Distributed Convex Optimization With State-Dependent (Social) Interactions and Time-Varying Topologies
| Autor: | Seyyed Shaho Alaviani, Nicola Elia |
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| Rok vydání: | 2021 |
| Předmět: |
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Mathematical optimization Optimization problem Computer science 020206 networking & telecommunications 02 engineering and technology Network topology Asynchronous communication Signal Processing Convergence (routing) Convex optimization 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Electrical and Electronic Engineering Convex function |
| Zdroj: | IEEE Transactions on Signal Processing. 69:2611-2624 |
| ISSN: | 1941-0476 1053-587X |
| DOI: | 10.1109/tsp.2021.3070223 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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