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Iterative methods for the split common fixed point problem in Hilbert spaces
Fixed Point Theory and Applications volume 2014, Article number: 78 (2014)
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.
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 intensitymodulated radiation therapy [3]. Mathematically, the SFP is an inverse problem that consists in finding $\stackrel{\u02c6}{x}$ 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 $A:H\to K$ 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 $Fix(U)$ and $Fix(T)$ stand for, respectively, the fixed point sets of $U:H\to H$ and $T:K\to K$.
One method solving the SFP is Byrne’s CQ algorithm [4], which generates a sequence $({x}_{n})$ by the recursive procedure:
where ${\rho}_{n}$ is known as the step, and ${P}_{C}$, ${P}_{Q}$ 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 quasinonexpansive [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 $({\rho}_{n})$ 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 ${\rho}_{n}$ in algorithm (1.4) that does not depend on the operator norm $\parallel A\parallel $?
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 quasinonexpansive and directed operators are also considered.
2 Preliminary and notation
Throughout, let I denote the identity operator, $Fix(T)$ denote the set of the fixed points of an operator T, and let ${\omega}_{w}({x}_{n})$ denote the set of weak cluster points of the sequence $({x}_{n})$. The notation ‘→’ stands for strong convergence and ‘⇀’ stands for weak convergence. Given a nonempty closed convex subset Q in K, let us define
where $A:H\to K$ is a linear bounded operator.
Definition 2.1 Assume $T:H\to H$ is a nonlinear operator. Then $IT$ is saide to be demiclosed at zero, if, for any $({x}_{n})$ in H, the following implication holds:
It is well known that nonexpansive operators are demiclosed at zero (cf. [9]). Recall that an operator $T:H\to H$ is called nonexpansive if $\parallel TxTy\parallel \le \parallel xy\parallel $, $\mathrm{\forall}x,y\in H$.
Definition 2.2 Let $T:H\to H$ be an operator with $Fix(T)\ne \mathrm{\varnothing}$. Then

(i)
$T:H\to H$ is called directed if
$$\u3008zTx,xTx\u3009\le 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}z\in Fix(T),x\in H;$$ 
(ii)
$T:H\to H$ is called quasinonexpansive if
$$\parallel Txz\parallel \le \parallel xz\parallel ,\phantom{\rule{1em}{0ex}}\mathrm{\forall}z\in Fix(T),x\in H;$$ 
(iii)
$T:H\to H$ is called τdemicontractive with $\tau <1$, if
$${\parallel Txz\parallel}^{2}\le {\parallel xz\parallel}^{2}+\tau {\parallel (IT)x\parallel}^{2},\phantom{\rule{1em}{0ex}}\mathrm{\forall}z\in Fix(T),x\in H,$$
or equivalently
A typical example of a directed operator is an orthogonal projection ${P}_{C}$ from H onto a nonempty closed convex subset $C\subset H$ defined by
It is well known that the projection ${P}_{C}$ is characterized by
Given a sequence $({x}_{n})$ in H, then $({x}_{n})$ 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 $({x}_{n})$ is Féjer monotone w.r.t. a nonempty closed convex subset C, then $\{{P}_{C}{x}_{n}\}$ converges strongly; moreover,
Lemma 2.4 If $U:H\to H$ is κdemicontractive, then
where $x\in H$, $z\in Fix(U)$, and ${U}_{\lambda}:=(1\lambda )I+\lambda U$ ($0<\lambda <1\kappa $).
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 $A:H\to K$ be a bounded linear operator and $T:K\to K$ a τdemicontractive operator with $\tau <1$. If ${A}^{1}(Fix(T))$ is nonempty, then
Proof It is clear that $(IT)Ax=0\Rightarrow {A}^{\ast}(IT)Ax=0$, $x\in H$. To see the converse, let $x\in H$ such that ${A}^{\ast}(IT)Ax=0$. Taking $z\in {A}^{1}(Fix(T))$,
where the inequality follows from (2.1), so that $Ax=T(Ax)$. Hence the proof is complete. □
Lemma 2.6 Let $A:H\to K$ be a bounded linear operator and $T:K\to K$ a τdemicontractive operator with $\tau <1$. If ${A}^{1}(Fix(T))\ne \mathrm{\varnothing}$, then
where $x\in H$, $Ax\ne T(Ax)$, $z\in {A}^{1}(Fix(T))$ and
Proof Take $x\in H$, $Ax\ne T(Ax)$. Then from the previous lemma ρ is well defined. Since T is τdemicontractive,
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:

$U:H\to H$ is κdemicontractive with $\kappa <1$;

$T:K\to K$ is τdemicontractive with $\tau <1$;

both $IU$ and $IT$ 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 $0<\lambda <1\tau $ and an initial guess ${x}_{0}\in H$ arbitrarily. Assume that the n th iterate ${x}_{n}$ has been constructed; then calculate the $(n+1)$th iterate ${x}_{n+1}$ via the formula:
where the step ${\rho}_{n}$ is chosen in such a way that
Remark 3.2 By Lemma 2.5, we see that $A{x}_{n}=T(A{x}_{n})$ if and only if ${A}^{\ast}(IT)A{x}_{n}=0$. So Algorithm 3.1 is well defined.
Theorem 3.3 Let $({x}_{n})$ be the sequence generated by Algorithm 3.1. Then $({x}_{n})$ converges weakly to a solution ${x}^{\ast}\in S$.
Proof First we verify that $({x}_{n})$ is Féjermonotone w.r.t. S. To see this, let ${y}_{n}:={x}_{n}{\rho}_{n}{A}^{\ast}(IT)A{x}_{n}$ and fix $z\in S$. For the case ${\rho}_{n}=0$, we have ${y}_{n}={x}_{n}$ and by (2.4)
which implies $\parallel {x}_{n+1}z\parallel \le \parallel {x}_{n}z\parallel $ because $0<\lambda <1\kappa $. For the case ${\rho}_{n}\ne 0$, we deduce from (2.4)(2.5) that
Hence we have shown in both cases that $\parallel {x}_{n+1}z\parallel \le \parallel {x}_{n}z\parallel $. Consequently $({x}_{n})$ is Féjermonotone w.r.t. S and $({\parallel {x}_{n}z\parallel}^{2})$ is therefore a convergent sequence.
We next show the following facts:
If ${\rho}_{n}=0$, it is clear that $(IT)A{x}_{n}=0$, and in view of (3.1)
because $({\parallel {x}_{n}z\parallel}^{2})$ is convergent. Otherwise, it follows from (3.2) that
and
This implies that $\parallel (IU){y}_{n}\parallel \to 0$ and
so that
and also that
Having in mind that $\parallel (IU){y}_{n}\parallel \to 0$, we conclude that $\parallel (IU){x}_{n}\parallel \to 0$. Consequently (3.3) holds in both cases.
Finally, we show that $({x}_{n})$ converges weakly to ${x}^{\ast}\in S$. By Lemma 2.3, it remains to show that ${\omega}_{w}({x}_{n})\subseteq S$. To see this let $\stackrel{\u02c6}{x}\in {\omega}_{w}({x}_{n})$ and let $\{{x}_{{n}_{k}}\}$ be a subsequence of $({x}_{n})$ converging weakly to $\stackrel{\u02c6}{x}$. By noting that $\parallel (IU){x}_{{n}_{k}}\parallel \to 0$, we then make use of the demiclosedness of $IU$ at zero to deduce that $\stackrel{\u02c6}{x}\in Fix(U)$; on the other hand, since, by weak continuity of A, $A{x}_{{n}_{k}}$ converges weakly to $A\stackrel{\u02c6}{x}$ and $\parallel (IT)A{x}_{{n}_{k}}\parallel \to 0$, this, together with the demiclosedness of $IT$ at zero, yields $A\stackrel{\u02c6}{x}\in Fix(T)$. Altogether $\stackrel{\u02c6}{x}\in S$, and therefore the proof is complete. □
Remark 3.4 By Lemmas 2.3, we see that the weak limit ${x}^{\ast}$ of the sequence $({x}_{n})$ generated by Algorithm 3.1 coincides with the limit of the sequence $({P}_{S}{x}_{n})$, that is, ${x}^{\ast}={lim}_{n\to \mathrm{\infty}}{P}_{S}{x}_{n}$. In fact, let $\stackrel{\u02c6}{x}$ be the strong limit of the sequence $({P}_{C}{x}_{n})$. It follows from (2.3) that
Noting that ${x}_{n}{P}_{C}{x}_{n}\rightharpoonup {x}^{\ast}\stackrel{\u02c6}{x}$ and ${x}^{\ast}{P}_{C}{x}_{n}\to {x}^{\ast}\stackrel{\u02c6}{x}$, we obtain by sending $n\to \mathrm{\infty}$ in (3.4)
Hence, ${x}^{\ast}=\stackrel{\u02c6}{x}$ and therefore ${x}^{\ast}={lim}_{n\to \mathrm{\infty}}{P}_{S}{x}_{n}$.
4 Some special cases
4.1 The case for quasinonexpansive operators
Consider now the SCFP (1.2) under the following assumptions:

$U:H\to H$ and $T:K\to K$ are both quasinonexpansive;

both $IU$ and $IT$ are demiclosed at zero;

it is consistent, i.e., its solution set, denoted by S, is nonempty.
Since every quasinonexpansive operator is clearly 0demicontractive, we can state the following result by using Algorithm 3.1.
Algorithm 4.1 Choose $0<\lambda <1$ and an initial guess ${x}_{0}\in H$ arbitrarily. Assume that the n th iterate ${x}_{n}$ has been constructed; then calculate the $(n+1)$th iterate ${x}_{n+1}$ via the formula:
where the step ${\rho}_{n}$ is chosen in such a way that
Corollary 4.2 Let $({x}_{n})$ be the sequence generated by Algorithm 4.1. Then $({x}_{n})$ converges weakly to a solution ${x}^{\ast}\in S$.
4.2 The case for directed operators
Let us consider the SCFP (1.2) under the following assumptions:

$U:H\to H$ and $T:K\to K$ are both directed;

$IU$ and $IT$ 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 −1demicontractive. Thus we can state the following result by using Algorithm 3.1.
Algorithm 4.3 Choose an initial guess ${x}_{0}\in H$ arbitrarily. Assume that the n th iterate ${x}_{n}$ has been constructed; then calculate the $(n+1)$th iterate ${x}_{n+1}$ via the formula:
where the step ${\rho}_{n}$ is chosen in such a way that
Corollary 4.4 Let $({x}_{n})$ be the sequence generated by Algorithm 4.3. Then $({x}_{n})$ converges weakly to a solution ${x}^{\ast}\in S$.
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].
References
 1.
Censor Y, Elfving T: A multiprojection algorithms using Bregman projection in a product space. Numer. Algorithms 1994, 8: 221–239. 10.1007/BF02142692
 2.
Byrne C: A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 2004, 18: 103–120.
 3.
Censor Y, Bortfeld T, Martin B, Trofimov A: A unified approach for inversion problems in intensitymodulated radiation therapy. Phys. Med. Biol. 2006, 51: 2353–2365. 10.1088/00319155/51/10/001
 4.
Byrne C: Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 2002, 18: 441–453. 10.1088/02665611/18/2/310
 5.
Censor Y, Segal A: The split common fixed point problem for directed operators. J. Convex Anal. 2009, 16: 587–600.
 6.
Moudafi A: A note on the split common fixedpoint problem for quasinonexpansive operators. Nonlinear Anal. 2011, 74(12):4083–4087. 10.1016/j.na.2011.03.041
 7.
Moudafi A: The split common fixed point problem for demicontractive mappings. Inverse Probl. 2010., 26: Article ID 055007
 8.
Wang F, Xu HK: Cyclic algorithms for split feasibility problems in Hilbert spaces. Nonlinear Anal. 2011, 74: 4105–4111. 10.1016/j.na.2011.03.044
 9.
Goebel K, Kirk WA 28. In Topics in Metric Fixed Point Theory. Cambridge University Press, Cambridge; 1990.
 10.
Bauschke HH, Borwein JM: On projection algorithms for solving convex feasibility problems. SIAM Rev. 1996, 38: 367–426. 10.1137/S0036144593251710
 11.
López G, MartínMárquez V, Wang F, Xu HK: Solving the split feasibility problem without prior knowledge of matrix norms. Inverse Probl. 2012., 28: Article ID 085004
 12.
Censor Y, Gibali A, Reich S: Algorithms for the split variational inequality problem. Numer. Algorithms 2012, 59(2):301–323. 10.1007/s1107501194905
 13.
Moudafi A: Split monotone variational inclusions. J. Optim. Theory Appl. 2011, 150: 275–283. 10.1007/s1095701198146
 14.
Byrne C, Censor Y, Gibali A, Reich S: The split common null point problem. J. Nonlinear Convex Anal. 2011, 13: 759–775.
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/10.1186/16871812201478
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Keywords
 split common fixed point problem
 directed operators
 demicontractive operator
 quasinonexpansive operator