Multi parameter proximal point algorithms

Oganeditse A. Boikanyo; Gheorghe Moroşanu

Multi parameter proximal point algorithms

Oganeditse A. Boikanyo, Gheorghe Moroşanu

Mathematics & Statistical Sciences

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

The aim of this paper is to prove a strong convergence result for an algorithm introduced by Y. Yao and M. A. Noor in 2008 under a new condition on one of the parameters involved. Further, convergence properties of a generalized proximal point algorithm which was introduced in [5] axe analyzed. The results in this paper axe proved under the general condition that errors tend to zero in norm. These results extend and improve several previous results on the regularization method and the proximal point algorithm.

Original language	English
Pages (from-to)	221-231
Number of pages	11
Journal	Journal of Nonlinear and Convex Analysis
Volume	13
Issue number	2
Publication status	Published - May 2012

All Science Journal Classification (ASJC) codes

Analysis
Applied Mathematics
Control and Optimization
Geometry and Topology

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title = "Multi parameter proximal point algorithms",

abstract = "The aim of this paper is to prove a strong convergence result for an algorithm introduced by Y. Yao and M. A. Noor in 2008 under a new condition on one of the parameters involved. Further, convergence properties of a generalized proximal point algorithm which was introduced in [5] axe analyzed. The results in this paper axe proved under the general condition that errors tend to zero in norm. These results extend and improve several previous results on the regularization method and the proximal point algorithm.",

author = "Boikanyo, {Oganeditse A.} and Gheorghe Moro{\c s}anu",

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AU - Moroşanu, Gheorghe

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AB - The aim of this paper is to prove a strong convergence result for an algorithm introduced by Y. Yao and M. A. Noor in 2008 under a new condition on one of the parameters involved. Further, convergence properties of a generalized proximal point algorithm which was introduced in [5] axe analyzed. The results in this paper axe proved under the general condition that errors tend to zero in norm. These results extend and improve several previous results on the regularization method and the proximal point algorithm.

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M3 - Article

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Multi parameter proximal point algorithms

Abstract

All Science Journal Classification (ASJC) codes

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