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# Algorithmic and analytical approach to the split common fixed points problem

- Li-Jun Zhu
^{1}, - Yeong-Cheng Liou
^{2, 3}, - Shin Min Kang
^{4}Email author and - Yonghong Yao
^{5}

**2014**:172

https://doi.org/10.1186/1687-1812-2014-172

© Zhu et al.; licensee Springer. 2014

**Received:**14 March 2014**Accepted:**25 July 2014**Published:**18 August 2014

## Abstract

The split problem, especially the split common fixed point problem, has been studied by many authors. In this paper, we study the split common fixed point problem for the pseudo-contractive mappings and the quasi-nonexpansive mappings. We suggest and analyze an iterative algorithm for solving this split common fixed point problem. A weak convergence theorem is given.

**MSC:**49J53, 49M37, 65K10, 90C25.

## Keywords

- split common fixed point problem
- pseudo-contractive mappings
- quasi-nonexpansive mapping
- weak convergence

## 1 Introduction

*H*. Note that the convex feasibility problem has received a lot of attention due to its extensive applications in many applied disciplines as diverse as approximation theory, image recovery and signal processing, control theory, biomedical engineering, communications, and geophysics (see [1–3] and the references therein). A special case of the convex feasibility problem is the split feasibility problem, which is to find a point ${x}^{\ast}$ such that

*C*and

*Q*are two closed convex subsets of two Hilbert spaces ${H}_{1}$ and ${H}_{2}$, respectively, and $A:{H}_{1}\to {H}_{2}$ is a bounded linear operator. Such problems arise in the field of intensity-modulated radiation therapy when one attempts to describe physical dose constraints and equivalent uniform dose constraints within a single model; see [4]. The problem with only a single pair of sets $C\in {\mathbb{R}}^{N}$ and $Q\in {\mathbb{R}}^{M}$ was first introduced by Censor and Elfving [5]. They used their simultaneous multi-projections algorithm to solve the split feasibility problem. Their algorithms, as well as others, see,

*e.g.*, Byrne [6], involve matrix inversion at each iterative step. Calculating inverses of matrices is very time-consuming, particularly if the dimensions are large. Therefore, a new algorithm for solving the split feasibility problem was devised by Byrne [7], called the CQ-algorithm:

where $\tau \in (0,\frac{2}{L})$ with *L* being the largest eigenvalue of the matrix ${A}^{\ast}A$, *I* is the unit matrix or operator and ${P}_{C}$ and ${P}_{Q}$ denote the orthogonal projections onto *C* and *Q*, respectively. In the case of nonlinear constraint sets, orthogonal projections may demand a great amount of work of solving a nonlinear optimization problem to minimize the distance between the point and the constraint set. However, it can easily be estimated by linear approximation using the current constraint violation and the sub-gradient at the current point. This was done by Yang, in his recent paper [8], where he proposed a relaxed version of the CQ-algorithm in which orthogonal projections are replaced by sub-gradient projections, which are easily executed when the sets *C* and *Q* are given as lower level sets of convex functions; see also [9]. There are a large number of references on the CQ method for the split feasibility problem in the literature; see, for instance, [10–25].

It is our main purpose in this paper to develop algorithms for the split common fixed point for the pseudo-contractive and quasi-nonexpansive mappings. Weak convergence theorem is given. Our results improve and develop previously discussed feasibility problems and related algorithms.

## 2 Preliminaries

Let *H* be a real Hilbert space with inner product $\u3008\cdot ,\cdot \u3009$ and norm $\parallel \cdot \parallel $, respectively. Let *C* be a nonempty closed convex subset of *H*.

**Definition 2.1**A mapping $S:C\to C$ is called

*pseudo*-

*contractive*if

for all $x,y\in C$.

We will use $Fix(S)$ to denote the set of fixed points of *S*, that is, $Fix(S)=\{x\in C:x=Sx\}$.

**Remark 2.2**It is clear that

*S*is pseudo-contractive if and only if

for all $x,y\in C$.

Interest in pseudo-contractive mappings stems mainly from their firm connection with the class of nonlinear accretive operators. It is a classical result, see Deimling [26], that if *S* is an accretive operator, then the solutions of the equations $Sx=0$ correspond to the equilibrium points of some evolution systems.

**Definition 2.3**A mapping $T:C\to C$ is called

*L*-

*Lipschitzian*if there exists $L>0$ such that

for all $x,y\in C$.

**Remark 2.4** We call *T* *nonexpansive* if $L=1$ and *T* is *contractive* if $L<1$.

**Definition 2.5**A mapping $T:C\to C$ is called

*quasi*-

*nonexpansive*if

**Remark 2.6** It is obvious that if *T* is nonexpansive with $Fix(T)\ne \mathrm{\varnothing}$, then *T* is quasi-nonexpansive.

Usually, the convergence of fixed point algorithms requires some additional smoothness properties of the mapping *T* such as demi-closedness.

**Definition 2.7** A mapping *T* is said to be *demi*-*closed* if, for any sequence $\{{x}_{k}\}$ which weakly converges to $\tilde{x}$, and if the sequence $\{T({x}_{k})\}$ strongly converges to *z*, then $T(\tilde{x})=z$.

*H*, the following equality holds:

for all $x,y\in H$ and $t\in [0,1]$.

**Lemma 2.8** ([27])

*Let*

*H*

*be a real Hilbert space*,

*C*

*a closed convex subset of*

*H*.

*Let*$U:C\to C$

*be a continuous pseudo*-

*contractive mapping*.

*Then*

- (i)
$Fix(U)$

*is a closed convex subset of**C*, - (ii)
$(I-U)$

*is demi*-*closed at zero*.

**Lemma 2.9** ([28])

*Let*

*H*

*be a Hilbert space and let*$\{{u}_{n}\}$

*be a sequence in*

*H*

*such that there exists a nonempty set*$\mathrm{\Omega}\subset H$

*satisfying the following*:

- (i)
*for every*$u\in \mathrm{\Omega}$, ${lim}_{n}\parallel {u}_{n}-u\parallel $*exists*, - (ii)
*any weak*-*cluster point of the sequence*$\{{u}_{n}\}$*belongs in*Ω.

*Then there exists* ${x}^{\u2020}\in \mathrm{\Omega}$ *such that* $\{{u}_{n}\}$ *weakly converges to* ${x}^{\u2020}$.

- 1.
${\omega}_{w}({u}_{n})=\{x:\mathrm{\exists}{u}_{{n}_{j}}\to x\text{weakly}\}$ denote the weak

*ω*-limit set of $\{{u}_{n}\}$; - 2.
${u}_{n}\rightharpoonup x$ stands for the weak convergence of $\{{u}_{n}\}$ to

*x*; - 3.
${u}_{n}\to x$ stands for the strong convergence of $\{{u}_{n}\}$ to

*x*.

## 3 Main results

where $\gamma \in (0,\frac{2}{\lambda})$, with *λ* being the largest eigenvalue of the matrix ${A}^{\ast}A$.

where $\beta \in (0,1)$, ${\alpha}_{k}\in (0,1)$ are relaxation parameters.

Inspired by their works, we introduce the following algorithm.

**Algorithm 3.1**Let ${H}_{1}$ and ${H}_{2}$ be two real Hilbert spaces. Let $A:{H}_{1}\to {H}_{2}$ be a bounded linear operator. Let $U:{H}_{1}\to {H}_{1}$ be a pseudo-contractive mapping with Lipschitzian constant

*L*and $T:{H}_{2}\to {H}_{2}$ be a quasi-nonexpansive mapping with nonempty $Fix(U)=C$ and $Fix(T)=Q$. Let ${x}_{0}\in {H}_{1}$. Define a sequence $\{{u}_{n}\}$ as follows:

for all $n\in \mathbb{N}$, where *γ* and *ν* are two constants, $\{{\alpha}_{n}\}$, $\{{\delta}_{n}\}$, and $\{{\xi}_{n}\}$ are three sequences in $[0,1]$.

In the sequel, we assume the parameters satisfy the following restrictions.

**Parameters restrictions:**

(R_{1}): $0<\nu <1$ and $0<\gamma <\frac{1}{\lambda \nu}$, where *λ* is the largest eigenvalue of the matrix ${A}^{\ast}A$;

(R_{2}): $0<{lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}{\alpha}_{n}\le {lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$;

(R_{3}): $0<k\le 1-{\delta}_{n}\le {\xi}_{n}<\frac{1}{\sqrt{1+{L}^{2}}+1}$ for all $n\in \mathbb{N}$, where *L* is the Lipschitz constant of *U*.

**Remark 3.2** Without loss of generality, we may assume that the Lipschitz constant $L>1$. It is obvious that $\frac{1}{\sqrt{1+{L}^{2}}+1}<\frac{1}{L}$ for all $n\ge 1$. Since ${\xi}_{n}<\frac{1}{\sqrt{1+{L}^{2}}+1}$, we have $1-2{\xi}_{n}-{\xi}_{n}^{2}{L}^{2}>0$ for all $n\in \mathbb{N}$.

**Theorem 3.3** *Let* ${H}_{1}$ *and* ${H}_{2}$ *be two real Hilbert spaces*. *Let* $A:{H}_{1}\to {H}_{2}$ *be a bounded linear operator*. *Let* $U:{H}_{1}\to {H}_{1}$ *be a pseudo*-*contractive mapping with Lipschitzian constant* *L* *and* $T:{H}_{2}\to {H}_{2}$ *be a quasi*-*nonexpansive mapping with nonempty* $Fix(U)=C$ *and* $Fix(T)=Q$. *Assume that* $T-I$ *is demi*-*closed at* 0 *and* $\mathrm{\Gamma}\ne \mathrm{\varnothing}$. *Then the sequence* $\{{u}_{n}\}$ *generated by algorithm* (3.3) *weakly converges to a split common fixed point* $\mu \in \mathrm{\Gamma}$.

*Proof*Let ${x}^{\ast}\in \mathrm{\Gamma}$. Then we get ${x}^{\ast}\in Fix(U)$ and $A{x}^{\ast}\in Fix(T)$. From (2.2) and (3.3), we have

for all $x\in C$.

*U*is

*L*-Lipschitzian and ${x}_{n}-{y}_{n}={\xi}_{n}(U{x}_{n}-{x}_{n})$. Then we have

*λ*is the spectral radius of the operator $A{A}^{\ast}$, we deduce

*T*is quasi-nonexpansive and $A{x}^{\ast}\in Fix(T)$, we have

Thus, $A\mu \in Fix(T)$.

*U*is

*L*-Lipschitzian, we have

Hence, $\mu \in Fix(U)$. Therefore, $\mu \in \mathrm{\Gamma}$. Since there is no more than one weak-cluster point, the weak convergence of the whole sequence $\{{u}_{n}\}$ follows by applying Lemma 2.9 with $\mathrm{\Omega}=\mathrm{\Gamma}$. This completes the proof. □

**Example 3.4**Let $H=\mathbb{R}$ with the inner product defined by $\u3008x,y\u3009=xy$ for all $x,y\in \mathbb{R}$ and the standard norm $|\cdot |$. Let $C=[0,+\mathrm{\infty})$ and $Tx=\frac{{x}^{2}+2}{1+x}$ for all $x\in C$. Obviously, $Fix(T)=2$. It is easy to see that

Hence, *T* is a continuous quasi-nonexpansive mapping but not nonexpansive.

**Example 3.5**Let $H=\mathbb{R}$ with the inner product defined by $\u3008x,y\u3009=xy$ for all $x,y\in \mathbb{R}$ and the standard norm $|\cdot |$. Let $C=[0,+\mathrm{\infty})$ and let $Ux=x-1+\frac{4}{x+1}$ for all $x\in C$. Observe that $Fix(U)=3$. It is easy to see that

for all $x,y\in C$.

Hence, *U* is a Lipschitzian pseudo-contractive mapping but it is not nonexpansive.

## Declarations

### Acknowledgements

The authors are grateful to the five reviewers for their valuable comments and suggestions. Li-Jun Zhu was supported in part by NNSF of China (61362033) and NZ13087. Yeong-Cheng Liou was supported in part by NSC 101-2628-E-230-001-MY3 and NSC 101-2622-E-230-005-CC3.

## Authors’ Affiliations

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