A Primal-Dual Partial Inverse Splitting for Constrained Monotone Inclusions: Applications to stochastic Programming and Mean Field Games

TL;DR

This paper introduces a primal-dual partial inverse splitting method for constrained monotone inclusions, with applications to stochastic programming and mean field games, demonstrating convergence and efficiency in complex optimization problems.

Contribution

It proposes a novel primal-dual splitting algorithm based on partial inverse operators, generalizing existing methods for constrained monotone inclusions.

Findings

Proves weak convergence of the proposed algorithm.

Shows effectiveness in non-smooth convex optimization problems.

Applies the method successfully to stochastic arc capacity expansion.

Abstract

In this work we study a constrained monotone inclusion involving the normal cone to a closed vector subspace and a priori information on primal solutions. We model this information by imposing that solutions belongs to the fixed point set of an averaged nonexpansive mapping. We characterize the solutions using an auxiliary inclusion that involves the partial inverse operator. Then, we propose the primal-dual partial inverse splitting and we prove its weak convergence to a solution of the inclusion, generalizing several methods in the literature. The efficiency of the proposed method is illustrated in two non-smooth convex optimization problems whose constraints have vector subspace structure. Finally, the proposed algorithm is applied to find a solution to a stochastic arc capacity expansion problem in transport networks.