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 …

Distributed optimization consists of multiple computation nodes working together to minimize
a common objective function through local computation iterations and network-constrained …

Although the field of distributed optimization is well developed, relevant literature focused on
the application of distributed optimization to multi-robot problems is limited. This survey …

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given
“difficult”(constrained) problem via finding solutions of a sequence of “easier”(often …

We investigate multiagent distributed online constrained convex optimization problems with
feedback delays, where agents make sequential decisions before being aware of the cost …

This paper considers the problem of decentralized consensus optimization over a network,
where each node holds a strongly convex and twice-differentiable local objective function …

This article addresses distributed optimization problems, in which a group of agents
cooperatively minimize the sum of their private objective functions via information …

In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of
cost functions over a decentralized network. We first develop a general algorithmic …

This paper considers the distributed optimization problem where each node of a peer-to-
peer network minimizes a finite sum of objective functions by communicating with its …

Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults
that can target their controllers and cause safety hazards and harm to patients. This paper …