{"title":"单项式曲线的凸壳和稀疏正定定理","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":"{\"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}","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
摘要
考虑参数为 \((t^{m_1},\ldots ,t^{m_n})\)的单项式曲线的闭凸壳 K,参数 t 在区间 I 中变化。我们利用构造论证证明,K 可以通过线性矩阵不等式(LMIs)进行提升半定量描述,每个线性矩阵不等式的大小为 \(\left\lfloor \frac{n}{2} \right\rfloor +1\) ,其中 \(d= \max \{m_1,\ldots ,m_n/}/)是曲线的阶数。在对偶方面,我们证明了如果阶数为 d 的单变量多项式 p(t) 在 \({\mathbb {R}}_+\) 上是非负的,且其单项式最多有\(2k+1\) 个、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 \(\deg (\sigma _i) \le 2k\).后者是稀疏多项式的单变量正弦定理,p 的非负性由 sos 多项式证明,而 sos 多项式的度数只取决于 p 的稀疏性。我们的结果符合将多项式优化问题表述为具有小尺寸 LMI 的半有限问题的一般尝试。从计算的角度来看,这种小规模的描述要容易得多。
Convex hulls of monomial curves, and a sparse positivstellensatz
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.