Given an undirected graph G=(N, E) of agents N={1,..., N} connected with edges in E, we
study how to compute an optimal decision on which there is consensus among agents and …

We consider solving a convex optimization problem with possibly stochastic gradient, and
over a randomly time-varying multiagent network. Each agent has access to some local …

Motivated by applications in machine learning and statistics, we study distributed
optimization problems over a network of processors, where the goal is to optimize a global …

In this paper, the problem of finding a Nash equilibrium (NE) of a multi-player game is
considered. The players are only aware of their own cost functions as well as the action …

We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize
the sum of composite convex functions, where each term in the sum is a private cost function …

This paper aims to develop distributed algorithms for nonconvex optimization problems with
complicated constraints associated with a network. The network can be a physical one, such …

This paper investigates two novel distributed algorithms based on alternating direction
method of multipliers (ADMM) for network resource allocation of N agents. The main …

Classical primal-dual algorithms attempt to solve by alternately minimizing over the primal
variable through primal descent and maximizing the dual variable through dual ascent …

There has been an increasing necessity for scalable optimization methods, especially due to
the explosion in the size of data sets and model complexity in modern machine learning …

In this article, we consider distributed nonconvex optimization over an undirected connected
network. Each agent can only access to its own local nonconvex cost function and all agents …