Design and Analysis of Distributed Multi-Agent Saddle Point Algorithm Based on Gradient-Free Oracle
In the paper, we are interested in one convex-concave function problem in network applications. Motivated by the saddle-point subgradient methods, we deal with a kind of saddle-point problem for multi-agent systems whose objective function for the underlying issue must be non-smooth but Lipschitz co...
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Published in | 2018 Australian & New Zealand Control Conference (ANZCC) pp. 362 - 365 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.12.2018
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Subjects | |
Online Access | Get full text |
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Summary: | In the paper, we are interested in one convex-concave function problem in network applications. Motivated by the saddle-point subgradient methods, we deal with a kind of saddle-point problem for multi-agent systems whose objective function for the underlying issue must be non-smooth but Lipschitz continuous. With the convex constrain set and global convex inequality constraints, we present a kind of distributed gradient-free algorithm in order to solve the issue of multi-agent convex-concave optimization. Under Slater's condition, We give the results of convergence rate and the effect of smoothing parameters on error bounds. |
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DOI: | 10.1109/ANZCC.2018.8606575 |