Abstract
The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed point set. Recently Censor and Segal proposed an efficient algorithm for solving such a problem. However, to employ their algorithm, one needs to know prior information on the norm of the bounded linear operator. In this paper we propose a new algorithm that does not need any prior information of the operator norm, and we establish the weak convergence of the proposed algorithm under some mild assumptions.
MSC:47J25, 47J20, 49N45, 65J15.
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1 Introduction
There has been growing interest in recent years in the split feasibility problem (SFP) [1]. The SFP is very useful in dealing with problems in signal processing and image reconstruction [2], especially in intensity-modulated radiation therapy [3]. Mathematically, the SFP is an inverse problem that consists in finding with the property
where H and K are two Hilbert spaces, C and Q are nonempty closed convex subsets of H and K, respectively, and is a bounded linear operator. In particular, if C and Q are composed of the fixed point sets of some nonlinear operators, then problem (1.1) is known as the split common fixed point problem (SCFP). More specifically, the SCFP consists in finding
where and stand for, respectively, the fixed point sets of and .
One method solving the SFP is Byrne’s CQ algorithm [4], which generates a sequence by the recursive procedure:
where is known as the step, and , are the orthogonal projections onto C and Q, respectively. When C and Q are simple in the sense that the associated projections are easily calculated, for example the half space, then the CQ algorithm is efficient to solve the problem. However, if C and Q are complex sets, for example the fixed point sets, the efficiency of the CQ algorithm will be affected because the projections onto such convex sets are generally hard to be accurately calculated. Alternatively, Censor and Segal [5] introduced the iterative scheme
solving problem (1.2) for directed operators. Subsequently, this algorithm was extended to the case of quasi-nonexpansive [6] operators, demicontractive operators [7], and finitely many directed operators [8].
Let us now consider the SCFP (1.2) whenever U and T are directed operators. Then, if the step is chosen as
algorithm (1.4) converges to a solution to problem (1.2) whenever such a solution exists. However, in order to implement this algorithm, one has first to compute (or, at least, estimate) the norm of A, which is in general not an easy work in practice. A natural question thus arises: Does there exist a way to select the step in algorithm (1.4) that does not depend on the operator norm ?
It is the purpose of this paper to answer the above question affirmatively. By introducing a new way of selections of the step, we obtain a method in a way that the implementation of algorithm (1.4) does not need any prior information of the operator norm. By using the Fejér monotonicity, we state the weak convergence of the new algorithm for demicontractive operators. Particular cases such as quasi-nonexpansive and directed operators are also considered.
2 Preliminary and notation
Throughout, let I denote the identity operator, denote the set of the fixed points of an operator T, and let denote the set of weak cluster points of the sequence . The notation ‘→’ stands for strong convergence and ‘⇀’ stands for weak convergence. Given a nonempty closed convex subset Q in K, let us define
where is a linear bounded operator.
Definition 2.1 Assume is a nonlinear operator. Then is saide to be demiclosed at zero, if, for any in H, the following implication holds:
It is well known that nonexpansive operators are demiclosed at zero (cf. [9]). Recall that an operator is called nonexpansive if , .
Definition 2.2 Let be an operator with . Then
-
(i)
is called directed if
-
(ii)
is called quasi-nonexpansive if
-
(iii)
is called τ-demicontractive with , if
or equivalently
A typical example of a directed operator is an orthogonal projection from H onto a nonempty closed convex subset defined by
It is well known that the projection is characterized by
Given a sequence in H, then is called Fejér monotone with respect to C, if
The sequence with Fejér monotonicity has the following property.
Lemma 2.3 [10]
If the sequence is Féjer monotone w.r.t. a nonempty closed convex subset C, then converges strongly; moreover,
Lemma 2.4 If is κ-demicontractive, then
where , , and ().
Proof First we deduce that
and using inequality (2.1) we have
Adding up these two formulas yields
which is the inequality as desired. □
Lemma 2.5 Let be a bounded linear operator and a τ-demicontractive operator with . If is nonempty, then
Proof It is clear that , . To see the converse, let such that . Taking ,
where the inequality follows from (2.1), so that . Hence the proof is complete. □
Lemma 2.6 Let be a bounded linear operator and a τ-demicontractive operator with . If , then
where , , and
Proof Take , . Then from the previous lemma ρ is well defined. Since T is τ-demicontractive,
where the inequality follows from (2.1). □
3 A new iterative algorithm
Let us first consider the SCFP (1.2) for demicontractive operators. More specifically, we make use of the following assumptions:
-
is κ-demicontractive with ;
-
is τ-demicontractive with ;
-
both and are demiclosed at zero;
-
it is consistent, i.e., its solution set, denoted by S, is nonempty.
Under these conditions, we propose the following algorithm.
Algorithm 3.1 Choose and an initial guess arbitrarily. Assume that the n th iterate has been constructed; then calculate the th iterate via the formula:
where the step is chosen in such a way that
Remark 3.2 By Lemma 2.5, we see that if and only if . So Algorithm 3.1 is well defined.
Theorem 3.3 Let be the sequence generated by Algorithm 3.1. Then converges weakly to a solution .
Proof First we verify that is Féjer-monotone w.r.t. S. To see this, let and fix . For the case , we have and by (2.4)
which implies because . For the case , we deduce from (2.4)-(2.5) that
Hence we have shown in both cases that . Consequently is Féjer-monotone w.r.t. S and is therefore a convergent sequence.
We next show the following facts:
If , it is clear that , and in view of (3.1)
because is convergent. Otherwise, it follows from (3.2) that
and
This implies that and
so that
and also that
Having in mind that , we conclude that . Consequently (3.3) holds in both cases.
Finally, we show that converges weakly to . By Lemma 2.3, it remains to show that . To see this let and let be a subsequence of converging weakly to . By noting that , we then make use of the demiclosedness of at zero to deduce that ; on the other hand, since, by weak continuity of A, converges weakly to and , this, together with the demiclosedness of at zero, yields . Altogether , and therefore the proof is complete. □
Remark 3.4 By Lemmas 2.3, we see that the weak limit of the sequence generated by Algorithm 3.1 coincides with the limit of the sequence , that is, . In fact, let be the strong limit of the sequence . It follows from (2.3) that
Noting that and , we obtain by sending in (3.4)
Hence, and therefore .
4 Some special cases
4.1 The case for quasi-nonexpansive operators
Consider now the SCFP (1.2) under the following assumptions:
-
and are both quasi-nonexpansive;
-
both and are demiclosed at zero;
-
it is consistent, i.e., its solution set, denoted by S, is nonempty.
Since every quasi-nonexpansive operator is clearly 0-demicontractive, we can state the following result by using Algorithm 3.1.
Algorithm 4.1 Choose and an initial guess arbitrarily. Assume that the n th iterate has been constructed; then calculate the th iterate via the formula:
where the step is chosen in such a way that
Corollary 4.2 Let be the sequence generated by Algorithm 4.1. Then converges weakly to a solution .
4.2 The case for directed operators
Let us consider the SCFP (1.2) under the following assumptions:
-
and are both directed;
-
and are both demiclosed at zero;
-
it is consistent, i.e., its solution set, denoted by S, is nonempty.
A simple calculation shows that every directed operator is −1-demicontractive. Thus we can state the following result by using Algorithm 3.1.
Algorithm 4.3 Choose an initial guess arbitrarily. Assume that the n th iterate has been constructed; then calculate the th iterate via the formula:
where the step is chosen in such a way that
Corollary 4.4 Let be the sequence generated by Algorithm 4.3. Then converges weakly to a solution .
Remark 4.5 Algorithm 4.3 covers the algorithm studied in [11] for solving the SFP. One can further apply the above result to the split variational inequality problem [12, 13] and the split common null point problem [14].
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant Nos. 11301253, 11271112).
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Cui, H., Wang, F. Iterative methods for the split common fixed point problem in Hilbert spaces. Fixed Point Theory Appl 2014, 78 (2014). https://doi.org/10.1186/1687-1812-2014-78
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DOI: https://doi.org/10.1186/1687-1812-2014-78