Linear Convergence of Distributed Aggregative Optimization with Coupled Inequality Constraints
Year: 2023
Type: preprint
Abstract: This article investigates a distributed aggregative optimization problem subject to coupled affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents' variables. To our best knowledge, this work is the first to address this problem, and a novel distributed aggregative primal-dual algorithm is proposed based on the dual diffusion strategy and gradient tracking technique. Through rigorous analysis, it is shown that the devised algorithm converges to the optimal solution at a linear rate. Finally, a numerical example is conducted to illustrate the effectiveness of the theoretical results.
Source: arXiv (Cornell University)
Authors: Du, Kaixin, Meng, Min
Institution: Tongji University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4380558392