Accelerated Proximal Point Method and Forward Method for Monotone Inclusions release_47j4ybllivfyfahbx6dkjqk2dy

by Donghwan Kim

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2019  

Abstract

This paper proposes an accelerated proximal point method for maximally monotone operators. The proof is computer-assisted via the performance estimation problem approach. The proximal point method includes various well-known convex optimization methods, such as the proximal method of multipliers and the alternating direction method of multipliers, and thus the proposed acceleration has wide applications. Numerical experiments are presented to demonstrate the accelerating behaviors. In addition, this paper shows that the proposed acceleration applies to the forward method for cocoercive operators.
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Date   2019-07-26
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arXiv  1905.05149v2
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