Entropic proximal decomposition methods for convex programs and variational inequalities

We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmic-quadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the first time provably convergent decomposition schemes based on C^∞ Langrangians for solving convex structured problems. Under the only assumption that the primal–dual problems have nonempty solution sets, global convergence of the primal–dual sequence produced by the algorithm is established.