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Strong convergence of a general iterative algorithm in Hilbert spaces
Journal of Inequalities and Applications volume 2013, Article number: 19 (2013)
Abstract
In this paper, the problem of approximating a common element in the common fixed point set of an infinite family of nonexpansive mappings, in the solution set of a variational inequality involving an inverse-strongly monotone mapping and in the solution set of an equilibrium problem is investigated based on a general iterative algorithm. Strong convergence of the iterative algorithm is obtained in the framework of Hilbert spaces. The results obtained in this paper improve the corresponding results announced by many authors.
AMS Subject Classification:47H09, 47J05, 47J25.
1 Introduction and preliminaries
Let H be a real Hilbert space, whose inner product and norm are denoted by and respectively. Let C be a nonempty, closed and convex subset of H and be a mapping. In this paper, we use to denote the set of fixed points of T. Recall that T is said to be a κ-contraction iff there exists a constant such that
T is said to be a nonexpansive mapping iff
Let be a mapping. Recall that B is said to be an α-inverse-strongly monotone iff there exits a positive constant α such that
The classical variational inequality is to find such that
In this paper, we use to denote the solution set of the variational inequality.
Let be the metric projection from H onto C. It is also known that satisfies
Moreover, is characterized by the properties and for all . One can see that the variational inequality is equivalent to a fixed point problem. The element is a solution of the variational inequality if and only if u is a fixed point of the mapping , where is a constant and I is the identity mapping. This alternative equivalent formulation has played a significant role in the studies of the variational inequality and related optimization problems.
Recall that an operator A is strongly positive on H iff there exists a constant with the property
Recall that a set-valued mapping is said to be monotone if for all , and imply . A monotone mapping is maximal if the graph of of S is not properly contained in the graph of any other monotone mapping. It is known that a monotone mapping S is maximal iff for , for every implies . Let B be a monotone map of C into H and let be the normal cone to C at , i.e., and define
Then S is maximal monotone and iff ; see [1] and the references therein.
Let F be a bifunction of into ℝ, where ℝ is the set of real numbers. The equilibrium problem for is to find such that
The set of solutions of the problem (1.2) is denoted by . Numerous problems in physics, optimization and economics reduce to finding a solution of (1.2). Recently, many iterative algorithms have been studied to solve the equilibrium problem (1.2); see, for instance, [2–19].
For solving the equilibrium problem (1.2), let us 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 semicontinuous.
In 2007, Takahashi and Takahashi [17] proved the following result.
Theorem TT Let C be a nonempty closed convex subset of H. Let F be a bifunction from to R satisfying (A1)-(A4) and let T be a nonexpansive mapping of C into H such that . Let f be a contraction of H into itself and let and be sequences generated by and
where and satisfy , , , , and . Then and strongly converge to some point z, where .
Recently, Plubtieng and Punpaeng [19] further improved the above results by involving a strongly positive self-adjoint operator. To be more precise, they proved the following results.
Theorem PP Let H be a real Hilbert space, let F be a bifunction from satisfying (A1)-(A4) and let T be a nonexpansive mapping on H such that . Let f be a contraction of H into itself with and let A be a strongly positive bounded linear operator on H with the coefficient and . Let be a sequence generated by and
where and satisfy , , , , and . Then and strongly converge to some point z, where .
In 2008, Su, Shang and Qin [2] considered the variational inequality (1.1), and the equilibrium problem (1.2) based on a composite iterative algorithm and proved the following theorem.
Theorem SSQ Let C be a nonempty closed convex subset of H. Let F be a bifunction from to R satisfying (A1)-(A4). Let A be α-inverse-strongly monotone and let T be a nonexpansive mapping of C into H such that . Let f be a contraction of H into itself and let and be sequences generated by and
where , where , and satisfy , , , , , and . Then and strongly converge to some point z, where .
The above results only involve a single mapping, we will consider an infinite family of mappings in this paper. To be more precise, we study the mapping defined by
where are real numbers such that , are an infinite family of mappings of C into itself.
Considering , we have the following lemmas which are important in proving our main results.
Lemma 1.1 [20]
Let C be a nonempty closed convex subset of a strictly convex Banach space E. Let be nonexpansive mappings of C into itself such that is nonempty, and let be real numbers such that for any . Then, for every and , the limit exists.
Using Lemma 1.1, one can define the mapping W of C into itself as follows:
Such a W is called the W-mapping generated by and . Throughout this paper, we will assume that , where b is some constant.
Lemma 1.2 [20]
Let C be a nonempty closed convex subset of a strictly convex Banach space E. Let be nonexpansive mappings of C into itself such that is nonempty, and let be real numbers such that for any . Then .
In this paper, based on a general iterative algorithm, we study the problem of approximating a common element in the common fixed point set of an infinite family of nonexpansive mappings, in the solution set of a variational inequality involving an inverse-strongly monotone mapping and in the solution set of an equilibrium problem. Strong convergence of the iterative algorithm is obtained in the framework of Hilbert spaces.
In order to obtain the strong convergence, we need the following tools.
Lemma 1.3 In Hilbert spaces, the following inequality holds:
Lemma 1.4 [21]
Assume that is a sequence of nonnegative real numbers such that
where is a sequence in and is a sequence such that
-
(i)
;
-
(ii)
or .
Then .
Lemma 1.5 [22]
Assume B is a strong positive linear bounded operator on a Hilbert space H with the coefficient and . Then .
Lemma 1.6 [22]
Let H be a Hilbert space. Let B be a strongly positive linear bounded self-adjoint operator with the constant and f be a contraction with the constant κ. Assume that . Let T be a nonexpansive mapping with a fixed point of the contraction . Then converges strongly as to a fixed point of T, which solves the variational inequality
Equivalently, we have .
Let C be a nonempty closed convex subset of H and let B be a bifunction of into ℝ satisfying (A1)-(A4). Let and . Then there exists such that
Define a mapping as follows:
Then the following hold:
-
(1)
is single-valued;
-
(2)
is firmly nonexpansive, i.e., for any ,
-
(3)
;
-
(4)
is closed and convex.
Lemma 1.8 [25]
Let and be bounded sequences in a Banach space X and let be a sequence in with . Suppose for all integers and
Then .
Let K be a nonempty closed convex subset of a Hilbert space H, be a family of infinitely nonexpansive mappings with , be a real sequence such that for each . If C is any bounded subset of K, then .
2 Main results
Theorem 2.1 Let C be a nonempty closed convex subset of a Hilbert space H. Let F be a bifunction from to ℝ which satisfies (A1)-(A4). Let be an infinite family of nonexpansive mappings of C into C. Let be an α-inverse-strongly monotone mapping. Let A be a strongly positive linear bounded self-adjoint operator on H with the coefficient . Assume that and . Let be a κ-contraction. Let be a sequence generated in the following iterative process:
where is generated in (1.3), , are real number sequences in , and are positive real number sequences. Assume that the following restrictions are satisfied:
-
(a)
;
-
(b)
, ;
-
(c)
, ;
-
(d)
, for some s, with .
Then converges strongly to , where , which solves the following variational inequality:
Proof We divide the proof into five steps.
Step 1. Show that the sequence is bounded.
Notice that is nonexpansive. Indeed, we see from the restriction (d) that
which implies the mapping is nonexpansive. Fix . Since , we have
Put
where
It follows that
Since as , we may assume, with no loss of generality, that for all n. It follows that
which yields
This in turn implies that
This completes the proof that the sequence is bounded. This completes the proof of Step 1.
Step 2. Show that .
In view of and , we see that
and
Putting in (2.1) and in (2.2), we find that
and
It follows from (A2) that
That is,
Without loss of generality, let us assume that there exists a real number m such that for all n. It follows that
It follows that
where is some real constant such that .
On the other hand, we have
where . Substituting (2.3) into (2.4) yields
where . Notice that
Since and are nonexpansive, we see from (1.3) that
where is a constant such that . Substituting (2.3), (2.5) and (2.7) into (2.6) yields
where is a constant such that
It follows from the restrictions (b) and (c) that
By virtue of Lemma 1.8, we obtain that
On the other hand, we have
This implies from (2.8) that
This completes the proof of Step 2.
Step 3. Show that .
Notice that . It follows that
This implies from the restriction (b) that
For any , we find that
That is,
This in turn implies that
from which it follows that
It follows from the restriction (b) and (2.9) that
Notice that
On the other hand, we have
Substituting (2.12) into (2.13), we find that
This in turn implies that
It follows from the restrictions (a), (b) and (d) that
On the other hand, we have
which yields
Substituting (2.15) into (2.13) yields
It follows that
In view of the restrictions (a), (b) and (d), we find from (2.9) that
Notice that
In the light of (2.8), (2.10), (2.11) and (2.16), we find that . On the other hand, we have
It follows from Lemma 1.9 that
This completes the proof of Step 3.
Step 4. Show that , where .
To see this, we choose a subsequence of such that
Correspondingly, there exists a subsequence of . Since is bounded, there exists a subsequence of which converges weakly to w. Without loss of generality, we can assume that . Since , we have
It follows from (A2) that
It follows that
In view of the restriction (c), we obtain from (2.11) that
Since , we have from (A4) that for all . For t with and , let . Since and , we have and hence . So, from (A1) and (A4), we have
That is, . It follows from (A3) that for all and hence . On the other hand, we see that . If , then we have the following. Since Hilbert spaces are Opial’s spaces, we find from (2.17) that
which derives a contradiction. Thus, we have . Next, let us first show that . Put
Since B is monotone, we see that S is maximal monotone. Let . Since and , we have
On the other hand, we have from that
That is,
It follows from the above that
which implies from (2.16) that . We have and hence . This completes the proof . On the other hand, we find from (2.18) that
This completes the proof of Step 4.
Step 5. Show .
It follows from Lemma 1.3 that
which implies that
where is a constant such that . On the other hand, we have
Substituting (2.20) into (2.21) yields
Let and
This implies that
In view of the restriction (b), we find from (2.8) and (2.11) that
We can easily draw the desired conclusion with the aid of Lemma 1.4. This completes the proof of Step 5. The proof is completed. □
From Theorem 2.1, we have the following results.
Corollary 2.2 Let C be a nonempty closed convex subset of a Hilbert space H. Let be an infinite family of nonexpansive mappings of C into C. Let be an α-inverse-strongly monotone mapping. Let A be a strongly positive linear bounded self-adjoint operator on H with the coefficient . Assume that and . Let be a κ-contraction. Let be a sequence generated in the following iterative process:
where is generated in (1.3), , are real number sequences in , and are positive real number sequences. Assume that the following restrictions are satisfied:
-
(a)
;
-
(b)
, ;
-
(c)
;
-
(d)
for some s, with .
Then converges strongly to , where , which solves the following variational inequality:
Proof Putting and , we can immediately draw the desired conclusion from Theorem 2.1. □
Corollary 2.3 Let C be a nonempty closed convex subset of a Hilbert space H. Let F be a bifunction from to ℝ, which satisfies (A1)-(A4). Let be an α-inverse-strongly monotone mapping. Let A be a strongly positive linear bounded self-adjoint operator on H with the coefficient . Assume that and . Let be a κ-contraction. Let be a sequence generated in the following iterative process:
where u is a fixed element in C, , are real number sequences in , and are positive real number sequences. Assume that the following restrictions are satisfied:
-
(a)
;
-
(b)
, ;
-
(c)
, ;
-
(d)
, for some s, with .
Then, converges strongly to , where , which solves the following variational inequality:
Proof Putting , where I is the identity mapping and , for all , we can immediately draw the desired conclusion from Theorem 2.1. □
Corollary 2.4 Let C be a nonempty closed convex subset of a Hilbert space H. Let F be a bifunction from to ℝ which satisfies (A1)-(A4). Let be an infinite family of nonexpansive mappings of C into C. Let be an α-inverse-strongly monotone mapping. Assume that . Let be a κ-contraction. Let be a sequence generated in the following iterative process:
where is generated in (1.3), , are real number sequences in , and are positive real number sequences. Assume that the following restrictions are satisfied:
-
(a)
;
-
(b)
, ;
-
(c)
, ;
-
(d)
, for some s, with .
Then converges strongly to , where , which solves the following variational inequality:
Proof Putting , where I is the identity mapping and , we can immediately draw the desired conclusion from Theorem 2.1. □
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Lv, S. Strong convergence of a general iterative algorithm in Hilbert spaces. J Inequal Appl 2013, 19 (2013). https://doi.org/10.1186/1029-242X-2013-19
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DOI: https://doi.org/10.1186/1029-242X-2013-19