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• W3207478911 abstract "This paper presents a novel backtracking strategy for additive Schwarz methods for general convex optimization problems as an acceleration scheme. The proposed backtracking strategy is independent of local solvers, so that it can be applied to any algorithms that can be represented in an abstract framework of additive Schwarz methods. Allowing for adaptive increasing and decreasing of the step size along the iterations, the convergence rate of an algorithm is greatly improved. Improved convergence rate of the algorithm is proven rigorously. In addition, combining the proposed backtracking strategy with a momentum acceleration technique, we propose a further accelerated additive Schwarz method. Numerical results for various convex optimization problems that support our theory are presented." @default.
• W3207478911 created "2021-10-25" @default.
• W3207478911 creator A5029854230 @default.
• W3207478911 date "2022-05-01" @default.
• W3207478911 modified "2023-10-16" @default.
• W3207478911 title "Additive Schwarz methods for convex optimization with backtracking" @default.
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• W3207478911 doi "https://doi.org/10.1016/j.camwa.2022.03.033" @default.
• W3207478911 hasPublicationYear "2022" @default.
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