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Convergence Rates for Projective Splitting

Patrick R. Johnstone (patrick.r.johnstone***at***gmail.com)
Jonathan Eckstein (jeckstei***at***business.rutgers.edu)

Abstract: Projective splitting is a family of methods for solving inclusions involving sums of maximal monotone operators. First introduced by Eckstein and Svaiter in 2008, these methods have enjoyed significant innovation in recent years, becoming one of the most flexible operator splitting frameworks available. While weak convergence of the iterates to a solution has been established, there have been few attempts to study convergence rates of projective splitting. The purpose of this manuscript is to do so under various assumptions. To this end, there are three main contributions. First, in the context of convex optimization, we establish an $O(1/k)$ ergodic function convergence rate. Second, for strongly monotone inclusions, strong convergence is established as well as an ergodic $O(1/\sqrt{k})$ convergence rate for the distance of the iterates to the solution. Finally, for inclusions featuring strong monotonicity and cocoercivity, linear convergence is established.

Keywords: operator splitting, convergence rates

Category 1: Convex and Nonsmooth Optimization


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Entry Submitted: 06/11/2018
Entry Accepted: 06/11/2018
Entry Last Modified: 08/08/2018

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