4.6 Article

Weak convergence of a relaxed and inertial hybrid projection-proximal point algorithm for maximal monotone operators in Hilbert space

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SIAM JOURNAL ON OPTIMIZATION
卷 14, 期 3, 页码 773-782

出版社

SIAM PUBLICATIONS
DOI: 10.1137/S1052623403427859

关键词

Hilbert space; maximal monotone operator; proximal point; inexact iteration; relative error; separating hyperplane; orthogonal projection; relaxation; weak convergence

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This paper introduces a general implicit iterative method for finding zeros of a maximal monotone operator in a Hilbert space which unifies three previously studied strategies: relaxation, inertial type extrapolation and projection step. The first two strategies are intended to speed up the convergence of the standard proximal point algorithm, while the third permits one to perform inexact proximal iterations with fixed relative error tolerance. The paper establishes the global convergence of the method for the weak topology under appropriate assumptions on the algorithm parameters.

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