In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

We study distributed composite optimization over networks: agents minimize a sum of
smooth (strongly) convex functions–the agents' sum-utility–plus a nonsmooth (extended …

The presence of embedded electronics and communication capabilities as well as sensing
and control in smart devices has given rise to the novel concept of cyber-physical networks …

This work concerns the analysis and design of distributed first-order optimization algorithms
over time-varying graphs. The goal of such algorithms is to optimize a global function that is …

In this paper, we provide a unified analysis of temporal difference learning algorithms with
linear function approximators by exploiting their connections to Markov jump linear systems …

We revisit the recent gradient tracking algorithm for distributed consensus optimization from
a control theoretic viewpoint. We show that the algorithm can be constructed by solving a …

We consider the distributed optimization problem for a multi-agent system. Here, multiple
agents cooperatively optimize an objective by sharing information through a communication …

We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG)
for solving decentralized strongly convex stochastic optimization problems where the …

We consider the distributed optimization problem in which a network of agents aims to
minimize the average of local functions. To solve this problem, several algorithms have …