Strong Convergence of Forward-Reflected-Backward Splitting Methods for Solving Monotone Inclusions with Applications to Image Restoration and Optimal Control

TL;DR

This paper introduces new strongly convergent forward-reflected-backward splitting algorithms for monotone inclusions, requiring minimal evaluations, with applications demonstrated in image restoration and optimal control.

Contribution

It proposes novel strongly convergent splitting methods with inertial variants that need only one forward and one backward evaluation per iteration, improving efficiency.

Findings

Methods achieve strong convergence under specified conditions.

Inertial versions enhance convergence speed.

Applications show effectiveness in image restoration and optimal control.

Abstract

In this paper, we propose and study several strongly convergent versions of the forward-reflected-backward splitting method of Malitsky and Tam for finding a zero of the sum of two monotone operators in a real Hilbert space. Our proposed methods only require one forward evaluation of the single-valued operator and one backward evaluation of the set-valued operator at each iteration; a feature that is absent in many other available strongly convergent splitting methods in the literature. We also develop inertial versions of our methods and strong convergence results are obtained for these methods when the set-valued operator is maximal monotone and the single-valued operator is Lipschitz continuous and monotone. Finally, we discuss some examples from image restorations and optimal control regarding the implementations of our methods in comparison with known related methods in the literature.