Asymptotic Properties of a Forward-Backward-Forward Differential Equation and Its Discrete Version for Solving Quasimonotone Variational Inequalities

TL;DR

This paper analyzes the asymptotic behavior of a forward-backward-forward differential equation and its discrete version for quasimonotone variational inequalities, introducing new convergence results and a novel Bregman-type algorithm with adaptive step-size.

Contribution

It extends continuous-time and discrete methods for VIs to quasimonotone operators, relaxing previous assumptions and proposing a new adaptive algorithm with strong convergence guarantees.

Findings

Weak and strong convergence under relaxed conditions

Ergodic convergence of continuous trajectories

A Bregman-type algorithm with adaptive step-size

Abstract

This paper investigates the asymptotic behavior of a forward-backward-forward (FBF) type differential equation and its discrete counterpart for solving quasimonotone variational inequalities (VIs). Building on recent continuous-time dynamical system frameworks for VIs, we extend these methods to accommodate quasimonotone operators. We establish weak and strong convergence under significantly relaxed conditions, without requiring strong pseudomonotonicity or sequential weak-to-weak continuity. Additionally, we prove ergodic convergence of the continuous trajectories, offering further insight into the long-term stability of the system. In the discrete setting, we propose a novel Bregman-type algorithm that incorporates a nonmonotone adaptive step-size rule based on the golden ratio technique. A key contribution of this work is demonstrating that the proposed method ensures strong convergence under the assumption of uniform continuity of the operator, thereby relaxing the standard Lipschitz continuity requirement prevalent in existing methods. Numerical experiments, including infinite-dimensional and non-Lipschitz cases, are presented to illustrate the improved convergence and broader applicability of the proposed approach.