Abstract
In this paper, an unconstrained collaborative optimization of a sum of convex functions is considered where agents make decisions using local information from their neighbors. The communication between nodes are described by a time-varying sequence of possibly state-dependent weighted networks. A new framework for modeling multi-Agent optimization problems over networks with state-dependent interactions and time-varying topologies is proposed. A gradient-based discrete-Time algorithm using diminishing step size is proposed for converging to the optimal solution under suitable assumptions. The algorithm is totally asynchronous without requiring B-connectivity assumption for convergence. The algorithm still works even if the weighted matrix of the graph is periodic and irreducible in synchronous protocol. Finally, a case study on a network of robots in an automated warehouse is provided in order to demonstrate the results.
Original language | English (US) |
---|---|
Article number | 9392368 |
Pages (from-to) | 2611-2624 |
Number of pages | 14 |
Journal | IEEE Transactions on Signal Processing |
Volume | 69 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1991-2012 IEEE.
Keywords
- Distributed optimization
- convex optimization
- state-dependent networks
- time-varying topologies