The alternating directions method (ADM) is an effective method for solving a class of variational inequalities (VI) when the proximal and penalty parameters in sub-VI problems are properly selected.Expand

We consider the linearly constrained separable convex programming, whose objective function is separable into m individual convex functions without coupled variables.Expand

This paper presents a new alternating direction method for solving co-coercive variational inequality problems, where the feasible set is the intersection of a simple set and polyhedron defined by a system of linear equations.Expand

In this paper, we aim to prove the linear rate convergence of the alternating direction method of multipliers (ADMM) for solving linearly constrained convex composite optimization problems.Expand

We consider the convergence of the Douglas--Rachford splitting method (DRSM) for minimizing the sum of a strongly convex function and a weakly convex functions in the “strongly + weakly” convex setting.Expand

We consider the effect of the so-called second-best tolls on the price of anarchy of the traffic equilibrium problem where there are multiple classes of users with a discrete set of values of time.… Expand

In signal processing, data analysis and scientific computing, one often encounters the problem of decomposing a tensor into a sum of contributions.Expand

In this paper, we give a new accuracy criterion for approximate proximal point algorithms. The criterion depends on the current iterate and is easy to verify. Under the suggested enforceable accuracy… Expand

We show that when one function in the objective is strongly convex, the penalty parameter and the operators in the linear equality constraint are appropriately restricted, it is sufficient to guarantee the convergence of the direct extension of ADMM.Expand