DocumentCode
2887051
Title
Strong Convergence of Hybrid Approximate Proximal-Type Algorithm
Author
Li, Wei
Author_Institution
Sch. of Math. & Stat., Hebei Univ. of Econ. & Bus., Shijiazhuang
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
602
Lastpage
606
Abstract
Finding zero points for maximal monotone operator is a very active topic in different branches of mathematical and engineering sciences since many physically significant problems can be ultimately converted to it. Considerable research efforts have been devoted to the study of iterative algorithms of zero points for maximal monotone operators in recent years. By now, there already exist some algorithms, but they are not quite enough to deal with problems defined in a more general space. In this paper, a new hybrid approximate proximal-type algorithm is introduced which is proved to be strongly convergent to zero point of maximal monotone operator in Banach space by using some techniques of Lyapunov functional and generalized projection operator, etc. Moreover, the application of the new algorithm is demonstrated
Keywords
Banach spaces; Lyapunov methods; convergence; iterative methods; mathematical operators; optimisation; Banach space; Lyapunov functional; convergence; generalized projection operator; hybrid approximate proximal-type algorithm; iterative algorithm; maximal monotone operator; Boundary value problems; Convergence; Cybernetics; Hilbert space; Iterative algorithms; Kernel; Machine learning; Machine learning algorithms; Mathematics; Statistics; Topology; Generalized projection operator; Hybrid approximate proximal-type algorithm; Maximal monotone operator 1. Introduction;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258384
Filename
4028135
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