Fixed Points of Averages of Resolvents: Geometry and Algorithms

TL;DR

This paper explores the fixed points of averaged resolvents, extending existing theories, and introduces two new algorithms with convergence analysis and numerical experiments demonstrating their effectiveness.

Contribution

It generalizes the understanding of fixed points of averaged resolvents and proposes two novel algorithms, one with proven convergence, for solving related problems.

Findings

Complete convergence proof for the first algorithm.

Numerical experiments show potential of the new algorithms.

Reformulation in product space enhances analysis.

Abstract

To provide generalized solutions if a given problem admits no actual solution is an important task in mathematics and the natural sciences. It has a rich history dating back to the early 19th century when Carl Friedrich Gauss developed the method of least squares of a system of linear equations - its solutions can be viewed as fixed points of averaged projections onto hyperplanes. A powerful generalization of this problem is to find fixed points of averaged resolvents (i.e., firmly nonexpansive mappings). This paper concerns the relationship between the set of fixed points of averaged resolvents and certain fixed point sets of compositions of resolvents. It partially extends recent work for two mappings on a question of C. Byrne. The analysis suggests a reformulation in a product space. Furthermore, two new algorithms are presented. A complete convergence proof that is based on averaged mappings is provided for the first algorithm. The second algorithm, which currently has no convergence proof, iterates a mapping that is not even nonexpansive. Numerical experiments indicate the potential of these algorithms when compared to iterating the average of the resolvents.