Convergence Analysis of the Relaxed Proximal Point Algorithm
Min Li, Yanfei You
Abstract and Applied Analysis · 2013
doi:10.1155/2013/912846 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Recently, a worst-case convergence rate was established for the Douglas-Rachford alternating direction method of multipliers (ADMM) in an ergodic sense. The relaxed proximal point algorithm (PPA) is a generalization of the original PPA which includes the Douglas-Rachford ADMM as a special case. In this paper, we provide a simple proof for the same convergence rate of the relaxed PPA in both ergodic and nonergodic senses.
Abstract licence: CC BY
References14
14 more references, whose wording the paper’s licence keeps private.
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.