Title :
Strong Convergence of Hybrid Approximate Proximal-Type Algorithm
Author_Institution :
Sch. of Math. & Stat., Hebei Univ. of Econ. & Bus., Shijiazhuang
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258384