Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

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 …

There is a growing cross-disciplinary effort in the broad domain of optimization and learning
with streams of data, applied to settings where traditional batch optimization techniques …

The world is becoming more digitized with the rise of modular robotic systems. Therefore,
with the increasing demands and needs for robotics, the modular robotic domain was …

In this article, the problem of online distributed optimization subject to a convex set is studied
by employing a network of agents, where the objective functions allocated to agents are …

We present the online Newton's method, a single-step second-order method for online
nonconvex optimization. We analyze its performance and obtain a dynamic regret bound …

Meta-reinforcement learning has widely been used as a learning-to-learn framework to
solve unseen tasks with limited experience. However, the aspect of constraint violations has …

This paper develops and analyzes an online distributed proximal-gradient method (DPGM)
for time-varying composite convex optimization problems. Each node of the network features …

Causality graphs are routinely estimated in social sciences, natural sciences, and
engineering due to their capacity to efficiently represent the spatiotemporal structure of multi …

Proximal operations are among the most common primitives appearing in both practical and
theoretical (or high-level) optimization methods. This basic operation typically consists in …