New Results on the Local Linear Convergence of ADMM: A Joint Approach

• 成果类型: Article
• 署名作者: Erseghe, Tomaso
• 署名单位: University of Padua
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3033512
• 发表日期: 2021
• 页码: 5096-5111
• 关键词: convergence; Convex functions; Eigenvalues and eigenfunctions; optimization; Transforms; Symmetric matrices; nickel; Alternating direction method of multipliers (ADMM); convergence speed; contraction properties; Douglas-Rachford splitting; distributed optimization; eigenvalues characterization; Legenre-Fenchel transform; matrix product

摘要

Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of multipliers (ADMM) is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine tuning of system parameters according to the specific application scenario, and which ultimately calls for a thorough understanding of the hidden mechanisms that control the convergence behavior. In this framework, we aim at providing new theoretical insights on the convergence process and specifically on some constituent matrices of ADMM whose eigenstructure provides a close link with the algorithm's convergence speed. One of the key techniques that we develop allows to effectively locate the eigenvalues of a (symmetric) matrix product, thus being able to estimate the contraction properties of ADMM. In the comparison with the results available from the literature, we are able to strengthen the precision of our speed estimate thanks to the fact that we are solving a joint problem (i.e., we are identifying the spectral radius of the product of two matrices) in place of two separate problems (the product of two matrix norms).