Randomized Douglas-Rachford methods for linear systems: Improved accuracy and efficiency

TL;DR

This paper introduces a randomized and momentum-accelerated Douglas-Rachford method for linear systems, achieving linear convergence in expectation and improved accuracy and efficiency over traditional approaches.

Contribution

The paper proposes the RrDR method with randomization and momentum, providing convergence guarantees and enhanced performance for linear feasibility problems.

Findings

RrDR achieves linear convergence in expectation.

Randomization improves convergence properties.

Momentum accelerates the convergence rate.

Abstract

The Douglas-Rachford (DR) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). However, the method converges weakly and the associated rate of convergence is hard to analyze in general. In addition, the direct extension of the DR method for solving more-than-two-sets feasibility problems, called the $r$-sets-DR method, is not necessarily convergent. To improve the efficiency of the optimization algorithms, the introduction of randomization and the momentum technique has attracted increasing attention. In this paper, we propose the randomized $r$-sets-DR (RrDR) method for solving the feasibility problem derived from linear systems, showing the benefit of the randomization as it brings linear convergence in expectation to the otherwise divergent $r$-sets-DR method. Furthermore, the convergence rate does not depend on the dimension of the coefficient matrix. We also study RrDR with heavy ball momentum and establish its accelerated rate. Numerical experiments are provided to confirm our results and demonstrate the notable improvements in accuracy and efficiency of the DR method, brought by the randomization and the momentum technique.