Abstract
We propose decentralized primal-dual methods for cooperative multiagent consensus optimization problems over both static and time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific convex functions over conic constraint sets defined by agent-specific nonlinear functions; hence, the optimal consensus decision should lie in the intersection of these private sets. Under the strong convexity assumption, we provide convergence rates for suboptimality, infeasibility, and consensus violation in terms of the number of communications required; examine the effect of underlying network topology on the convergence rates.
Original language | English (US) |
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Pages (from-to) | 5682-5697 |
Number of pages | 16 |
Journal | IEEE Transactions on Automatic Control |
Volume | 67 |
Issue number | 11 |
DOIs | |
State | Published - Nov 1 2022 |
Keywords
- Convergence analysis
- constrained optimization
- distributed algorithms
- saddle point problem
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering