Convergence of Hybrid Steepest–Descent Methods for Generalized Variational Inequalities | Zendy

Zeng Liu | Zendy; NgaiChing Wong | Zendy; JenChih Yao | Zendy

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Convergence of Hybrid Steepest–Descent Methods for Generalized Variational Inequalities

Author(s) -

Zeng Liu,

NgaiChing Wong,

JenChih Yao

Publication year - 2005

Publication title -

acta mathematica sinica english series

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.518

H-Index - 41

eISSN - 1439-8516

pISSN - 1439-7617

DOI - 10.1007/s10114-005-0608-3

Subject(s) - variational inequality , hilbert space , mathematics , convergence (economics) , method of steepest descent , inverse , gradient descent , fixed point , set (abstract data type) , descent (aeronautics) , mathematical optimization , mathematical analysis , computer science , artificial neural network , geometry , machine learning , aerospace engineering , engineering , economics , programming language , economic growth

In this paper, we consider the generalized variational inequality GVI(F, g,C), where F and g are mappings from a Hilbert space into itself and C is the fixed point set of a nonexpansive mapping. We propose two iterative algorithms to find approximate solutions of the GVI(F, g,C). Strong convergence results are established and applications to constrained generalized pseudo–inverse are included.

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