Alternating proximal algorithms for linearly constrained variational inequalities: Application to domain decomposition for PDE’s

Let X,Y,Z be real Hilbert spaces, let f:X→R∪{+∞}, g:Y→R∪{+∞} be closed convex functions and let A:X→Z, B:Y→Z be linear continuous operators. Let us consider the constrained minimization problem Given a sequence (γ_n) which tends toward 0 as n→+∞, we study the following alternating proximal algorithm where α and ν are positive parameters. It is shown that if the sequence (γ_n) tends moderately slowly toward 0, then the iterates of (A) weakly converge toward a solution of (P). The study is extended to the setting of maximal monotone operators, for which a general ergodic convergence result is obtained. Applications are given in the area of domain decomposition for PDE’s.