1699. A Unified Decentralized Nonconvex Algorithm under Kurdyka-Lojasiewicz Condition
Invited abstract in session MC-37: Optimization algorithms and applications 1, stream Nonlinear Optimization.
Monday, 10:30-12:00Room: JUR – Seminar-Raum 43
Authors (first author is the speaker)
| 1. | Liping Wang
|
| School of Mathematics, Nanjing University of Aeronautics and Astronautics |
Abstract
In this paper, a unified decentralized nonconvex algorithmic framework is proposed which subsumes several state-of-the-art gradient tracking algorithms and quasi-Newton algorithms. We develop an analytical convergence of the unified algorithm under the Kurdyka-Lojasiewicz condition for nonconvex optimization problem. We also propose some quasi-Newton variants that fit into the proposed framework. The numerical results show that these newly developed algorithms are very efficient compared with other state-of-the-art algorithms for solving decentralized nonconvex smooth optimization problems.
Keywords
- Optimization Models and Methods
- Distributed Computing
Status: accepted
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