- Research Article
- Open Access

# An Iterative Algorithm Combining Viscosity Method with Parallel Method for a Generalized Equilibrium Problem and Strict Pseudocontractions

- Jian-Wen Peng
^{1}, - Yeong-Cheng Liou
^{2}Email author and - Jen-Chih Yao
^{3}

**2009**:794178

https://doi.org/10.1155/2009/794178

© Jian-Wen Peng et al. 2009

**Received:**5 August 2008**Accepted:**4 January 2009**Published:**10 February 2009

## Abstract

We introduce a new approximation scheme combining the viscosity method with parallel method for finding a common element of the set of solutions of a generalized equilibrium problem and the set of fixed points of a family of finitely strict pseudocontractions. We obtain a strong convergence theorem for the sequences generated by these processes in Hilbert spaces. Based on this result, we also get some new and interesting results. The results in this paper extend and improve some well-known results in the literature.

## Keywords

- Convex Function
- Equilibrium Problem
- Nonexpansive Mapping
- Iterative Scheme
- Common Element

## 1. Introduction

The set of solutions of (1.1) is denoted by . Flores-Bazán [1] provided some characterizations of the nonemptiness of the solution set for problem (1.1) in reflexive Banach spaces in the quasiconvex case. Bigi et al. [2] studied a dual problem associated with the problem (1.1) with .

The set of solutions of (1.2) is denoted by . The problem (1.2) includes, as special cases, the optimization problem, the variational inequality problem, the fixed point problem, the nonlinear complementarity problem, the Nash equilibrium problem in noncooperative games, and the vector optimization problem. For more detail, please see [3–5] and the references therein.

The set of solutions of (1.3) is denoted by .

where denotes the identity operator on . When , is said to be nonexpansive. Note that the class of strict pseudocontraction mappings strictly includes the class of nonexpansive mappings. We denote the set of fixed points of by .

Ceng and Yao [6], Yao et al. [8], and Peng and Yao [9, 10] introduced some iterative schemes for finding a common element of the set of solutions of the mixed equilibrium problem (1.4) and the set of common fixed points of a family of finitely (infinitely) nonexpansive mappings (strict pseudocontractions) in a Hilbert space and obtained some strong convergence theorems(weak convergence theorems). Some methods have been proposed to solve the problem (1.2); see, for instance, [3–5, 11–18] and the references therein. Recently, S. Takahashi and W. Takahashi [12] introduced an iterative scheme by the viscosity approximation method for finding a common element of the set of solutions of problem (1.2) and the set of fixed points of a nonexpansive mapping in a Hilbert space and proved a strong convergence theorem. Su et al. [13] introduced an iterative scheme by the viscosity approximation method for finding a common element of the set of solutions of problem (1.2) and the set of fixed points of a nonexpansive mapping and the set of solutions of the variational inequality problem for an -inverse strongly monotone mapping in a Hilbert space. Tada and Takahashi [14] introduced two iterative schemes for finding a common element of the set of solutions of problem (1.2) and the set of fixed points of a nonexpansive mapping in a Hilbert space and obtained both strong convergence theorem and weak convergence theorem. Ceng et al. [15] introduced an iterative algorithm for finding a common element of the set of solutions of problem (1.2) and the set of fixed points of a strict pseudocontraction mapping. Chang et al. [16] introduced some iterative processes based on the extragradient method for finding the common element of the set of fixed points of a family of infinitely nonexpansive mappings, the set of problem (1.2), and the set of solutions of a variational inequality problem for an -inverse strongly monotone mapping. Colao et al. [17] introduced an iterative method for finding a common element of the set of solutions of problem (1.2) and the set of fixed points of a finite family of nonexpansive mappings in a Hilbert space and proved the strong convergence of the proposed iterative algorithm to the unique solution of a variational inequality, which is the optimality condition for a minimization problem. To the best of our knowledge, there is not any algorithms for solving problem (1.1).

On the other hand, Marino and Xu [19] and Zhou [20] introduced and researched some iterative scheme for finding a fixed point of a strict pseudocontraction mapping. Acedo and Xu [21] introduced some parallel and cyclic algorithms for finding a common fixed point of a family of finite strict pseudocontraction mappings and obtained both weak and strong convergence theorems for the sequences generated by the iterative schemes.

In the present paper, we introduce a new approximation scheme combining the viscosity method with parallel method for finding a common element of the set of solutions of the generalized equilibrium problem and the set of fixed points of a family of finitely strict pseudocontractions. We obtain a strong convergence theorem for the sequences generated by these processes. Based on this result, we also get some new and interesting results. The results in this paper extend and improve some well-known results in the literature.

## 2. Preliminaries

For each , we denote by the convex hull of . A multivalued mapping is said to be a KKM map if, for every finite subset ,

We will use the following results in the sequel.

Lemma 2.1 (see [22]).

Let be a nonempty subset of a Hausdorff topological vector space and let be a KKM map. If is closed for all and is compact for at least one , then

For solving the generalized equilibrium problem, let us give the following assumptions for the bifunction , and the set :

(A2) is monotone, that is, for any ;

(A3) for each , is weakly upper semicontinuous;

(A5)for each is lower semicontinuous;

Lemma 2.2.

for all . Assume that either (B1) or (B2) holds. Then, the following conclusions hold:

Proof.

Hence, is weakly compact. Thus, in both cases, we can use Lemma 2.1 and have .

This implies that . Hence, is weakly closed. Hence, . Hence, from the arbitrariness of , we conclude that , .

At last, we claim that is a closed convex. Indeed, Since is firmly nonexpansive, is also nonexpansive. By [23, Proposition 5.3], we know that is closed and convex.

Remark 2.3.

It is easy to see that Lemma 2.2 is a generalization of [9, Lemma 2.3].

Lemma 2.5.

## 3. Strong Convergence Theorems

In this section, we show a strong convergence of an iterative algorithm based on both viscosity approximation method and parallel method which solves the problem of finding a common element of the set of solutions of a generalized equilibrium problem and the set of fixed points of a family of finitely strict pseudocontractions in a Hilbert space.

We need the following assumptions for the parameters and :

Theorem 3.1.

for every , where and are sequences of numbers satisfying the conditions (C1)–(C5). Then, , and converge strongly to .

Proof.

for all Since is complete, there exists a unique element such that .

for every Therefore, is bounded. From (3.3) and (3.5), we also obtain that and are bounded.

It follows from (C1) and (C2) that .

It follows from (C1), (C2), and that .

Since is bounded, there exists a subsequence of which converges weakly to . Without loss of generality, we can assume that From , we obtain that . From , we also obtain that . Since and is closed and convex, we obtain .

So by the demiclosedness principle [21, Proposition 2.6(ii)], it follows that .

Finally, we show that , where .

It follows from (C1), (3.43), (3.45), and Lemma 2.4 that . From and , we have and . The proof is now complete.

Theorem 3.2.

for every where and are sequences of numbers satisfying the conditions (C1)–(C5). Then, , and converge strongly to .

Proof.

## 4. Applications

By Theorems 3.1 and 3.2, we can obtain many new and interesting strong convergence theorems. Now, give some examples as follows: for , let , by Theorems 3.1 and 3.2, respectively, we have the following results.

Theorem 4.1.

for every where , , and are sequences of numbers satisfying the conditions (C1)–(C4). Then, , and converge strongly to .

Theorem 4.2.

for every where , , and are sequences of numbers satisfying the conditions (C1)–(C4). Then, , and converge strongly to .

We need the following two assumptions.

Let , by Theorems 3.1 and 3.2, respectively, we obtain the following results.

Theorem 4.3.

for every where and are sequences of numbers satisfying the conditions (C1)–(C5). Then, , and converge strongly to .

Theorem 4.4.

for every where and are sequences of numbers satisfying the conditions (C1)–(C5). Then, , and converge strongly to .

Let for all , by Theorems 3.1 and 3.2, respectively, we obtain the following results.

Theorem 4.5.

for every , where and are sequences of numbers satisfying the conditions (C1)–(C5). Then, , and converge strongly to .

Theorem 4.6.

Let , and let for all . Then . By Theorems 3.1 and 3.2, we obtain the following results.

Theorem 4.7.

for every , where and are sequences of numbers satisfying the conditions (C1)–(C3) and (C5). Then, and converge strongly to .

Theorem 4.8.

for every , where and are sequences of numbers satisfying the conditions (C1)–(C3) and (C5). Then, and converge strongly to .

- (1)
Since the nonexpansive mappings have been replaced by the strict pseudocontractions, Theorems 3.1, 3.2, 4.1 and 4.2 extend and improve [6, Theorem 3.1], [8, Theorem 3.5], [9, Theorems 4.1 and 4.2], [18, Theorem 4.1], and the main results in [9–11, 13–16].

- (2)
Since the weak convergence has been replaced by strong convergence, Theorems 3.1, 3.2, 4.1−4.4 extend and improve [12, Theorem 3.1], [10, Corollary 4.1].

- (3)
Theorems 4.7 and 4.8 are strong convergence theorems for strict pseudocontractions without constraints and hence they improve the corresponding results in [19, 21]. Theorems 3.1 and 3.2 also improve [10, Corollary 3.1].

## Declarations

### Acknowledgments

The first author was supported by the National Natural Science Foundation of China (Grants No. 10771228 and No. 10831009), the Science and Technology Research Project of Chinese Ministry of Education (Grant no. 206123), the Education Committee project Research Foundation of Chongqing Normal University (Grant no. KJ070816); the second and third authors were partially supported by the Grants NSC97-2221-E-230-017 and NSC96-2628-E-110-014-MY3, respectively.

## Authors’ Affiliations

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