊凸函数的混合整数外延,这类凸函数通常带有非凸指标约束,用于对解的支持施加逻辑约束。我们考虑的这一类函数被定义为低维非线性函数与仿射函数的组合。描述此类集合凸壳的扩展公式可以很容易地通过析取编程法构建,但直接应用这种方法往往会产生过大的公式,其大小与变量数量成指数关系。在本文中,我们为所研究的集合提出了一种新的分条件表示法,它可以得到紧凑的公式,其大小与非线性函数的维度成指数关系,但与变量的数量成多项式关系。此外,我们还展示了如何在维数为一的情况下投影出额外变量,从而恢复或推广针对此类集合凸壳(在原始变量空间中)的已知结果。我们的计算结果表明,所提出的方法可以显著提高结构化问题求解器的性能:这项工作得到了美国国家科学基金会计算与通信基础部[Grant 2006762]的支持。
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Compact Extended Formulations for Low-Rank Functions with Indicator Variables
We study the mixed-integer epigraph of a special class of convex functions with nonconvex indicator constraints, which are often used to impose logical constraints on the support of the solutions. The class of functions we consider are defined as compositions of low-dimensional nonlinear functions with affine functions. Extended formulations describing the convex hull of such sets can easily be constructed via disjunctive programming although a direct application of this method often yields prohibitively large formulations, whose size is exponential in the number of variables. In this paper, we propose a new disjunctive representation of the sets under study, which leads to compact formulations with size exponential in the dimension of the nonlinear function but polynomial in the number of variables. Moreover, we show how to project out the additional variables for the case of dimension one, recovering or generalizing known results for the convex hulls of such sets (in the original space of variables). Our computational results indicate that the proposed approach can significantly improve the performance of solvers in structured problems.Funding: This work was supported by the National Science Foundation Division of Computing and Communication Foundations [Grant 2006762].
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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