Title | ||
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CPCA: A Chebyshev Proxy and Consensus based Algorithm for General Distributed Optimization |
Abstract | ||
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We consider a general distributed optimization problem, aiming to optimize the average of a set of local objectives that are Lipschitz continuous univariate functions, with the existence of same local constraint sets. To solve the problem, we propose a Chebyshev Proxy and Consensus-based Algorithm (CPCA). Compared with existing distributed optimization algorithms, CPCA is able to address the problem with non-convex Lipschitz objectives, and has low computational costs since it is free from gradient or projection calculations. These benefits result from i) the idea of optimizing a Chebyshev polynomial approximation (i.e. a proxy) for the global objective to obtain ()-optimal solutions for any given precision (), and ii) the use of average consensus where the local proxies’ coefficient vectors are gossiped to enable every agent to obtain such a global proxy. We provide comprehensive analysis of the accuracy and complexities of the proposed algorithm. Simulations are conducted to illustrate its effectiveness. |
Year | DOI | Venue |
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2020 | 10.23919/ACC45564.2020.9147791 | 2020 American Control Conference (ACC) |
Keywords | DocType | ISSN |
Chebyshev approximation,Approximation algorithms,Optimization,Complexity theory,Machine learning algorithms,Linear programming,Convergence | Conference | 0743-1619 |
ISBN | Citations | PageRank |
978-1-5386-8266-1 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiyu He | 1 | 0 | 0.34 |
Jianping He | 2 | 177 | 23.47 |
Cai-Lian Chen | 3 | 831 | 98.98 |
Xinping Guan | 4 | 2791 | 253.38 |