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 …

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 …

In this work, we propose a new algorithm for finding a zero of the sum of two monotone
operators where one is assumed to be single-valued and Lipschitz continuous. This …

Backtracking linesearch is the de facto approach for minimizing continuously differentiable
functions with locally Lipschitz gradient. In recent years, it has been shown that in the convex …

This paper proposes Triangularly Preconditioned Primal-Dual algorithm, a new primal-dual
algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two …

In this paper, we consider the decentralized dynamic optimization problem defined over a
multi-agent network. Each agent possesses a time-varying local objective function, and all …

Many large-scale and distributed optimization problems can be brought into a composite
form in which the objective function is given by the sum of a smooth term and a nonsmooth …

In this paper, we present a novel derivation of an existing algorithm for distributed
optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial …

Motivated by modern large-scale information processing problems in engineering, this
paper concentrates on studying distributed constrained convex optimization problems over a …

This article addresses the decentralized optimization problem where a group of agents with
coupled private objective functions work together to exactly optimize the summation of local …