Year: 2023
Type: preprint
Abstract: In this work, we establish convergence results for the distributed proximal point algorithm (DPPA) for distributed optimization problems. We consider the problem on the whole domain Rd and find a general condition on the stepsize and cost functions such that the DPPA is stable. We prove that the DPPA with stepsize $η> 0$ exponentially converges to an $O(η)$-neighborhood of the optimizer. Our result clearly explains the advantage of the DPPA with respect to the convergence and stability in comparison with the distributed gradient descent algorithm. We also provide numerical tests supporting the theoretical results.
Source: arXiv (Cornell University)
Author: Choi, Woocheol
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4378770550