Impact of Communication Delays on the Convergence Rate of Distributed Optimization Algorithms
Year: 2017
Type: preprint
Abstract: In this paper, we study distributed optimization problems over a network of nodes, where the goal is to optimize a global objective composed of a sum of local functions. For solving such optimization problems, we are interested in a popular distributed gradient-based consensus algorithm, which only requires local computation and communication. A significant challenge in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems. We provide convergence results and convergence rate estimates of the gradient-based consensus algorithm in the presence of uniform, but possibly arbitrarily large, communication delays between the nodes. Our results explicitly characterize the rate of convergence of the algorithm as a function of the network size, topology, and the inter-node communication delays.
Source: arXiv (Cornell University)
Authors: Thinh T. Doan, Carolyn L. Beck, R. Srikant
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2744955408