Sparse Polynomial Optimization with Matrix Constraints

Fuente: arXiv
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Hauptverfasser: Nie, Jiawang, Qu, Zheng, Tang, Xindong, Zhang, Linghao
Format: Preprint
Veröffentlicht: 2024
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author Nie, Jiawang
Qu, Zheng
Tang, Xindong
Zhang, Linghao
author_facet Nie, Jiawang
Qu, Zheng
Tang, Xindong
Zhang, Linghao
contents This paper studies the hierarchy of sparse matrix Moment-SOS relaxations for solving sparse polynomial optimization problems with matrix constraints. First, we prove a sufficient and necessary condition for the sparse hierarchy to be tight. Second, we discuss how to detect the tightness and extract minimizers. Third, for the convex case, we show that the hierarchy of the sparse matrix Moment-SOS relaxations is tight, under some general assumptions. In particular, we show that the sparse matrix Moment-SOS relaxation is tight for every order when the problem is SOS-convex. Numerical experiments are provided to show the efficiency of the sparse relaxations.
format Preprint
id arxiv_https___arxiv_org_abs_2411_18820
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Sparse Polynomial Optimization with Matrix Constraints
Nie, Jiawang
Qu, Zheng
Tang, Xindong
Zhang, Linghao
Optimization and Control
This paper studies the hierarchy of sparse matrix Moment-SOS relaxations for solving sparse polynomial optimization problems with matrix constraints. First, we prove a sufficient and necessary condition for the sparse hierarchy to be tight. Second, we discuss how to detect the tightness and extract minimizers. Third, for the convex case, we show that the hierarchy of the sparse matrix Moment-SOS relaxations is tight, under some general assumptions. In particular, we show that the sparse matrix Moment-SOS relaxation is tight for every order when the problem is SOS-convex. Numerical experiments are provided to show the efficiency of the sparse relaxations.
title Sparse Polynomial Optimization with Matrix Constraints
topic Optimization and Control
url https://arxiv.org/abs/2411.18820