{"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. 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":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Programming","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10107-024-02092-1","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
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.