Stochastic gradient descent (SGD), also known as stochastic approximation, refers to certain
simple iterative structures used for solving stochastic optimization and root-finding problems …

We study the question of obtaining last-iterate convergence rates for no-regret learning
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …

We study the extension of the proximal gradient algorithm where only a stochastic gradient
estimate is available and a relaxation step is allowed. We establish convergence rates for …

This paper considers an n-player stochastic Nash equilibrium problem (NEP) in which the i
th player minimizes a composite objective f_i(∙,x_-i)+r_i(∙), where f_i is an expectation-valued …

We consider the task of sampling with respect to a log concave probability distribution. The
potential of the target distribution is assumed to be composite, ie, written as the sum of a …

We develop a new stochastic algorithm for solving pseudomonotone stochastic variational
inequalities. Our method builds on Tseng's forward-backward-forward algorithm, which is …

Minimum entropy deconvolution (MED) and its related methods have been applied to
bearing fault detection extensively due to their good performance in fault feature …

Vibration monitoring is widely used for machinery fault detection as one of the most effective
and common methods. However, it is difficult to extract fault features from the vibration …

We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-
value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized …

We study a stochastically perturbed version of the well-known Krasnoselskii–Mann iteration
for computing fixed points of nonexpansive maps in finite dimensional normed spaces. We …