Linear Convergence in Optimization Over Directed Graphs With Row-Stochastic Matrices
| Autor: | Ran Xin, Chenguang Xi, Van Sy Mai, Eyad H. Abed, Usman A. Khan |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Linear programming Computer science Contrast (statistics) 020206 networking & telecommunications 02 engineering and technology Directed graph Computer Science Applications Matrix (mathematics) 020901 industrial engineering & automation Rate of convergence Optimization and Control (math.OC) Control and Systems Engineering FOS: Mathematics 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Convex function Focus (optics) Mathematics - Optimization and Control |
| Zdroj: | IEEE Transactions on Automatic Control. 63:3558-3565 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2018.2797164 |
| Popis: | This paper considers a distributed optimization problem over a multi-agent network, in which the objective function is a sum of individual cost functions at the agents. We focus on the case when communication between the agents is described by a \emph{directed} graph. Existing distributed optimization algorithms for directed graphs require at least the knowledge of the neighbors' out-degree at each agent (due to the requirement of column-stochastic matrices). In contrast, our algorithm requires no such knowledge. Moreover, the proposed algorithm achieves the best known rate of convergence for this class of problems, $O(\mu^k)$ for $0 Comment: arXiv admin note: text overlap with arXiv:1607.04757 |
| Databáze: | OpenAIRE |
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