On Difference-of-SOS and Difference-of-Convex-SOS Decompositions for Polynomials

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED
Yi-Shuai Niu, Hoai An Le Thi, Dinh Tao Pham
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引用次数: 0

Abstract

SIAM Journal on Optimization, Volume 34, Issue 2, Page 1852-1878, June 2024.
Abstract. In this article, we are interested in developing polynomial decomposition techniques based on sums-of-squares (SOS), namely the difference-of-sums-of-squares (D-SOS) and the difference-of-convex-sums-of-squares (DC-SOS). In particular, the DC-SOS decomposition is very useful for difference-of-convex (DC) programming formulation of polynomial optimization problems. First, we introduce the cone of convex-sums-of-squares (CSOS) polynomials and discuss its relationship to the sums-of-squares (SOS) polynomials, the non-negative polynomials, and the SOS-convex polynomials. Then we propose the set of D-SOS and DC-SOS polynomials and prove that any polynomial can be formulated as D-SOS and DC-SOS. The problem of finding D-SOS and DC-SOS decompositions can be formulated as a semi-definite program and solved for any desired precision in polynomial time using interior point methods. Some algebraic properties of CSOS, D-SOS, and DC-SOS are established. Second, we focus on establishing several practical algorithms for exact D-SOS and DC-SOS polynomial decompositions without solving any SDP. The numerical performance of the proposed D-SOS and DC-SOS decomposition algorithms and their parallel versions, tested on a dataset of 1750 randomly generated polynomials, is reported.
论多项式的 SOS 差分和凸 SOS 差分分解
SIAM 优化期刊》,第 34 卷,第 2 期,第 1852-1878 页,2024 年 6 月。 摘要在本文中,我们致力于开发基于平方和(SOS)的多项式分解技术,即平方差分解(D-SOS)和凸差分解(DC-SOS)。特别是,DC-SOS 分解对于多项式优化问题的凸差(DC)编程表述非常有用。首先,我们介绍了凸-平方和(CSOS)多项式锥,并讨论了它与平方和(SOS)多项式、非负多项式和 SOS-凸多项式的关系。然后,我们提出了 D-SOS 和 DC-SOS 多项式集,并证明任何多项式都可以表述为 D-SOS 和 DC-SOS。寻找 D-SOS 和 DC-SOS 分解的问题可以表述为一个半定式程序,并使用内点法在多项式时间内求解任何所需的精度。我们建立了 CSOS、D-SOS 和 DC-SOS 的一些代数性质。其次,我们重点研究了在不求解任何 SDP 的情况下精确分解 D-SOS 和 DC-SOS 多项式的几种实用算法。我们报告了所提出的 D-SOS 和 DC-SOS 分解算法及其并行版本的数值性能,并在 1750 个随机生成的多项式数据集上进行了测试。
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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