Accelerated first-order methods for a class of semidefinite programs

• 成果类型: Article
• 署名作者: Wang, Alex L.; Kilinc-Karzan, Fatma
• 署名单位: Carnegie Mellon University; Purdue University System; Purdue University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-024-02073-4
• 发表日期: 2025
• 页码: 503-556
• 关键词: trust-region subproblem; 2nd-order cone; prox-method; optimization; relaxation; algorithm; optimality; cut

摘要

This paper introduces a new storage-optimal first-order method, CertSDP, for solving a special class of semidefinite programs (SDPs) to high accuracy. The class of SDPs that we consider, the exact QMP-like SDPs, is characterized by low-rank solutions, a priori knowledge of the restriction of the SDP solution to a small subspace, and standard regularity assumptions such as strict complementarity. Crucially, we show how to use a certificate of strict complementarity to construct a low-dimensional strongly convex minimax problem whose optimizer coincides with a factorization of the SDP optimizer. From an algorithmic standpoint, we show how to construct the necessary certificate and how to solve the minimax problem efficiently. Our algorithms for strongly convex minimax problems with inexact prox maps may be of independent interest. We accompany our theoretical results with preliminary numerical experiments suggesting that CertSDP significantly outperforms current state-of-the-art methods on large sparse exact QMP-like SDPs.