Convergence rates for sums-of-squares hierarchies with correlative sparsity

• 成果类型: Article
• 署名作者: Korda, Milan; Magron, Victor; Rios-Zertuche, Rodolfo
• 署名单位: Centre National de la Recherche Scientifique (CNRS); Czech Technical University Prague; UiT The Arctic University of Tromso
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-024-02071-6
• 发表日期: 2025
• 页码: 435-473
• 关键词: polynomial optimization; positive polynomials; relaxations; complexity

摘要

This work derives upper bounds on the convergence rate of the moment-sum-of-squares hierarchy with correlative sparsity for global minimization of polynomials on compact basic semialgebraic sets. The main conclusion is that both sparse hierarchies based on the Schmudgen and Putinar Positivstellensatze enjoy a polynomial rate of convergence that depends on the size of the largest clique in the sparsity graph but not on the ambient dimension. Interestingly, the sparse bounds outperform the best currently available bounds for the dense hierarchy when the maximum clique size is sufficiently small compared to the ambient dimension and the performance is measured by the running time of an interior point method required to obtain a bound on the global minimum of a given accuracy.