Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator

In this paper, based on the viscosity approximation method and the regularized gradient-projection algorithm, we find a common element of the solution set of a constrained convex minimization problem and the set of zero points of the maximal monotone operator problem. In particular, the set of zero...

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Published in:Fixed point theory and applications (Hindawi Publishing Corporation) Vol. 2015; no. 1; pp. 1 - 23
Main Authors: Tian, Ming, Jiao, Si-Wen
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2015
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ISSN:1687-1812, 1687-1812
Online Access:Get full text
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Summary:In this paper, based on the viscosity approximation method and the regularized gradient-projection algorithm, we find a common element of the solution set of a constrained convex minimization problem and the set of zero points of the maximal monotone operator problem. In particular, the set of zero points of the maximal monotone operator problem can be transformed into the equilibrium problem. Under suitable conditions, new strong convergence theorems are obtained, which are useful in nonlinear analysis and optimization. As an application, we apply our algorithm to solving the split feasibility problem and the constrained convex minimization problem in Hilbert spaces.
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ISSN:1687-1812
1687-1812
DOI:10.1186/s13663-015-0258-9