2011
DOI: 10.1186/1687-1812-2011-10
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Strong convergence theorems by hybrid projection methods for equilibrium problems and fixed point problems of the asymptotically quasi-ϕ-nonexpansive mappings

Abstract: We consider a hybrid projection method for finding a common element in the fixed point set of an asymptotically quasi-j-nonexpansive mapping and in the solution set of an equilibrium problem. Strong convergence theorems of common elements are established in a uniformly smooth and strictly convex Banach space which has the Kadec-Klee property. 2000 Mathematics subject classification: 47H05, 47H09, 47H10, 47J25

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Cited by 36 publications
(23 citation statements)
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“…Theorem 2.1 mainly improve the corresponding results in [14], [15], [17] and [18]. The framework of the space is weak which do not require the uniform convexness.…”
Section: Resultssupporting
confidence: 66%
See 1 more Smart Citation
“…Theorem 2.1 mainly improve the corresponding results in [14], [15], [17] and [18]. The framework of the space is weak which do not require the uniform convexness.…”
Section: Resultssupporting
confidence: 66%
“…Recently, the above nonlinear problems have been extensively studied based on iterative techniques; see [3], [6]- [10], [14]- [17], [19], [22]- [26] and the references therein. In this paper, we study generalized mixed equilibrium problem (1.1) based on a monotone projection technique without any compactness assumption.…”
Section: Introductionmentioning
confidence: 99%
“…With the aid of generalization projections, we establish a strong theorem in a strictly convex and uniformly smooth Banach space. The results obtained in this paper mainly improve the corresponding results in [15], [16], [19], [20], [23], [30]. In order to prove our main results, we also need the following lemmas.…”
Section: Introductionsupporting
confidence: 69%
“…Since bifunction equilibrium problem covers variational inequality problems, zero point problems, and variational inclusion problems, it has been investigated by many authors via fixed point algorithms; see, for example, [5,6,8,9,10], [13]- [17], [24]- [28], [30] and the references therein.…”
Section: Introductionmentioning
confidence: 99%