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Accelerated Proximal Point Method and Forward Method for Monotone
Inclusions
release_47j4ybllivfyfahbx6dkjqk2dy
by
Donghwan Kim
Released
as a article
.
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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1905.05149v2
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