Comparing Averaged Relaxed Cutters and Projection Methods: Theory and Examples

TL;DR

This paper analyzes the convergence of averaged relaxed cutter methods, which generalize projection algorithms like Douglas--Rachford, by establishing convergence under certain conditions despite the loss of firm nonexpansivity.

Contribution

It provides a convergence proof for averaged relaxed cutter methods, clarifying parameter choices and extending analysis beyond firm nonexpansivity assumptions.

Findings

Convergence is established for a family of relaxed cutter methods.

Parameter selection critically affects convergence behavior.

Illustrative examples demonstrate practical implementation.

Abstract

We focus on the convergence analysis of averaged relaxations of cutters, specifically for variants that---depending upon how parameters are chosen---resemble \emph{alternating projections}, the \emph{Douglas--Rachford method}, \emph{relaxed reflect-reflect}, or the \emph{Peaceman--Rachford} method. Such methods are frequently used to solve convex feasibility problems. The standard convergence analyses of projection algorithms are based on the \emph{firm nonexpansivity} property of the relevant operators. However if the projections onto the constraint sets are replaced by cutters (projections onto separating hyperplanes), the firm nonexpansivity is lost. We provide a proof of convergence for a family of related averaged relaxed cutter methods under reasonable assumptions, relying on a simple geometric argument. This allows us to clarify fine details related to the allowable choice of the relaxation parameters, highlighting the distinction between the exact (firmly nonexpansive) and approximate (strongly quasi-nonexpansive) settings. We provide illustrative examples and discuss practical implementations of the method.