Distributed optimization provides a framework for deriving distributed algorithms for a variety
of multi-robot problems. This tutorial constitutes the first part of a two-part series on …

This paper considers the decentralized optimization problem, where agents in a network
cooperate to minimize the sum of their local objective functions by communication and local …

Decentralized learning has recently attracted much research attention because of its
robustness and user privacy advantages. Decentralized algorithms play central roles in …

We present a distributed quasi-Newton (DQN) method, which enables a group of agents to
compute an optimal solution of a separable multi-agent optimization problem locally using …

This paper investigates the use of the cubic-regularized Newton method within a federated
learning framework while addressing two major concerns that commonly arise in federated …

We present DisCo, a distributed algorithm for contact-rich, multi-robot tasks. DisCo is a
distributed contact-implicit trajectory optimization algorithm, which allows a group of robots …

We consider fully asynchronous decentralized optimization over a directed graph. While
various algorithms have been proposed, real-world applications require relaxing the …

This paper considers the decentralized optimization problem of minimizing a finite sum of
strongly convex and twice continuously differentiable functions over a fixed connected …