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Main Authors: Zhong, Suhan, Zhou, Jinling, Nie, Jiawang, Tang, Xindong
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.00180
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author Zhong, Suhan
Zhou, Jinling
Nie, Jiawang
Tang, Xindong
author_facet Zhong, Suhan
Zhou, Jinling
Nie, Jiawang
Tang, Xindong
contents This paper studies the copositive optimization problem whose objective is a sparse polynomial, with linear constraints over the nonnegative orthant. We propose sparse Moment-SOS relaxations to solve it. Necessary and sufficient conditions are shown for these relaxations to be tight. In particular, we prove they are tight under the cop-SOS convexity assumption. Compared to the traditional dense ones, the sparse Moment-SOS relaxations are more computationally efficient. Numerical experiments are given to show the efficiency.
format Preprint
id arxiv_https___arxiv_org_abs_2604_00180
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Sparse Copositive Polynomial Optimization
Zhong, Suhan
Zhou, Jinling
Nie, Jiawang
Tang, Xindong
Optimization and Control
This paper studies the copositive optimization problem whose objective is a sparse polynomial, with linear constraints over the nonnegative orthant. We propose sparse Moment-SOS relaxations to solve it. Necessary and sufficient conditions are shown for these relaxations to be tight. In particular, we prove they are tight under the cop-SOS convexity assumption. Compared to the traditional dense ones, the sparse Moment-SOS relaxations are more computationally efficient. Numerical experiments are given to show the efficiency.
title Sparse Copositive Polynomial Optimization
topic Optimization and Control
url https://arxiv.org/abs/2604.00180