Accelerated proximal point method for maximally monotone operators

• 成果类型: Article
• 署名作者: Kim, Donghwan
• 署名单位: Korea Advanced Institute of Science & Technology (KAIST)
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-021-01643-0
• 发表日期: 2021
• 页码: 57-87
• 关键词: primal-dual algorithms; CONVERGENCE; optimization; rachford

摘要

This paper proposes an accelerated proximal point method for maximally monotone operators. The proof is computer-assisted via the performance estimation problem approach. The proximal point method includes various well-known convex optimization methods, such as the proximal method of multipliers and the alternating direction method of multipliers, and thus the proposed acceleration has wide applications. Numerical experiments are presented to demonstrate the accelerating behaviors.