Convergence Analysis of Stochastic Saddle Point Mirror Descent Algorithm -- A Projected Dynamical View Point

Abstract

Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches to tackle saddle point problem, with a focus on continuous-time contexts. In this paper we propose a continuous time dynamics to tackle saddle point problem utilizing projected dynamical system in non-Euclidean domain. This involves computing the (sub/super) gradient of the min-max function within a Riemannian metric. Additionally, we establish viable Caratheodory solutions also prove the Lyapunov stability and asymptotic set stability of the proposed continuous time dynamical system. Next, we present the Stochastic Saddle Point Mirror Descent (SSPMD) algorithm and establish its equivalence with the proposed continuous-time dynamics. Leveraging stability analysis of the continuous-time dynamics, we demonstrate the almost sure convergence of the algorithm's iterates. Furthermore, we introduce the Zeroth-Order Saddle Point Mirror Descent (SZSPMD) algorithm, which approximates gradients using Nesterov's Gaussian Approximation, showcasing convergence to a neighborhood around saddle points. The analysis in this paper provides geometric insights into the mirror descent algorithm and demonstrates how these insights offer theoretical foundations for various practical applications of the mirror descent algorithm in diverse scenarios.