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A strong convergence theorem of common elements in Hilbert spaces
Fixed Point Theory and Applications volume 2013, Article number: 59 (2013)
Abstract
The purpose of this article is to investigate the convergence of an iterative process for equilibrium problems, fixed point problems and variational inequalities. Strong convergence of the purposed iterative process is obtained in the framework of Hilbert spaces.
MSC:47H05, 47H09, 47J25.
1 Introduction
Nonlinear analysis plays an important role in optimization problems, economics and transportation. The theory of variational inequalities has emerged as a rapidly growing area of research because of its applications; see [1–17] fore more details and the references therein. To study variational inequalities based on iterative methods has been attracting many authors’ attention. For the iterative methods, the most popular method is the Mann iterative method which was introduced by Mann in 1953; see [18] and the references therein. The Mann iterative process has been proved to be weak convergence for nonexpansive mappings in infinite dimension spaces; see [19] and the reference therein. Recently, many authors studied the modification of Mann iterative methods. The most popular one is to use projections. We call the method a hybrid projection method; see [20] and the reference therein. In this paper, we study equilibrium problems, fixed point problems and variational inequalities based on the hybrid projection method. Strong convergence theorems for common solutions of the problems are established in infinite dimension Hilbert spaces.
2 Preliminaries
Throughout this paper, we always assume that H is a real Hilbert space with the inner product and the norm . Let C be a nonempty closed convex subset of H. Let be a mapping. In this paper, we use to denote the fixed point set of S.
Recall that the mapping S is said to be nonexpansive if
S is said to be quasi-nonexpansive if and
Let be a mapping. Recall that A is said to be monotone if
A is said to be strongly monotone if there exists a constant such that
For such a case, A is also said to be α-strongly monotone. A is said to be inverse-strongly monotone if there exists a constant such that
For such a case, A is also said to be α-inverse-strongly monotone. A is said to be Lipschitz if there exits a constant such that
For such a case, A is also said to be L-Lipschitz. A set-valued mapping is said to be monotone if for all , and imply . A monotone mapping is maximal if the graph of T is not properly contained in the graph of any other monotone mapping. It is known that a monotone mapping T is maximal if and only if, for any , for all implies .
Let F be a bifunction of into ℝ, where ℝ denotes the set of real numbers and is an inverse-strongly monotone mapping. In this paper, we consider the following generalized equilibrium problem:
In this paper, the set of such an is denoted by , i.e.,
To study the generalized equilibrium problems (2.1), we may assume that F satisfies the following conditions:
-
(A1)
for all ;
-
(A2)
F is monotone, i.e., for all ;
-
(A3)
for each ,
-
(A4)
for each , is convex and lower semi-continuous.
Next, we give two special cases of the problem (2.1).
-
(I)
If , then the generalized equilibrium problem (2.1) is reduced to the following equilibrium problem:
(2.2) -
(II)
If , then the problem (2.1) is reduced to the following classical variational inequality:
(2.3)
It is known that is a solution to (2.3) if and only if x is a fixed point of the mapping , where is a constant and I is the identity mapping.
Recently, many authors studied the problems (2.1), (2.2) and (2.3) based on hybrid projection methods; see, for example, [21–36] and the references therein. Motivated by these results, we investigated the common element problems of the generalized equilibrium problem (2.1) and quasi-nonexpansive mappings based on the shrinking projection algorithm. A strong convergence theorem of common elements is established in the framework of Hilbert spaces.
In order to prove our main results, we also need the following definitions and lemmas.
The following lemma can be found in [4] and [9].
Lemma 2.1 Let C be a nonempty closed convex subset of H and let be a bifunction satisfying (A1)-(A4). Then, for any and , there exists such that
Further, define
for all and . Then the following hold:
-
(a)
is single-valued;
-
(b)
is firmly nonexpansive, i.e., for any ,
-
(c)
;
-
(d)
is closed and convex.
Lemma 2.2 [37]
Let B be a monotone mapping of C into H and the normal cone to C at , i.e.,
and define a mapping M on C by
Then M is maximal monotone and if and only if for all .
3 Main results
Theorem 3.1 Let C be a nonempty, closed and convex subset of a real Hilbert space H. Let be a bifunction from to ℝ which satisfies (A1)-(A4) and be a -inverse-strongly monotone mapping for every , where N denotes some positive integer. Let be a continuous quasi-nonexpansive mapping which is assumed to be demiclosed at zero and let be a β-inverse-strongly monotone mapping. Assume that . Let be a positive sequence in and be a positive sequence in for every . Let and be sequences in . Let be a sequence generated in the following iterative process:
where is such that
for each . Assume that the above sequence also satisfies the following restrictions:
-
(a)
;
-
(b)
and for each ;
-
(c)
and for each ,
where a, b, c, d, e and f are real numbers. Then the sequence strongly converges to .
Proof
In view of Lemma 2.1, we see that
Letting , we obtain that
In view of the restriction (c), we obtain that
This shows that is nonexpansive for every . In view of the restriction (c), we also see that is nonexpansive.
Next, we show that is closed and convex. In view of the assumption in the main body of the theorem, we see that is closed and convex. Suppose that is closed and convex for some . We show that is closed and convex for the same i. Indeed, for any , we see that
is equivalent to
Thus is closed and convex. This shows that is closed and convex.
Next, we show that for each . From the assumption, we see that . Assume that for some . For any , we see that
This shows that . This proves that . Notice that . For each , we have
In particular, we have
This implies that is bounded. Since and , we arrive at
It follows that
This implies that exists. On the other hand, we have
It follows that
Notice that . It follows that
This in turn implies that
In view of (3.1), we obtain that
On the other hand, we have
It follows from (3.2) that
For any , we have from Lemma 2.1 that
On the other hand, we have
Substituting (3.4) into (3.5), we arrive at
This in turn implies that
In view of the restrictions (a)-(c), we obtain from (3.2) that
On the other hand, we have from Lemma 2.1 that
This implies that
Notice that
Substituting (3.8) into (3.9), we see that
It follows that
In view of the restrictions (a) and (b), we obtain from (3.2) and (3.7) that
Since is bounded, we may assume that there is a subsequence of converging weakly to some point x. It follows from (3.12) that converges weakly to x for every .
Next, we show that for every . Since for any , we have
From the condition (A2), we see that
Replacing n by , we arrive at
For with and , let . Since and , we have for every . It follows from (3.14) that
From (3.12), we have as for every . On the other hand, we obtain from the monotonicity of that . It follows from (A4) that
From (A1) and (A4), we obtain from (3.16) that
which yields that
Letting in the above inequality for every , we arrive at
This shows that for every , that is, . Putting , we see that
and
This in turn implies that
In view of the restriction (a)-(c), we obtain from (3.2) that
On the other hand, we have from the firm nonexpansivity of that
This implies that
from which it follows that
Hence, we get that
In view of the restriction (a), we obtain from (3.2) and (3.19) that
Note that
In view of (3.12) and (3.20), we get that
Next, we prove that . In fact, let M be the maximal monotone mapping defined by
For any given , we have . Since , by the definition of , we have
In view of the algorithm, we obtain that
and hence
Since B is monotone, we obtain from (3.23) that
It follows from (3.21) that . On the other hand, we have that B is -Lipschitz continuous. It follows from (3.20) that
Notice that M is maximal monotone and hence . This shows that . Notice that
We find from (3.3) and (3.21) that
Next, we prove that . Since S is demiclosed at zero, we see that . This proves that . Notice that and , we have
On the other hand, we have
We, therefore, obtain that
This implies . Since is an arbitrary subsequence of , we obtain that as . This completes the proof. □
If S is an identity mapping, we obtain from Theorem 3.1 the following.
Corollary 3.2 Let C be a nonempty, closed and convex subset of a real Hilbert space H. Let be a bifunction from to ℝ which satisfies (A1)-(A4) and be a -inverse-strongly monotone mapping for every , where N denotes some positive integer. Let be a β-inverse-strongly monotone mapping. Assume that . Let be a positive sequence in and be a positive sequence in for every . Let and be sequences in . Let be a sequence generated in the following iterative process:
where is such that
for each . Assume that the above sequence also satisfies the following restrictions:
-
(a)
;
-
(b)
and for each ;
-
(c)
and for each ,
where a, b, c, d, e and f are real numbers. Then the sequence strongly converges to .
If , we obtain from Theorem 3.1 the following.
Corollary 3.3 Let C be a nonempty, closed and convex subset of a real Hilbert space H. Let F be a bifunction from to ℝ which satisfies (A1)-(A4) and be a κ-inverse-strongly monotone mapping. Let be a continuous quasi-nonexpansive mapping which is assumed to be demiclosed at zero and let be a β-inverse-strongly monotone mapping. Assume that . Let be a positive sequence in and be a positive sequence in . Let be a sequence in . Let be a sequence generated in the following iterative process:
where is such that
Assume that the above sequence also satisfies the following restrictions:
-
(a)
;
-
(b)
and for each ,
where a, b, c, d and e are real numbers. Then the sequence strongly converges to .
If B is a zero operator, we obtain from Theorem 3.1 the following.
Corollary 3.4 Let C be a nonempty, closed and convex subset of a real Hilbert space H. Let be a bifunction from to ℝ which satisfies (A1)-(A4) and be a -inverse-strongly monotone mapping for every , where N denotes some positive integer. Let be a continuous quasi-nonexpansive mapping which is assumed to be demiclosed at zero. Assume that . Let be a positive sequence in for every . Let and be sequences in . Let be a sequence generated in the following iterative process:
where is such that
for each . Assume that the above sequence also satisfies the following restrictions:
-
(a)
;
-
(b)
and for each ;
-
(c)
for each ,
where a, b, c and d are real numbers. Then the sequence strongly converges to .
Finally, we consider the following optimization problem: Find an such that
where is a convex and lower semicontinuous function for each , where is some positive integer.
Theorem 3.5 Let C be a nonempty, closed and convex subset of a real Hilbert space H. Let be a proper convex and lower semicontinuous function for every , where N denotes some positive integer. Assume that , denotes the solution set of the above optimization problem. Let and be sequences in . Let be a sequence generated in the following iterative process:
where is such that
for each . Assume that the above sequence also satisfies the following restrictions:
-
(a)
;
-
(b)
and for each ;
-
(c)
for each ,
where a, b, c and d are real numbers. Then the sequence strongly converges to .
Proof Putting , and , we find from Theorem 3.1 the desired conclusion immediately. □
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Chen, Z. A strong convergence theorem of common elements in Hilbert spaces. Fixed Point Theory Appl 2013, 59 (2013). https://doi.org/10.1186/1687-1812-2013-59
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DOI: https://doi.org/10.1186/1687-1812-2013-59