{"title":"Convex hulls of monomial curves, and a sparse positivstellensatz","authors":"Gennadiy Averkov, Claus Scheiderer","doi":"10.1007/s10107-024-02060-9","DOIUrl":null,"url":null,"abstract":"<p>Consider the closed convex hull <i>K</i> of a monomial curve given parametrically as <span>\\((t^{m_1},\\ldots ,t^{m_n})\\)</span>, with the parameter <i>t</i> varying in an interval <i>I</i>. We show, using constructive arguments, that <i>K</i> admits a lifted semidefinite description by <span>\\(\\mathcal {O}(d)\\)</span> linear matrix inequalities (LMIs), each of size <span>\\(\\left\\lfloor \\frac{n}{2} \\right\\rfloor +1\\)</span>, where <span>\\(d= \\max \\{m_1,\\ldots ,m_n\\}\\)</span> is the degree of the curve. On the dual side, we show that if a univariate polynomial <i>p</i>(<i>t</i>) of degree <i>d</i> with at most <span>\\(2k+1\\)</span> monomials is non-negative on <span>\\({\\mathbb {R}}_+\\)</span>, then <i>p</i> admits a representation <span>\\(p = t^0 \\sigma _0 + \\cdots + t^{d-k} \\sigma _{d-k}\\)</span>, where the polynomials <span>\\(\\sigma _0,\\ldots ,\\sigma _{d-k}\\)</span> are sums of squares and <span>\\(\\deg (\\sigma _i) \\le 2k\\)</span>. The latter is a univariate positivstellensatz for sparse polynomials, with non-negativity of <i>p</i> being certified by sos polynomials whose degree only depends on the sparsity of <i>p</i>. Our results fit into the general attempt of formulating polynomial optimization problems as semidefinite problems with LMIs of small size. Such small-size descriptions are much more tractable from a computational viewpoint.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"10 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-02-16","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/s10107-024-02060-9","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
Consider the closed convex hull K of a monomial curve given parametrically as \((t^{m_1},\ldots ,t^{m_n})\), with the parameter t varying in an interval I. We show, using constructive arguments, that K admits a lifted semidefinite description by \(\mathcal {O}(d)\) linear matrix inequalities (LMIs), each of size \(\left\lfloor \frac{n}{2} \right\rfloor +1\), where \(d= \max \{m_1,\ldots ,m_n\}\) is the degree of the curve. On the dual side, we show that if a univariate polynomial p(t) of degree d with at most \(2k+1\) monomials is non-negative on \({\mathbb {R}}_+\), then p admits a representation \(p = t^0 \sigma _0 + \cdots + t^{d-k} \sigma _{d-k}\), where the polynomials \(\sigma _0,\ldots ,\sigma _{d-k}\) are sums of squares and \(\deg (\sigma _i) \le 2k\). The latter is a univariate positivstellensatz for sparse polynomials, with non-negativity of p being certified by sos polynomials whose degree only depends on the sparsity of p. Our results fit into the general attempt of formulating polynomial optimization problems as semidefinite problems with LMIs of small size. Such small-size descriptions are much more tractable from a computational viewpoint.
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.