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 |
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All Science Journal Classification (ASJC) codes
- Analysis
- Applied Mathematics
- Control and Optimization
- Geometry and Topology
Cite this
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Multi parameter proximal point algorithms. / Boikanyo, Oganeditse A.; Moroşanu, Gheorghe.
In: Journal of Nonlinear and Convex Analysis, Vol. 13, No. 2, 05.2012, p. 221-231.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multi parameter proximal point algorithms
AU - Boikanyo, Oganeditse A.
AU - Moroşanu, Gheorghe
PY - 2012/5
Y1 - 2012/5
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84870048672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870048672&partnerID=8YFLogxK
M3 - Article
VL - 13
SP - 221
EP - 231
JO - Journal of Nonlinear and Convex Analysis
JF - Journal of Nonlinear and Convex Analysis
SN - 1345-4773
IS - 2
ER -