{"title":"最大同质无二次型集合的表征","authors":"Gonzalo Muñoz, Joseph Paat, Felipe Serrano","doi":"10.1007/s10107-024-02092-1","DOIUrl":null,"url":null,"abstract":"<p>The intersection cut framework was introduced by Balas in 1971 as a method for generating cutting planes in integer optimization. In this framework, one uses a full-dimensional convex <i>S</i>-free set, where <i>S</i> is the feasible region of the integer program, to derive a cut separating <i>S</i> from a non-integral vertex of a linear relaxation of <i>S</i>. Among all <i>S</i>-free sets, it is the inclusion-wise maximal ones that yield the strongest cuts. Recently, this framework has been extended beyond the integer case in order to obtain cutting planes in non-linear settings. In this work, we consider the specific setting when <i>S</i> is defined by a homogeneous quadratic inequality. In this ‘quadratic-free’ setting, every function <span>\\(\\Gamma : D^m \\rightarrow D^n\\)</span>, where <span>\\(D^k\\)</span> is the unit sphere in <span>\\(\\mathbb {R}^k\\)</span>, generates a representation of a quadratic-free set. While not every <span>\\(\\Gamma \\)</span> generates a maximal quadratic free set, it is the case that every full-dimensional maximal quadratic free set is generated by some <span>\\(\\Gamma \\)</span>. Our main result shows that the corresponding quadratic-free set is full-dimensional and maximal if and only if <span>\\(\\Gamma \\)</span> is non-expansive and satisfies a technical condition. This result yields a broader class of maximal <i>S</i>-free sets than previously known. Our result stems from a new characterization of maximal <i>S</i>-free sets (for general <i>S</i> beyond the quadratic setting) based on sequences that ‘expose’ inequalities defining the <i>S</i>-free set.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"25 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A characterization of maximal homogeneous-quadratic-free sets\",\"authors\":\"Gonzalo Muñoz, Joseph Paat, Felipe Serrano\",\"doi\":\"10.1007/s10107-024-02092-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The intersection cut framework was introduced by Balas in 1971 as a method for generating cutting planes in integer optimization. In this framework, one uses a full-dimensional convex <i>S</i>-free set, where <i>S</i> is the feasible region of the integer program, to derive a cut separating <i>S</i> from a non-integral vertex of a linear relaxation of <i>S</i>. Among all <i>S</i>-free sets, it is the inclusion-wise maximal ones that yield the strongest cuts. Recently, this framework has been extended beyond the integer case in order to obtain cutting planes in non-linear settings. In this work, we consider the specific setting when <i>S</i> is defined by a homogeneous quadratic inequality. In this ‘quadratic-free’ setting, every function <span>\\\\(\\\\Gamma : D^m \\\\rightarrow D^n\\\\)</span>, where <span>\\\\(D^k\\\\)</span> is the unit sphere in <span>\\\\(\\\\mathbb {R}^k\\\\)</span>, generates a representation of a quadratic-free set. While not every <span>\\\\(\\\\Gamma \\\\)</span> generates a maximal quadratic free set, it is the case that every full-dimensional maximal quadratic free set is generated by some <span>\\\\(\\\\Gamma \\\\)</span>. Our main result shows that the corresponding quadratic-free set is full-dimensional and maximal if and only if <span>\\\\(\\\\Gamma \\\\)</span> is non-expansive and satisfies a technical condition. This result yields a broader class of maximal <i>S</i>-free sets than previously known. 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引用次数: 0
摘要
相交切框架由巴拉斯于 1971 年提出,是一种在整数优化中生成切平面的方法。在这个框架中,我们使用一个全维凸无 S 集(其中 S 是整数程序的可行区域)来导出一个切面,将 S 与 S 的线性松弛的非积分顶点分开。最近,这一框架已被扩展到整数情况之外,以获得非线性环境中的切割平面。在这项研究中,我们考虑了 S 由同质二次不等式定义的特殊情况。在这种 "无二次不等式 "设置中,每个函数(\Gamma : D^m \rightarrow D^n\),其中(\(D^k\)是\(\mathbb {R}^k\) 中的单位球)都会生成一个无二次不等式集的表示。虽然并不是每一个 \(\Gamma \) 都会生成一个最大二次自由集,但每一个全维最大二次自由集都是由\(\Gamma \) 生成的。我们的主要结果表明,当且仅当\(\Gamma \)是非扩张的并且满足一个技术条件时,相应的无二次方集合才是全维的和最大的。这一结果产生了一类比以前已知的更广泛的最大无S集。我们的结果源于对最大无S集的新描述(对于一般S,超出了二次设定),这种描述基于 "暴露 "定义无S集的不等式的序列。
A characterization of maximal homogeneous-quadratic-free sets
The intersection cut framework was introduced by Balas in 1971 as a method for generating cutting planes in integer optimization. In this framework, one uses a full-dimensional convex S-free set, where S is the feasible region of the integer program, to derive a cut separating S from a non-integral vertex of a linear relaxation of S. Among all S-free sets, it is the inclusion-wise maximal ones that yield the strongest cuts. Recently, this framework has been extended beyond the integer case in order to obtain cutting planes in non-linear settings. In this work, we consider the specific setting when S is defined by a homogeneous quadratic inequality. In this ‘quadratic-free’ setting, every function \(\Gamma : D^m \rightarrow D^n\), where \(D^k\) is the unit sphere in \(\mathbb {R}^k\), generates a representation of a quadratic-free set. While not every \(\Gamma \) generates a maximal quadratic free set, it is the case that every full-dimensional maximal quadratic free set is generated by some \(\Gamma \). Our main result shows that the corresponding quadratic-free set is full-dimensional and maximal if and only if \(\Gamma \) is non-expansive and satisfies a technical condition. This result yields a broader class of maximal S-free sets than previously known. Our result stems from a new characterization of maximal S-free sets (for general S beyond the quadratic setting) based on sequences that ‘expose’ inequalities defining the S-free set.
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.