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Simultaneous iterative algorithms for the split common fixed-point problem of generalized asymptotically quasi-nonexpansive mappings without prior knowledge of operator norms

Abstract

Let H 1 , H 2 , H 3 be real Hilbert spaces, let A: H 1 H 3 , B: H 2 H 3 be two bounded linear operators. Moudafi introduced simultaneous iterative algorithms (Trans. Math. Program. Appl. 1:1-11, 2013) with weak convergence for the following split common fixed-point problem:

find xF(U),yF(T)such thatAx=By,
(1)

where U: H 1 H 1 and T: H 2 H 2 are two firmly quasi-nonexpansive operators with nonempty fixed-point sets F(U)={x H 1 :Ux=x} and F(T)={x H 2 :Tx=x}. Note that by taking H 2 = H 3 and B=I, we recover the split common fixed-point problem originally introduced in Censor and Segal (J. Convex Anal. 16:587-600, 2009). In this paper, we will continue to consider the split common fixed-point problem (1) governed by the general class of generalized asymptotically quasi-nonexpansive mappings. To estimate the norm of an operator is a very difficult, if it is not an impossible task. The purpose of this paper is to propose a simultaneous iterative algorithm with a way of selecting the stepsizes such that the implementation of the algorithm does not need any prior information as regards the operator norms.

MSC:47H09, 47H10, 47J05, 54H25.

1 Introduction and preliminaries

Throughout this paper, we always assume that H is a real Hilbert space with the inner product , and the norm . Let T:HH be a mapping. A point xH is said to be a fixed point of T provided Tx=x. In this paper, we use F(T) to denote the fixed point set and use → and to denote the strong convergence and weak convergence, respectively. We use ω w ( x k )={x: x k j x} stand for the weak ω-limit set of { x k }.

Let C and Q be nonempty closed convex subset of real Hilbert spaces H 1 and H 2 , respectively. The split feasibility problem (SFP) is to find a point

xCsuch thatAxQ,
(1.1)

where A: H 1 H 2 is a bounded linear operator. The SFP in finite-dimensional Hilbert spaces was first introduced by Censor and Elfving [1] for modeling inverse problems which arise from phase retrievals and in medical image reconstruction [2]. Recently, it has been found that the SFP can also be used in various disciplines such as image restoration, computer tomograph and radiation therapy treatment planning [35]. Various algorithms have been invented to solve it, etc. (see [69] and references therein).

Note that if the split feasibility problem (1.1) is consistent (i.e., (1.1) has a solution), then (1.1) can be formulated as a fixed point equation by using the fact

P C ( I γ A ( I P Q ) A ) x = x .
(1.2)

That is, x solves the SFP (1.1) if and only if x solves the fixed point equation (1.2) (see [10] for the details). This implies that we can use fixed point algorithms (see [1013]) to solve SFP. A popular algorithm that solves the SFP (1.1) is due to Byrne’s CQ algorithm [2], which is found to be a gradient-projection method (GPM) in convex minimization.

In [14], Censor and Segal consider the following split common fixed-point problem (SCFP):

find  x F(U)such thatA x F(T),
(1.3)

where A: H 1 H 2 is a bounded linear operator, U: H 1 H 1 and T: H 2 H 2 are two nonexpansive operators with nonempty fixed-point sets. To solve (1.3), Censor and Segal [14] proposed and proved, in finite-dimensional spaces, the convergence of the following algorithm:

x k + 1 =U ( x k + γ A t ( T I ) A x k ) ,kN,

where γ(0, 2 λ ), with λ being the largest eigenvalue of the matrix A t A ( A t stands for matrix transposition). SCFP (1.3) is in itself at the core of the modeling of many inverse problems in various areas of mathematics and physical sciences and has been used to model significant real-world inverse problems in sensor networks, in radiation therapy treatment planning, in resolution enhancement, in wavelet-based denoising, in antenna design, in computerized tomography, in materials science, in watermarking, in data compression, in magnetic resonance imaging, in holography, in color imaging, in optics and neural networks, in graph matching, etc. (see [15, 16]).

Let H 1 , H 2 , H 3 be real Hilbert spaces, let A: H 1 H 3 , B: H 2 H 3 be two bounded linear operators, let U: H 1 H 1 and T: H 2 H 2 be two firmly quasi-nonexpansive operators. In [17], Moudafi introduced the following split common fixed-point problem:

find xF(U),yF(T)such thatAx=By,
(1.4)

which allows asymmetric and partial relations between the variables x and y. The interest is to cover many situations, for instance, in decomposition methods for PDEs, applications in game theory and in intensity-modulated radiation therapy (IMRT). In decision sciences, this allows to consider agents who interplay only via some components of their decision variables (see [18]). In IMRT, this amounts to envisaging a weak coupling between the vector of doses absorbed in all voxels and that of the radiation intensity (see [3]). If H 2 = H 3 and B=I, then SCFP (1.4) reduces to SCFP (1.3).

For solving the SCFP (1.4), Moudafi [17] introduced the following alternating algorithm:

{ x k + 1 = U ( x k γ k A ( A x k B y k ) ) , y k + 1 = T ( y k + γ k B ( A x k + 1 B y k ) )
(1.5)

for firmly quasi-nonexpansive operators U and T, where non-decreasing sequence γ k (ε,min( 1 λ A , 1 λ B )ε), λ A , λ B stand for the spectral radiuses of A A and B B, respectively.

Very recently, Moudafi [19] introduced the following simultaneous iterative method to solve SCFP (1.4):

{ x k + 1 = U ( x k γ k A ( A x k B y k ) ) , y k + 1 = T ( y k + γ k B ( A x k B y k ) )
(1.6)

for firmly quasi-nonexpansive operators U and T, where γ k (ε, 2 λ A + λ B ε), λ A , λ B stand for the spectral radiuses of A A and B B, respectively.

Note that in the algorithms (1.5) and (1.6) mentioned above, the determination of the stepsize { γ k } depends on the operator (matrix) norms A and B (or the largest eigenvalues of A A and B B). In order to implement the alternating algorithm (1.5) and the simultaneous algorithm (1.6) for solving SCFP (1.4), one has first to compute (or, at least, estimate) operator norms of A and B, which is in general not an easy work in practice. To overcome this difficulty, López et al. [20] and Zhao and Yang [21] presented a helpful method for estimating the stepsizes which do not need prior knowledge of the operator norms for solving the split feasibility problems and multiple-set split feasibility problems, respectively. Inspired by them, in this paper, we introduce a new choice of the stepsize sequence { γ k } for the simultaneous iterative algorithm to solve SCFP (1.4) governed by generalized asymptotically quasi-nonexpansive operators as follows:

γ k ( 0 , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) .
(1.7)

The advantage of our choice (1.7) of the stepsizes lies in the fact that no prior information as regards the operator norms of A and B is required, and still convergence is guaranteed.

Now let us recall some definitions, notations and conclusions which will be needed in proving our main results.

Definition 1.1 A mapping T:HH is called demiclosed at the origin if, for any sequence { x n } which weakly converges to x, if the sequence {T x n } strongly converges to 0, then Tx=0.

Definition 1.2 (1) A mapping T:HH is said to be nonexpansive if

TxTyxy,(x,y)H×H.

(2) A mapping T:HH is said to be firmly nonexpansive if

T x T y 2 x y 2 ( x y ) ( T x T y ) 2 ,(x,y)H×H.

(3) A mapping T:HH is said to be quasi-nonexpansive if F(T) such that

Txqxq,(x,q)H×F(T).

(4) A mapping T:HH is said to be firmly quasi-nonexpansive if F(T) such that

T x q 2 x q 2 x T x 2 ,(x,q)H×F(T).

(5) A mapping T:HH is said to be asymptotically nonexpansive if there exists a nonnegative real sequence { l k } with l k 0 such that for each k1,

T k x T k y 2 x y 2 + l k x y 2 ,(x,y)H×H.

(6) A mapping T:HH is said to be asymptotically quasi-nonexpansive if F(T) and there exists a nonnegative real sequence { l k } with l k 0 such that for each k1,

T k x q 2 x q 2 + l k x q 2 ,(x,q)H×F(T).

(7) A mapping T:HH is said to be generalized asymptotically quasi-nonexpansive mapping with ({ l k },{ μ k }) if F(T), and there exist nonnegative real sequences { l k }, { μ k } with l k 0 and μ k 0 such that for each k1,

T k x q 2 x q 2 + l k x q 2 + μ k ,(x,q)H×F(T).

It is clear from this definition that every firmly nonexpansive mapping is nonexpansive, nonexpansive mapping with a fixed point is quasi-nonexpansive, each firmly quasi-nonexpansive mapping is quasi-nonexpansive, and each quasi-nonexpansive mapping is asymptotically quasi-nonexpansive. We remark that an asymptotically nonexpansive mapping with a nonempty fixed point set F(T) is an asymptotically quasi-nonexpansive mapping, but the converse may be not true. The class of generalized asymptotically quasi-nonexpansive mappings is more general than the class of asymptotically quasi-nonexpansive mappings and asymptotically nonexpansive mappings. The following example shows that the inclusion is proper. Let K=[ 1 π , 1 π ] and define (see [22]) Tx= x 2 sin( 1 x ) if x0 and Tx=0 if x=0. Then T k x0 uniformly but T is not Lipschitzian. Notice that F(T)={0}. For each fixed k, define f k (x)= T k xx for xK. Set μ k = sup x K { f k (x),0}. Then lim k μ k =0 and

T k x x+ μ k .

This shows that T is a generalized asymptotically quasi-nonexpansive but it is not asymptotically quasi-nonexpansive and asymptotically nonexpansive because it is not Lipschitzian.

Definition 1.3 A mapping T:HH is said to be uniformly L-Lipschitzian if there exists a constant L>0 such that for each k1,

T k x T k y Lxy,(x,y)H×H.

In real Hilbert space, we easily get the following equality:

2x,y= x 2 + y 2 x y 2 = x + y 2 x 2 y 2 ,x,yH.
(1.8)

In what follows, we give some preliminary results needed for the convergence analysis of our algorithms.

Lemma 1.4 ([23])

Let H be a real Hilbert space. Then for all t[0,1] and x,yH,

t x + ( 1 t ) y 2 =t x 2 +(1t) y 2 t(1t) x y 2 .

Lemma 1.5 ([24])

Let { a k }, { b k } and { δ k } be sequences of nonnegative real numbers satisfying

a k + 1 (1+ δ k ) a k + b k ,k1.

If k = 1 δ k < and k = 1 b k <, then the limit lim k a k exists.

2 Simultaneous iterative algorithm without prior knowledge of operator norms

In this section we introduce a simultaneous iterative algorithm where the stepsizes don‘t depend on the operator norms A and B and prove weak convergence of the algorithm to solve SCFP (1.4) governed by generalized asymptotically quasi-nonexpansive operators.

We always assume that H 1 , H 2 , H 3 are real Hilbert spaces and A: H 1 H 3 , B: H 2 H 3 are two bounded linear operators. Let U: H 1 H 1 and T: H 2 H 2 be two generalized asymptotically quasi-nonexpansive mappings with ({ l k },{ μ k }). In the sequel, we use Γ to denote the set of solutions of SCFP (1.4), i.e.,

Γ= { x F ( U ) , y F ( T )  such that  A x = B y } .

Algorithm 2.1 Let x 0 H 1 , y 0 H 2 be arbitrary and α k [0,1]. Assume that the kth iterate x k H 1 , y k H 2 has been constructed; then we calculate the (k+1) th iterate ( x k + 1 , y k + 1 ) via the formula:

{ u k = x k γ k A ( A x k B y k ) , x k + 1 = α k u k + ( 1 α k ) U k ( u k ) , v k = y k + γ k B ( A x k B y k ) , y k + 1 = α k v k + ( 1 α k ) T k ( v k ) .
(2.1)

The stepsize γ k is chosen in such a way that

γ k ( 0 , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) ,kΩ,
(2.2)

otherwise, γ k =γ (γ being any nonnegative value), where the set of indices Ω={k:A x k B y k 0}.

Remark 2.2 Note that in (2.2) the choice of the stepsize γ k is independent of the norms A and B. The value of γ does not influence the considered algorithm, but it was introduced just for the sake of clarity. Furthermore, we will see from Lemma 2.3 that γ k is well defined.

Lemma 2.3 If Γ is nonempty, then γ k defined by (2.2) is well defined.

Proof Taking (x,y)Γ, i.e., xF(U), yF(T) and Ax=By. We have

A ( A x k B y k ) , x k x =A x k B y k ,A x k Ax

and

B ( A x k B y k ) , y y k =A x k B y k ,ByB y k .

By adding the two above equalities and by taking into account the fact that Ax=By, we obtain

A x k B y k 2 = A ( A x k B y k ) , x k x + B ( A x k B y k ) , y y k A ( A x k B y k ) x k x + B ( A x k B y k ) y y k .

Consequently, for kΩ, that is, A x k B y k >0, we have A (A x k B y k )0 or B (A x k B y k )0. This leads to the conclusion that γ k is well defined. □

Theorem 2.4 Assume that UI, TI are demiclosed at origin, and U, T are uniformly L-Lipschitzian. Let the sequence {( x k , y k )} be generated by Algorithm 2.1. Assume Γ is nonempty and for small enough ϵ>0,

γ k ( ϵ , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ϵ ) ,

where kΩ. Then {( x k , y k )} weakly converges to a solution ( x , y ) of (1.4) provided that k = 1 ( l k + μ k )< and { α k }(δ,1δ) for small enough δ>0. Moreover, { x k } and { y k } are asymptotically regular and A x k B y k 0.

Proof From the condition on γ k , we have

inf k Ω { 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 γ k } >0.
(2.3)

On the other hand, from A ( A x k B y k ) 2 A 2 A x k B y k 2 and B ( A x k B y k ) 2 B 2 A x k B y k 2 we obtain { 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 } is lower bounded by 2 A 2 + B 2 and so

inf k Ω { 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 } 2 A 2 + B 2 >.

It follows from (2.3) that sup k Ω γ k <+ and { γ k } k 1 is bounded.

Taking (x,y)Γ, i.e., xF(U); yF(T) and Ax=By. We have

u k x 2 = x k γ k A ( A x k B y k ) x 2 = x k x 2 2 γ k x k x , A ( A x k B y k ) + γ k 2 A ( A x k B y k ) 2 .
(2.4)

Using equality (1.8), we have

2 x k x , A ( A x k B y k ) = 2 A x k A x , A x k B y k = A x k A x 2 A x k B y k 2 + B y k A x 2 .
(2.5)

By (2.4) and (2.5), we obtain

u k x 2 = x k x 2 γ k A x k A x 2 γ k A x k B y k 2 + γ k B y k A x 2 + γ k 2 A ( A x k B y k ) 2 .
(2.6)

Similarly, by (2.1) we have

v k y 2 = y k y 2 γ k B y k B y 2 γ k A x k B y k 2 + γ k A x k B y 2 + γ k 2 B ( A x k B y k ) 2 .
(2.7)

By adding the two last inequalities, and by taking into account fact that Ax=By, we obtain

u k x 2 + v k y 2 = x k x 2 + y k y 2 γ k [ 2 A x k B y k 2 γ k ( A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) ] .
(2.8)

By the fact that U and T are generalized asymptotically quasi-nonexpansive mappings with ({ l k },{ μ k }), it follows from Lemma 1.4 that

x k + 1 x 2 = α k u k x 2 + ( 1 α k ) U k ( u k ) x 2 α k ( 1 α k ) U k ( u k ) u k 2 α k u k x 2 + ( 1 α k ) u k x 2 + ( 1 α k ) l k u k x 2 + ( 1 α k ) μ k α k ( 1 α k ) U k ( u k ) u k 2 = u k x 2 + ( 1 α k ) l k u k x 2 + ( 1 α k ) μ k α k ( 1 α k ) U k ( u k ) u k 2

and

y k + 1 y 2 v k y 2 +(1 α k ) l k v k y 2 +(1 α k ) μ k α k (1 α k ) T k ( v k ) v k 2 .

So, by (2.8), we have

x k + 1 x 2 + y k + 1 y 2 [ 1 + ( 1 α k ) l k ] ( x k x 2 + y k y 2 ) + 2 ( 1 α k ) μ k γ k [ 1 + ( 1 α k ) l k ] [ 2 A x k B y k 2 γ k ( A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) ] α k ( 1 α k ) U k ( u k ) u k 2 α k ( 1 α k ) T k ( v k ) v k 2 .
(2.9)

Now, by setting ρ k (x,y):= x k x 2 + y k y 2 , we obtain the following inequality:

ρ k + 1 ( x , y ) ( 1 + ξ k ) ρ k ( x , y ) + η k γ k ( 1 + ξ k ) [ 2 A x k B y k 2 γ k ( A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) ] α k ( 1 α k ) U k ( u k ) u k 2 α k ( 1 α k ) T k ( v k ) v k 2 ,
(2.10)

where ξ k =(1 α k ) l k and η k =2(1 α k ) μ k . By the condition k = 1 ( l k + μ k )<, we have k = 1 ξ k < and k = 1 η k <. It follows from the condition on { γ k } that

ρ k + 1 (x,y)(1+ ξ k ) ρ k (x,y)+ η k .

By Lemma 1.5, the following limit exists:

lim k ρ k (x,y):=ρ(x,y).

So the sequences { x k } and { y k } are bounded. Now, we rewrite (2.10) as follows:

γ k ( 1 + ξ k ) [ 2 A x k B y k 2 γ k ( A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) ] + α k ( 1 α k ) U k ( u k ) u k 2 + α k ( 1 α k ) T k ( v k ) v k 2 ρ k ( x , y ) ρ k + 1 ( x , y ) + ξ k ρ k ( x , y ) + η k .
(2.11)

It follows from the assumption

γ k ( ϵ , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ϵ ) ,kΩ

that

lim k ( A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ) =0.
(2.12)

(Note that A x k B y k =0 if kΩ.) So, we obtain

lim k A ( A x k B y k ) = lim k B ( A x k B y k ) =0.

Similarly, by the conditions on { α k }, we obtain

lim k U k ( u k ) u k = lim k T k ( v k ) v k =0.
(2.13)

Using the assumption on γ k , (2.11), (2.12), and the convergence of ρ k (x,y) we have

lim k A x k B y k =0.
(2.14)

Since

u k x k = γ k A ( A x k B y k )

and { γ k } is bounded, we have lim k u k x k =0. It follows from lim k U k ( u k ) u k =0 that lim k U k ( u k ) x k =0. So,

x k + 1 x k α k u k x k +(1 α k ) U k ( u k ) x k 0
(2.15)

as k, from which one infers that { x k } is asymptotically regular, namely lim k x k + 1 x k =0. Noting

u k + 1 u k = x k + 1 x k γ k + 1 A ( A x k + 1 B y k + 1 ) + γ k A ( A x k B y k ) x k + 1 x k + γ k + 1 A ( A x k + 1 B y k + 1 ) + γ k A ( A x k B y k ) ,

from (2.14) and (2.15) we have

lim k u k + 1 u k =0.
(2.16)

Similarly, lim k v k y k =0, { y k } and { v k } are asymptotically regular, too.

Next, we prove that u k U( u k )0 and v k T( v k )0 as k. In fact, since U is uniformly L-Lipschitzian continuous, it follows from (2.13) and (2.16) that

u k U ( u k ) u k U k ( u k ) + U k ( u k ) U ( u k ) u k U k ( u k ) + L U k 1 ( u k ) u k u k U k ( u k ) + L ( U k 1 ( u k ) U k 1 ( u k 1 ) + U k 1 ( u k 1 ) u k ) u k U k ( u k ) + L ( L u k u k 1 + U k 1 ( u k 1 ) u k 1 + u k 1 u k ) 0 .

Since T is uniformly L-Lipschitzian continuous, in the same way as above, we can also prove that v k T( v k )0 as k.

Taking ( x , y ) ω w ( x k , y k ), from lim k u k x k =0 and lim k v k y k =0, we have x ω w ( u k ) and y ω w ( v k ). Combined with the demiclosednesses of UI and TI at 0,

lim k U ( u k ) u k = lim k T ( v k ) v k =0

yields U x = x and T y = y . So, x F(U) and y F(T). On the other hand, A x B y ω w (A x k B y k ) and weakly lower semicontinuity of the norm imply that

A x B y lim inf k A x k B y k =0,

hence ( x , y )Γ.

Finally, we will show the uniqueness of the weak cluster points of { x k } and { y k }. Indeed, let x ¯ , y ¯ be other weak cluster points of { x k } and { y k }, respectively, then ( x ¯ , y ¯ )Γ. From the definition of ρ k (x,y), we have

ρ k ( x , y ) = x k x ¯ 2 + x ¯ x 2 + 2 x k x ¯ , x ¯ x + y k y ¯ 2 + y ¯ y 2 + 2 y k y ¯ , y ¯ y = ρ k ( x ¯ , y ¯ ) + x ¯ x 2 + y ¯ y 2 + 2 x k x ¯ , x ¯ x + 2 y k y ¯ , y ¯ y .
(2.17)

Without loss of generality, we may assume that x k x ¯ and y k y ¯ . By passing to the limit in relation (2.17), we obtain

ρ ( x , y ) =ρ( x ¯ , y ¯ )+ x ¯ x 2 + y ¯ y 2 .

Reversing the role of ( x , y ) and ( x ¯ , y ¯ ), we also have

ρ( x ¯ , y ¯ )=ρ ( x , y ) + x x ¯ 2 + y y ¯ 2 .

By adding the two last equalities, we obtain x = x ¯ and y = y ¯ , which implies that the whole sequence {( x k , y k )} weakly converges to a solution of problem (1.4). This completes the proof. □

The following conclusions can be obtained from Theorem 2.4 immediately.

Theorem 2.5 Let U: H 1 H 1 and T: H 2 H 2 be two asymptotically quasi-nonexpansive mappings with ({ l k }). Assume that UI, TI are demiclosed at origin, and U, T are uniformly L-Lipschitzian. Let the sequence {( x k , y k )} be generated by Algorithm  2.1. Assume Γ is nonempty and for small enough ϵ>0,

γ k ( ϵ , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ϵ ) ,

where kΩ. Then {( x k , y k )} weakly converges to a solution ( x , y ) of (1.4) provided that k = 1 l k < and { α k }(δ,1δ) for small enough δ>0. Moreover, { x k } and { y k } are asymptotically regular and A x k B y k 0.

Theorem 2.6 Let U: H 1 H 1 and T: H 2 H 2 be two quasi-nonexpansive mappings. Assume that UI, TI are demiclosed at origin, and U, T are uniformly L-Lipschitzian. Let the sequence {( x k , y k )} be generated by Algorithm 2.1. Assume Γ is nonempty and for small enough ϵ>0,

γ k ( ϵ , 2 A x k B y k 2 A ( A x k B y k ) 2 + B ( A x k B y k ) 2 ϵ ) ,

where kΩ. Then {( x k , y k )} weakly converges to a solution ( x , y ) of (1.4) provided that { α k }(δ,1δ) for small enough δ>0. Moreover, { x k } and { y k } are asymptotically regular and A x k B y k 0.

Remark 2.7 When B=I, Algorithm 2.1 becomes

{ u k = x k γ k A ( A x k y k ) , x k + 1 = α k u k + ( 1 α k ) U k ( u k ) , v k = ( 1 γ k ) y k + γ k A x k , y k + 1 = β k v k + ( 1 β k ) T k ( v k ) ,
(2.18)

where the stepsize γ k is chosen in such a way that

γ k ( 0 , 2 A x k B y k 2 A ( A x k B y k ) 2 + A x k B y k 2 ) ,kΩ,

otherwise γ k =γ (γ being any nonnegative value), where the set of indices Ω={k:A x k y k 0}. This solves SCFP (1.3) for generalized asymptotically quasi-nonexpansive operators, asymptotically quasi-nonexpansive operators, quasi-nonexpansive operators, and firmly quasi-nonexpansive operators without prior knowledge of operator norm A.

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Acknowledgements

The research was supported by the Fundamental Research Funds for the Central Universities (Program No. 3122013k004), it was also supported by the Fundamental Research Funds for the Central Universities (Program No. 3122013C002).

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Correspondence to Jing Zhao.

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Zhao, J., He, S. Simultaneous iterative algorithms for the split common fixed-point problem of generalized asymptotically quasi-nonexpansive mappings without prior knowledge of operator norms. Fixed Point Theory Appl 2014, 73 (2014). https://doi.org/10.1186/1687-1812-2014-73

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Keywords

  • split common fixed-point problem
  • generalized asymptotically quasi-nonexpansive mappings
  • weak convergence
  • simultaneous iterative algorithm
  • Hilbert space