A Fenchel dual gradient method enabling regularization for nonsmooth distributed optimization over time-varying networks
In this paper, we develop a regularized Fenchel dual gradient method (RFDGM), which allows nodes in a time-varying undirected network to find a common decision, in a fully distributed fashion, for minimizing the sum of their local objective functions subject to their local constraints. Different fro...
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| Vydané v: | Optimization methods & software Ročník 38; číslo 4; s. 813 - 836 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Abingdon
Taylor & Francis
04.07.2023
Taylor & Francis Ltd |
| Predmet: | |
| ISSN: | 1055-6788, 1029-4937, 1029-4937 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | In this paper, we develop a regularized Fenchel dual gradient method (RFDGM), which allows nodes in a time-varying undirected network to find a common decision, in a fully distributed fashion, for minimizing the sum of their local objective functions subject to their local constraints. Different from most existing distributed optimization algorithms that also cope with time-varying networks, RFDGM is able to handle problems with general convex objective functions and distinct local constraints, and still has non-asymptotic convergence results. Specifically, under a standard network connectivity condition, we show that RFDGM is guaranteed to reach ϵ-accuracy in both optimality and feasibility within
iterations. Such iteration complexity can be improved to
if the local objective functions are strongly convex but not necessarily differentiable. Finally, simulation results demonstrate the competence of RFDGM in practice. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1055-6788 1029-4937 1029-4937 |
| DOI: | 10.1080/10556788.2023.2189713 |