Strong Convergence Theorems for the Generalized Split Common Fixed Point Problem
Cuijie Zhang1
Academic Editor: Rudong Chen
Received09 Jan 2012
Accepted17 Feb 2012
Published08 May 2012
Abstract
We introduce the generalized split common fixed point problem (GSCFPP) and show that the GSCFPP for nonexpansive operators is equivalent to the common fixed point problem. Moreover, we introduce a new iterative algorithm for finding a solution of the GSCFPP and obtain some strong convergence theorems under suitable assumptions.
1. Introduction
Let and be real Hilbert spaces and let be a bounded linear operator. Given intergers , let us recall that the multiple-set split feasibility problem (MSSFP) was recently introduced [1] and is to find a point:
where and are nonempty closed convex subsets of and , respectively. If , the MSSFP (1.1) becomes the so-called split feasibility problem (SFP) [2] which is to find a point:
where and are nonempty closed convex subsets of and , respectively. Recently, the SFP (1.2) and MSSFP (1.1) have been investigated by many researchers; see, [3β10].
Since every closed convex subset in a Hilbert space is looked as the fixed point set of its associating projection, the MSSFP (1.1) becomes a special case of the split common fixed point problem (SCFPP), which is to find a point:
where and are nonlinear operators. If , the problem (1.3) reduces to the so-called two-set SCFPP, which is to find a point:
Censor and Segal in [11] firstly introduced the concept of SCFPP in finite-dimensional Hilbert spaces and considered the following iterative algorithm for the two-set SCFPP for Class- operators:
where , and is the identity operator. They proved the convergence of the algorithm (1.5) to a solution of problem (1.4). Moreover, they introduced a parallel iterative algorithm, which converges to a solution of the SCFPP (1.3). However, the parallel iterative algorithm does not include the algorithm (1.5) as a special case.
Very recently, Wang and Xu in [12] considered the SCFPP (1.3) for Class- operators and introduced the following iterative algorithm for solving the SCFPP (1.3):
Under some mild conditions, they proved some weak and strong convergence theorems. Their iterative algorithm (1.6) includes Censor and Segalβs algorithm (1.5) as a special case for the two-set SCFPP (1.4). Moreover, they prove that the SCFPP (1.3) for the Class- operators is equivalent to a common fixed point problem. This is also a classical method. Many problems eventually converted to a common fixed point problem; see [13β15].
Motivated and inspired by the aforementioned research works, we introduce a generalized split common fixed point problem (GSCFPP) which is to find a point:
Then, we show that the GSCFPP (1.7) for nonexpansive operators is equivalent to the following common fixed point problem:
where for every . Moreover, we give a new iterative algorithm for solving the GSCFPP (1.7) for nonexpansive operators and obtain some strong convergence theorems.
2. Preliminaries
Throughout this paper, we write and to indicate that converges weakly to and converges strongly to , respectively.
An operator is said to be nonexpansive if for all . The set of fixed points of is denoted by . It is known that is closed and convex. An operator is called contraction if there exists a constant such that for all . Let be a nonempty closed convex subset of . For each , there exists a unique nearest point in , denoted by , such that for every . is called a metric projection of onto . It is known that for each ,
for all .
Let be a sequence of operators of into itself. The set of common fixed points of is denoted by , that is, . A sequence is said to be strongly nonexpansive if each is nonexpansive and
whenever and are sequences in such that is bounded and ; see [16, 17]. A sequence in is said to be an approximate fixed point sequence of if . The set of all bounded approximate fixed point sequences of is denoted by ; see [16, 17]. We know that if has a common fixed point, then is nonempty; that is, every bounded sequence in the common fixed point set is an approximate fixed point sequence. A sequence with a common fixed point is said to satisfy the condition () if every weak cluster point of is a common fixed point whenever . A sequence of nonexpansive mappings of into itself is said to satisfy the condition () if
for every nonempty bounded subset of ; see [18].
In order to prove our main results, we collect the following lemmas in this section.
Lemma 2.1 (see [16]). Let be a nonempty subset of a Hilbert space . Let be a sequence of nonexpansive mappings of into . Let be a sequence in such that . Let be a sequence of mappings of into defined by for , where is the identity mapping on . Then is a strongly nonexpansive sequence.
Lemma 2.2 (see [16]). Let be a Hilbert space, a nonempty subset of , and and sequences of nonexpansive self-mappings of . Suppose that or is a strongly nonexpansive sequence and is nonempty. Then .
Lemma 2.3 (see [17]). Let be a Hilbert space, and a nonempty subset of . Both and satisfy the condition and is bounded for any bounded subset of . Then satisfies the condition .
Lemma 2.4 (see [19]). Let and be bounded sequences in a Banach space and let be a sequence in with . Suppose for all integers and
Then .
Lemma 2.5 (see [20]). 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 .
3. Main Results
Now we state and prove our main results of this paper.
Lemma 3.1. Let be a given bounded linear operator and let be a sequence of nonexpansive operators. Assume
For each constant , is defined by the following:
Then . Moreover, for , is nonexpansive on for .
Proof. Since the inclusion is evident, now we only need to show the converse inclusion. If , then we have . Since , we take an arbitrary . Hence
It follows that , then for every , hence . Next we turn to show that is a nonexpansive operator for . Since is nonexpansive, we have
Hence
For , we can immediately obtain that is a nonexpansive operator for every .
From Lemma 3.1, we can obtain that the solution set of GSCFPP (1.7) is identical to the solution set of problem (1.8).
Theorem 3.2. Let and be sequences of nonexpansive operators on Hilbert space . Both and satisfy the conditions and . Let be a contraction with coefficient . Suppose . Take an initial guess and define a sequence by the following algorithm:
where , , , and are sequences in . If the following conditions are satisfied:(i);(ii)ββandββ ;(iii);(iv);(v), then converges strongly to where .
Proof. We proceed with the following steps. Step 1. First show that there exists such that . In fact, since is a contraction with coefficient , we have
for every , . Hence is also a contraction. Therefore, there exists a unique such that .Step 2. Now we show that is bounded. Let , then and . Hence
Then
By induction on ,
for every . This shows that and are bounded, and hence, , , and are also bounded.Step 3. We claim that and , where . We first show the former equality. Let be a bounded sequence in . If , then
Hence . On the other hand, if , combining (3.11) and , we obtain that . Hence . Therefore, . Next, we show the latter equality. Using Lemma 2.1, we know that is a strongly nonexpansive sequence. Thus, since , from Lemma 2.2 we have
Step 4. satisfies the condition , where . Let be a nonempty bounded subset of . From the definition of , we have, for all ,
It follows that
Since satisfies the condition and , we have
that is, satisfies the condition . Since is bounded for any bounded subset of , by using Lemma 2.3, we have that satisfies the condition , that is, satisfies the condition .Step 5. We show . We can write (3.6) as where . It follows that
From Step 2, we may assume that , where is a bounded set of . Then from (3.16), we obtain
It follows that
Since satisfies the condition , combining as , we have
Hence by Lemma 2.4, we get as . Consequently,
Step 6. We claim that . From (3.6), we have
and hence
Since , and , we derive
Thus (3.23) and Steps 2 and 3 imply that
Step 7. Show , where . Since is bounded, there exist a point and a subsequence of such that
and . Since and satisfy the condition , from Step 6, we have . Using (2.1), we get
Step 8. Show . Since , using (3.8), we have
which implies that
for every . Consequently, according to Step 7, , and Lemma 2.5, we deduce that converges strongly to . This completes the proof.
Combining Lemma 3.1 and Theorem 3.2, we can obtain the following strong convergence theorem for solving the GSCFPP (1.7).
Theorem 3.3. Let and be sequences of nonexpansive operators on Hilbert space and , respectively. Both and satisfy the conditions and . Let be a contraction with coefficient . Suppose that the solution set of GSCFPP (1.7) is nonempty. Take an initial guess and define a sequence by the following algorithm:
where , and , , , are sequences in . If the following conditions are satisfied:(i);(ii)ββandββ;(iii);(iv);(v), then converges strongly to where .
Proof. Set . By Lemma 3.1, is a nonexpansive operator for every . We can rewrite (3.29) as
We only need to prove that satisfies the conditions and . Assume that is a nonempty bounded subset of . For every , we have
Since satisfies the condition , and is bounded, it follows from (3.31) that
Therefore, satisfies the condition . Assume that and ; we next show that . By using , we have . Since , we choose an arbitrary point ; then for every ,
Hence
Then we get . Since satisfies the condition and , we have . From Lemma 3.1, we have .
Let be a nonexpansive mapping with a fixed point, and define for all . Then satisfies the conditions and . Thus, one obtains the algorithm for solving the two-set SCFPP (1.4).
Corollary 3.4. Let and be nonexpansive operators on Hilbert space and , respectively. Let be a contraction with coefficient . Suppose that the solution set of SCFPP (1.4) is nonempty. Take an initial guess and define a sequence by the following algorithm in (3.29), where , and , , , are sequences in . If the following conditions are satisfied:(i);(ii) and ;(iii);(iv);(v). Then converges strongly to where .
Remark 3.5. By adding more operators to the families and by setting for and for , the SCFPP (1.3) can be viewed as a special case of the GSCFPP (1.7).
Acknowledgment
This research is supported by the science research foundation program in Civil Aviation University of China (07kys09), the Fundamental Research Funds for the Central Universities (Program No. ZXH2011D005), and the NSFC Tianyuan Youth Foundation of Mathematics of China (No. 11126136).
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