Extragradient method with feasible inexact projection to variational inequality problem

TL;DR

This paper introduces two inexact extragradient methods with feasible inexact projections for solving variational inequality problems, reducing computational effort while ensuring convergence under broad conditions.

Contribution

It proposes novel inexact extragradient algorithms with feasible inexact projections and establishes their convergence without requiring Lipschitz continuity.

Findings

Convergence proven for both methods under pseudo-monotonicity.

The second method adaptively finds step sizes via line search.

Methods require only two projections per iteration.

Abstract

The variational inequality problem in finite-dimensional Euclidean space is addressed in this paper, and two inexact variants of the extragradient method are proposed to solve it. Instead of computing exact projections on the constraint set, as in previous versions extragradient method, the proposed methods compute feasible inexact projections on the constraint set using a relative error criterion. The first version of the proposed method provided is a counterpart to the classic form of the extragradient method with constant steps. In order to establish its convergence we need to assume that the operator is pseudo-monotone and Lipschitz continuous, as in the standard approach. For the second version, instead of a fixed step size, the method presented finds a suitable step size in each iteration by performing a line search. Like the classical extragradient method, the proposed method does just two projections into the feasible set in each iteration. A full convergence analysis is provided, with no Lipschitz continuity assumption of the operator defining the variational inequality problem.