Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

This letter investigates the problem of tracking solutions of stochastic optimization problems
with time-varying costs that depend on random variables with decision-dependent …

This paper focuses on stochastic saddle point problems with decision-dependent
distributions. These are problems whose objective is the expected value of a stochastic …

This letter considers a feedback-based projected gradient method for optimizing systems
modeled as algebraic maps. The focus is on a setup where the gradient is corrupted by …

Distributed online optimization and game have been increasingly researched in the last
decade, mostly motivated by its wide applications in sensor networks, robotics (eg …

We look at a stochastic time‐varying optimization problem and we formulate online
algorithms to find and track its optimizers in expectation. The algorithms are derived from the …

This paper focuses on the online gradient and proximal-gradient methods with stochastic
gradient errors. In particular, we examine the performance of the online gradient descent …

The success of stochastic optimization hinges on the assumption that the distribution of the
data remains stationary both throughout the run of an optimization algorithm and after …

Power systems have experienced significant transformations in recent years driven by the
integration of new technologies, such as renewable generation and distributed energy …

Stochastic gradient descent with momentum (SGDM) has been widely used in machine
learning. However, in non-convex domains, high probability learning bounds for SGDM are …