Two new self-adaptive projection methods for variational inequality problems

A New Projection Method for Variational Inequality Problems∗

We propose a new projection algorithm for solving the variational inequality problem, where the underlying function is continuous and satisfies a certain generalized monotonicity assumption (e.g., it can be pseudomonotone). The method is simple and admits a nice geometric interpretation. It consists of two steps. First, we construct an appropriate hyperplane which strictly separates the current...

Augmented Lagrangian methods for variational inequality problems

We introduce augmented Lagrangian methods for solving finite dimensional variational inequality problems whose feasible sets are defined by convex inequalities, generalizing the proximal augmented Lagrangian method for constrained optimization. At each iteration, primal variables are updated by solving an unconstrained variational inequality problem, and then dual variables are updated through ...

Some new projection methods for variational inequalities

In this paper, we propose some new double-projection methods for solving variational inequalities by using the Wiener–Hopf equations technique. It is shown that these methods converge linearly under mild conditions and include some existing projection methods as special cases. Some examples are given to illustrate the efficiency of the proposed methods. 2002 Elsevier Science Inc. All rights res...

A New Projection Algorithm for Generalized Variational Inequality

Adaptive frame methods for nonlinear variational problems

In this paper we develop adaptive numerical solvers for certain nonlinear variational problems. The discretization of the variational problems is done by a suitable frame decomposition of the solution, i.e., a complete, stable, and redundant expansion. The discretization yields an equivalent nonlinear problem on the space of frame coefficients. The discrete problem is then adaptively solved usi...