Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs

Abstract

A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When the graph is a simple path without loops, efficient off-the-shelf algorithms can be used. However, when the graph is large and unstructured, such algorithms cannot be used directly. In this paper, an algorithm, referred to as “Snake,” is proposed to solve such regularized problems over general graphs. The algorithm consists in properly selecting random simple paths in the graph and performing the proximal gradient algorithm over these simple paths. This algorithm is an instance of a new general stochastic proximal gradient algorithm, whose convergence is proven. Applications to trend filtering and graph inpainting are provided among others. Numerical experiments are conducted over large graphs.