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 bandit online convex optimization with time-varying coupled inequality
constraints is considered, motivated by a repeated game between a group of learners and …

This paper considers the problem of solving distributed online optimization over a network
that consists of multiple interacting nodes. Each node in the network is endowed with a …

This article considers the distributed online convex optimization problem with time-varying
constraints over a network of agents. This is a sequential decision making problem with two …

This paper presents new learning-based, energy-delay-aware power control strategies for
the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks …

Feedback optimization enables autonomous optimality seeking of a dynamical system
through its closed-loop interconnection with iterative optimization algorithms. Among various …

Stochastic zeroth-order optimization algorithms have been predominantly analyzed under
the assumption that the objective function being optimized is time-invariant. Motivated by …

We study numerical optimization algorithms that use zeroth-order information to minimize
time-varying geodesically convex cost functions on Riemannian manifolds. In the Euclidean …

This paper focuses on a multi-agent zeroth-order online optimization problem in a federated
learning setting for target tracking. The agents only sense their current distances to their …

In this paper, we consider the optimisation of a time varying scalar field by a network of
agents with no gradient information. We propose a composite control law, blending …