Skip to main content

Strong convergence theorems for generalized nonexpansive mappings on star-shaped set with applications

Abstract

In this paper, we study attractive points for a class of generalized nonexpansive mappings on star-shaped sets and establish strong convergence theorems of the Halpern iterative sequences generated by these mappings in a real Hilbert space. We modify Halpern’s iterations for finding an attractive point of a mapping T satisfying condition (E) on a star-shaped set C in a real Hilbert space H and provide an affirmative answer to an open problem posed by Akashi and Takahashi in a recent work of (Appl. Math. Comput. 219(4):2035-2040, 2012) for nonexpansive and nonspreading mappings. Furthermore, we study the approximation of common attractive points of generalized nonexpansive mappings and derive a strong convergence theorem by a new iteration scheme for these mappings. As applications of our results, we study multiple sets split monotone inclusion problems for inverse strongly monotone mappings, multiple sets split optimization problems, and multiple sets split feasibility problems. Our results contain many original results on multiple sets split feasibility problem in the literature. Our results also improve and generalize many well-known results in the current literature.

MSC:47H10, 37C25.

1 Introduction

Throughout this paper, we denote the set of real numbers and the set of positive integers by and , respectively. Let H be a Hilbert space with the norm and C a nonempty subset of H. Let T:CH be a mapping. We denote by F(T) the set of fixed points of T and by A(T) the set of attractive points (see [1]) of T, i.e.,

F(T)={xC:Tx=x}andA(T)= { x H : T y x y x , y C } .

A mapping T:CH is said to be nonexpansive if TxTyxy for all x,yC. A mapping T:CH is said to be quasi-nonexpansive if F(T) and Txyxy for all xC and yF(T). A mapping T:CH is said to be strongly monotone if there exists γ ¯ >0 such that xy,TxTy γ ¯ x y 2 for all x,yH.

Recall that the one-step Halpern iteration (see [2]) is given by the following formula:

x n + 1 = α n u+(1 α n )T x n ,uC, x 1 C.
(1.1)

Here, { α n } n N is a real sequence in [0,1] satisfying some appropriate conditions. A more general iteration scheme of one-step Halpern iteration is two-step Halpern iteration given by

{ u C , x 1 C chosen arbitrarily , y n = ( 1 β n ) x n + β n T x n , x n + 1 = α n u + ( 1 α n ) y n ,
(1.2)

where the sequences { β n } n N and { α n } n N satisfy some appropriate conditions. In particular, when all β n =1, the Halpern iteration (1.2) becomes the standard Halpern iteration (1.2).

Definition 1.1 Let C be a nonempty subset of a Hilbert space H. Then C is called star-shaped if there exists zC such that for any xC and any λ(0,1),

λz+(1λ)xC.

Such zC is called a center of the star-shaped set C.

Recently, Takahashi and Takeuchi [1] introduced the concept of attractive points. Akashi and Takahashi [3] proved the following strongly convergence attractive point theorem for nonexpansive mappings on a star-shaped set C of a Hilbert space.

Theorem 1.1 Let H be a Hilbert space and C be a star-shaped subset of H with center zC. Let T:CC be a nonexpansive mapping with A(T). Suppose that { x n } n N is a sequence generated by x 1 =xC and

x n + 1 = α n z+(1 α n )T x n ,nN,

where 0 α n 1, lim n α n =0, n = 1 α n = and n = 1 | α n α n + 1 |<. Then { x n } n N converges strongly to P A ( T ) z, where P A ( T ) is metric projection of H onto A(T).

Akashi and Takahashi [3] posed the following open problem in their final remark.

Question 1.1 Is there any strong convergence theorem of Halpern’s type for a wide class of nonlinear mappings which contains nonexpansive mappings and nonspreading mappings in a real Hilbert space H?

Definition 1.2 ([4])

Let C be a nonempty subset of a Banach space X. For μ1, we say that a mapping T:CX satisfies condition ( E μ ) on C if there exists μ1 such that for all x,yC,

xTyμxTx+xy.

We say that T satisfies condition (E) on C whenever T satisfies ( E μ ) on C for some μ1.

The split feasibility problem (SFP) is to find a point

x C such that A x Q,

where C is a nonempty closed convex subset of a Hilbert space H 1 , Q is a nonempty closed convex subset of a Hilbert space H 2 , and A: H 1 H 2 is an operator. The split feasibility problem in finite dimensional Hilbert spaces was first introduced by Censor et al. [5] for modeling inverse problems which arise from phase retrievals and in medical image reconstruction. The split feasibility problem has applications in signal processing, image reconstruction, approximation theory, control theory, biomedical engineering, communications, and geophysics. One may refer to [69].

Let C 1 , C 2 ,, C m be nonempty closed convex subsets of a Hilbert space H 1 , let Q 1 , Q 2 ,, Q n be nonempty, closed convex subsets of Hilbert space H 2 and let A 1 , A 2 ,, A m : H 1 H 2 be linear operators. The well known multiple sets split feasibility problem (MSSFP) is to find x H 1 such that x C i and A i x Q i for all i=1,2,,m.

Multiple sets split feasibility problem (MSSFP) contains convex feasibility problem (CFP) and split feasibility problems (SFP) as special cases [5, 10, 11].

In this paper, we study attractive points for a class of generalized nonexpansive mappings on star-shaped sets and establish strong convergence theorems of the Halpern iterative sequences generated by these mappings in a real Hilbert space. We modify the Halpern iterations for finding an attractive point of a mapping T satisfying condition (E) on a star-shaped set C in a real Hilbert space H and provide an affirmative answer to open Question 1.1. Furthermore, we study the approximation of common attractive points of generalized nonexpansive mappings and derive a strong convergence theorem by a new iterative scheme for these mappings. As applications of our results, we study multiple sets split monotone inclusion problems for inverse strongly monotone mappings, multiple sets split optimization problem, multiple sets split feasibility problem. To the best of our knowledge, there is no result on multiple sets split monotone inclusion for inverse strongly monotone mappings and multiple sets split optimization problem in the literature. Our results also improve and generalize many well-known results in the current literature; see, for example, [3].

2 Preliminaries

Following Kohsaka and Takahashi [12], a mapping T:CH is said to be nonspreading if

2 T x T y 2 T x y 2 + x T y 2
(2.1)

for all x,yC.

Let C be a nonempty, closed convex subset of a Hilbert space H and xH. Then there exists a unique nearest point zC such that xz= inf y C xy. We denote such a correspondence by z= P C x. The mapping P C is called a metric projection of H onto C.

Definition 2.1 ([13])

Let C be a nonempty subset of a Banach space X. We say that a mapping T:CX satisfies condition (C) on C if for all x,yC,

1 2 xTxxy

implies

TxTyxy.

Remark 2.1 ([13] and [4])

Let T:CX.

  1. (i)

    It is obvious that if T is nonexpansive, then T satisfies ( E μ ) on C for some μ1, but the converse is not true.

  2. (ii)

    If T satisfies condition (C), then T satisfies ( E μ ) on C for some μ1, but the converse is not true.

  3. (iii)

    If T satisfies condition (E), it is easy to see that

    A(T)C=F(T).

In this section, we collect some lemmas which will be used in the proofs for the main results in next sections. We start with the following well-known lemma.

Lemma 2.1 ([14])

Let H be a real Hilbert space and C a nonempty convex subset of H. For given xH:

  1. (i)

    z= P C x if and only if

    xz,yz0,yC.
  2. (ii)

    z= P C x if and only if

    x z 2 x y 2 y z 2 ,yC.
  3. (iii)

    P C x P C y,xy P C x P C y 2 , x,yH. Consequently, P C is a nonexpansive mapping.

Lemma 2.2 ([15])

In a Hilbert space H, we have

  1. (i)

    for all x,yH and λ[0,1]

    λ x + ( 1 λ ) y 2 =λ x 2 +(1λ) y 2 λ(1λ) x y 2 ;
  2. (ii)

    for all x,y,zH

    x y 2 x ( y + z ) 2 +2xy,z;
  3. (iii)

    for all x,yH

    x + y 2 x 2 +2y,x+y;
  4. (iv)

    for all x,y,wH,

    ( x y ) + ( x w ) , y w = x w 2 x y 2 .

Takahashi and Takeuchi [1] proved the following useful lemmas related to attractive points of a nonempty set C in a Hilbert space H.

Lemma 2.3 Let C be a nonempty subset of a real Hilbert space H and T:CH be a mapping. Then A(T) is a closed convex subset of H.

Lemma 2.4 Let C be a nonempty subset of a real Hilbert space H and T:CH be a mapping. If { u n } n N be a sequence in H such that

lim sup n ( u n y ) + ( u n T y ) , y T y 0

for all yC. If a subsequence { u n i } i N of { u n } n N , converges weakly to uH, then uA(T).

Lemma 2.5 Let C be a nonempty subset of a real Hilbert space H and T:CC be a nonexpansive mapping. Then the following assertions are equivalent.

  1. (1)

    The attractive point set A(T).

  2. (2)

    There exists xC such that the sequence { T n x } n N is bounded.

Proposition 2.1 Let C be a nonempty subset of a Banach space X and T:CC be a mapping which satisfies condition ( E μ ) for some μ1. Then the following statements hold.

  1. (i)

    For all xC,

    x T 2 x(μ+1)xTx.
  2. (ii)

    For all x,yC,

    TxTyμ(μ+2)xTx+Txy.
  3. (iii)

    For all x,yC,

    T x T y 2 T x y 2 [ μ 2 ( μ + 2 ) 2 x T x 2 + 2 μ ( μ + 2 ) T x y ] xTx.

Proof

  1. (i)

    Since T satisfies condition ( E μ ),

    x T 2 x μxTx+xTx(μ+1)xTx.
  2. (ii)

    By (i),

    T x T y μ T x T 2 x + T x y μ ( T x x + x T 2 x ) + T x y μ ( μ + 2 ) T x x + T x y .
  3. (iii)

    This follows immediately from (ii). This completes the proof.

 □

Example 2.1 Let T:[0,2][0,2] be defined by

Tx= { 0 if  x [ 0 , 2 ) , 1 if  x = 2 .

Then T is a nonspreading mapping with F(T)={0}. Indeed, for any x[0,2) and y=2, we have Tx=0 and Ty=1. Observe now that

2 | T x T y | 2 = 2 | 0 1 | 2 | x 1 | 2 + | 2 0 | 2 = | x T y | 2 + | y T x | 2 .

The other cases can be verified similarly. It is worth mentioning that T is neither nonexpansive nor continuous.

Proposition 2.2 Let C be a nonempty subset of a Banach space X and T:CC be a mapping. If T is a nonspreading mapping, then it satisfies condition ( E 2 ) on C.

Proof Since T is a nonspreading mapping, then we have

2 T x T y 2 T x y 2 + x T y 2 ,x,yC.

This implies that, for any x,yC,

(1)TxTyTxyor(2)TxTyxTy.

If (1) holds, then we have

x T y x T x + T x T y x T x + T x y x T x + T x x + x y = 2 x T x + x y .

If (2) holds, then we obtain

y T x y T y + T y T x y T y + x T y y T y + x y + y T y = 2 y T y + x y .

This completes the proof. □

Let us give an example of a generalized nonexpansive mapping which is not a nonspreading mapping.

Example 2.2 Let T:[2,1][2,1] be defined by

Tx= { | x | 2 if  x [ 2 , 1 ) , 1 2 if  x = 1 .

It could easily be verified that T satisfies condition (E) on [2,1]; for more details, see [4]. However, the mapping T is not a nonspreading mapping. Indeed, for x=2 and y=1, we have Tx=1 and Ty= 1 2 . Thus we obtain

2 | T x T y | 2 =2 | 1 + 1 2 | 2 = 9 2
(2.2)

and

| T x y | 2 + | x T y | 2 = | 2 + 1 2 | 2 = 9 4 .

If T is a nonspreading mapping, then, in view of (2.2), we have

| T x T y | 2 = 9 2 9 4 .

This is a contradiction. Therefore, T is not a nonspreading mapping.

Lemma 2.6 (see [[16], Lemma 2.1])

Let { s n } n N be a sequence of nonnegative real numbers satisfying the inequality:

s n + 1 (1 γ n ) s n + γ n δ n ,n0,

where { γ n } n N and { δ n } n N satisfy the conditions:

  1. (i)

    { γ n } n N [0,1] and n = 0 γ n =, or equivalently, n = 0 (1 γ n )=0;

  2. (ii)

    lim sup n δ n 0, or

(ii)′ n = 0 γ n δ n <.

Then lim n s n =0.

To prove our main result, we need the following lemma.

Lemma 2.7 ([17])

Let { s n } n N be a sequence of nonnegative real numbers, let { α n } n N be a sequence of [0,1] with n = 1 α n =, let { β n } n N be a sequence of nonnegative real numbers with n = 1 β n < and let { γ n } n N be a sequence of real numbers with lim sup n γ n 0. Suppose that

s n + 1 (1 α n ) s n + α n γ n + β n ,n0.

Then lim n s n =0.

The following lemma has been proved in [18].

Lemma 2.8 Let { a n } n N be a sequence of real numbers such that there exists a subsequence { n i } i N of { n } n N such that a n i < a n i + 1 for all iN. Then there exists a subsequence { m k } k N N such that m k and the following properties are satisfied by all (sufficiently large) numbers kN:

a m k a m k + 1 and a k a m k + 1 .

In fact, m k =max{jk: a j < a j + 1 }.

Let X be a real Banach space. The modulus δ of convexity of X is denoted by

δ(ϵ)=inf { 1 x + y 2 : x 1 , y 1 , x y ϵ }

for every ϵ with 0ϵ2. A Banach space X is said to be uniformly convex if δ(ϵ)>0 for every ϵ>0. It is well that any Hilbert space is a uniformly convex Banach space; see, for more details [14].

We know the following result from [19].

Lemma 2.9 Let X be a uniformly convex Banach space and B r :={xX:xr}, r>0. Then there exists a continuous strictly increasing convex function g:[0,)[0,) with g(0)=0 such that

λ x + β y + γ z 2 λ x 2 +β y 2 +γ z 2 λβg ( x y )

for all x,y,z B r and all λ,β,γ[0,1] with λ+β+γ=1.

The following result has been proved in [20].

Lemma 2.10 Let X be a uniformly convex Banach space, r>0 be a constant. Then there exists a continuous, strictly increasing and convex function g:[0,)[0,) such that

k = 0 α k x k 2 k = 0 α k x k 2 α i α j g ( x i x j )

for all i,jN{0}, x k B r :={zX:zr}, α k (0,1) and kN{0} with k = 0 α k =1.

3 Strong convergence theorems

The following result presents an existence theorem of attractive points of a generalized nonexpansive mapping T on a nonempty subset C of a Hilbert space H.

Theorem 3.1 Let C be a nonempty subset of a real Hilbert space H. Let T:CC be a mapping satisfying condition (E) on C which is uniformly asymptotically regular, i.e., lim n T n x T n + 1 x=0 for all xC. Then the following assertions are equivalent.

  1. (1)

    The attractive point set A(T).

  2. (2)

    There exists xC such that the sequence { T n x } n N is bounded.

Proof The implication (1) (2) is obvious. For the converse implication, suppose that there exists xC such that the sequence { T n x } n N is bounded. Setting u n = T n x for all nN, the uniformly asymptotically regularity of T assures that

lim n T u n u n = lim n T n + 1 x T n x =0.
(3.1)

Since { u n } n N is bounded and C is a nonempty subset of the Hilbert space H, there exists a subsequence { u n k } k N of { u n } n N such that u n k yC as k. Next, we denote u n k by x k for all kN. This, together with (3.1), implies that

lim k T x k x k = lim k T u n k u n k =0and x k yas k.

Thus we have

lim sup k x k Tyμ lim sup k T x k x k + lim sup k x k y= lim sup k x k y.

The Opial property implies that yF(T)A(T), which completes the proof. □

The following strong convergence result provides an affirmative answer to open Question 1.1 in the case where the mapping T is a generalized nonexpansive mapping.

Theorem 3.2 Let H be a Hilbert space and C be a star-shaped subset of H with center zC. Let T:CC be a mapping satisfying condition (E) on C such that A(T). Suppose that { x n } n N is a sequence generated by x 1 =xC and

x n + 1 = α n z+(1 α n )T x n ,nN,
(3.2)

where 0 α n 1, lim n α n =0, n = 1 α n = and n = 1 | α n α n + 1 |<. Then { x n } n N converges strongly to P A ( T ) z, where P A ( T ) is metric projection of H onto A(T).

Proof Let x 1 C and u= P A ( T ) z. Following the same argument as in Theorem 3.1 [3], we can show that the sequences { x n } n N and { T x n } n N are bounded.

Let K:=sup{|z,T x n u|:nN}, with the same argument as in Theorem 3.1 [3], we see that

x n + 1 x n 2K| α n α n + 1 |+(1 α n ) x n x n 1 .

Since 0 α n 1, lim n α n =0, n = 1 α n = and n = 1 | α n α n + 1 |<, we have from Lemma 2.7 that

lim n x n + 1 x n =0.

This last result together with (3.2) amounts to

lim n x n T x n =0.

In view of Lemma 2.2(iv), we get, for any yC,

lim sup n ( x n y ) + ( x n T y ) , y T y = lim sup n ( T x n y ) + ( T x n T y ) , y T y = lim sup n ( T x n T y 2 T x n y 2 ) lim sup n [ μ 2 ( μ + 2 ) 2 x n T x n 2 + 2 μ ( μ + 2 ) T x n y ] x n T x n lim sup n [ μ 2 ( μ + 2 ) 2 + 2 μ ( μ + 2 ) ] M 1 x n T x n ,

where M 1 =sup{T x n y,T x n x n :nN}. Thus we obtain

lim sup n ( x n y ) + ( x n T y ) , y T y 0,yC.

Since { x n } n N is bounded, there exists a subsequence { x n i } i N of { x n } n N such that x n i y, and

lim sup n x n u,zu= lim i x n i u,zu=yu,zu.

By Lemma 2.4, yA(T). This, together with Lemma 2.1(ii), implies that

lim sup n x n + 1 u , z u lim sup n x n + 1 x n , z u + lim sup n x n u , z u = lim sup n x n u , z u = lim i x n i u , z u = y u , z u = y P A ( T ) z , z P A ( T ) z 0 .

From Lemma 2.2(iii) and (3.2), we have

x n + 1 P A ( T ) z 2 = α n z + ( 1 α n ) x n P A ( T ) z 2 ( 1 α n ) ( x n P A ( T ) z ) 2 + 2 α n x n + 1 P A ( T ) z , z P A ( T ) z ( 1 α n ) x n P A ( T ) z 2 + 2 α n x n + 1 P A ( T ) z , z P A ( T ) z .

Then Theorem 3.2 follows from Lemma 2.7. □

Applying Theorems 3.1 and 3.2 and following the same arguments as Theorem 3.2 [3], we have the following fixed point theorem, which generalizes Theorem 3.2 [3].

Theorem 3.3 Let H be a Hilbert space and C be a closed star-shaped subset of H. Let T:CC be a mapping satisfying condition (E) on C such that T is uniformly asymptotically regular and { T n x } n N is bounded for some xC. Then F(T).

For the special case of Theorem 3.2, we have the following fixed point theorem.

Corollary 3.1 Let H be a Hilbert space and C be a closed star-shaped subset of H. Let T:CC be a mapping satisfying condition (C) on C such that T is uniformly asymptotically regular and { T n x } n N is bounded for some xC. Then F(T).

Applying Theorem 3.2 and following the same arguments as Theorem 3.4 [3], we have the following fixed point convergence theorem, which generalizes Theorem 3.4 [3].

Theorem 3.4 Let H be a Hilbert space and C be a closed star-shaped subset of H with center zC. Let T:CC be a mapping satisfying condition (E) on C such that F(T). Suppose that { x n } n N is a sequence generated by x 1 =xC and

x n + 1 = α n z+(1 α n )T x n ,nN,

where 0 α n 1, lim n α n =0, n = 1 α n = and n = 1 | α n α n + 1 |<. Then { x n } n N converges strongly to some u 0 F(T), where

u 0 =arg min u F ( T ) uz.

Corollary 3.2 Let H be a Hilbert space and C be a closed star-shaped subset of H with center zC. Let T:CC be a mapping satisfying condition (C) on C such that F(T). Suppose that { x n } n N is a sequence generated by x 1 =xC and

x n + 1 = α n z+(1 α n )T x n ,nN,

where 0 α n 1, lim n α n =0, n = 1 α n = and n = 1 | α n α n + 1 |<. Then { x n } n N converges strongly to some u 0 F(T), where

u 0 =arg min u F ( T ) uz.

Remark 3.1 The two-step Halpern iteration process is a generalization of the one-step Halpern iteration process. It provides more flexibility in defining the algorithm parameters which is important from the numerical implementation perspective.

In the following, we prove strong convergence theorems of common attractive points for generalized nonexpansive mappings in a Hilbert space.

Theorem 3.5 Let H be a Hilbert space and C be a convex subset of H and zC. Let T 1 :CC be a mapping satisfying condition ( E λ ) on C and T 2 :CC be a mapping satisfying condition ( E μ ) on C such that A:=A( T 1 )A( T 2 ). Let { α n } n N , { β n , 1 } n N , { β n , 2 } n N , and { β n , 3 } n N be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    β n , 1 + β n , 2 + β n , 3 =1, nN;

  4. (d)

    lim inf n β n , j β n , 3 >0, j=1,2.

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n , x n + 1 = α n z + ( 1 α n ) y n .
(3.3)

Then the sequence { x n } n N defined in (3.3) converges strongly to P A z, where P A is the metric projection from H onto A.

Proof We divide the proof into several steps. Set

u= P A z.

Step 1. We prove that the sequences { x n } n N , { y n } n N , { T 1 x n } n N , and { T 2 x n } n N are bounded.

We first show that { x n } n N is bounded.

Let pA be fixed. In view of Lemma 2.9, there exists a continuous strictly increasing convex function g:[0,)[0,) with g(0)=0 such that

y n p 2 = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n p 2 β n , 1 T 1 x n p 2 + β n , 2 T 2 x n p 2 + β n , 3 x n p 2 β n , j β n , 3 g ( x n T j x n ) β n , 1 x n p 2 + β n , 2 x n p 2 + β n , 3 x n p 2 β n , j β n , 3 g ( x n T j x n ) = x n p 2 β n , j β n , 3 g ( x n T j x n ) x n p 2 , j = 1 , 2 .
(3.4)

This together with (3.3) entails

x n + 1 p = α n u + ( 1 α n ) y n p α n u p + ( 1 α n ) y n p α n u p + ( 1 α n ) x n p max { u p , x n p } .

Consequently, by induction, we deduce that

x n + 1 pmax { u p , x 1 p }

for all nN. This implies that the sequence { x n p } n N is bounded and hence the sequence { x n } n N is bounded. Then, by (3.4), { y n } n N , { T 1 x n } n N , and { T 2 x n } n N are bounded.

Step 2. We prove that for any nN

x n + 1 u 2 (1 α n ) x n u 2 +2 α n x n + 1 u,zu.
(3.5)

Let us show (3.5). For each nN and j=1,2, in view of (3.4), we obtain

y n u 2 x n u 2 β n , j β n , 3 g ( x n T j x n ) .

This implies that

x n + 1 u 2 = α n z + ( 1 α n ) y n u 2 α n z u 2 + ( 1 α n ) y n u 2 α n z u 2 + ( 1 α n ) [ x n u 2 β n , j β n , 3 g ( x n T j x n ) ] .
(3.6)

Let M 2 :=sup{| z u 2 x n u 2 |:nN,j=1,2}. It follows from (3.6) that

β n , j β n , 3 g ( x n T j x n ) x n u 2 x n + 1 u 2 + α n M 2 ,j=1,2.
(3.7)

In view of Lemma 2.2(ii) and (3.4), we obtain

x n + 1 u 2 = α n z + ( 1 α n ) y n u 2 α n z + ( 1 α n ) y n u α n ( z u ) 2 + 2 x n + 1 u , α n ( z u ) = ( 1 α n ) ( y n u ) 2 + 2 α n x n + 1 u , z u ( 1 α n ) y n u 2 + 2 α n x n + 1 u , z u ( 1 α n ) x n u 2 + 2 α n x n + 1 u , z u .

Step 3. We prove that x n u as n.

We discuss the following two possible cases.

Case 1. Suppose that there exists n 0 N such that { x n u } n = n 0 is nonincreasing. Then the sequence { x n u } n N is convergent. Thus we have x n u 2 x n + 1 u 2 0 as n. This, together with conditions (c), (d), and (3.7), imply that

lim n x n T 1 x n =0,and lim n x n T 2 x n =0.
(3.8)

On the other hand, we have

y n x n = β n , 1 ( x n T 1 x n )+ β n , 2 ( x n T 2 x n ),and x n + 1 y n = α n (z y n ).

This implies that

lim n y n x n =0,and lim n x n + 1 y n =0.
(3.9)

By the triangle inequality, we conclude that

x n + 1 x n x n + 1 y n + y n x n .

It follows from (3.9) that

lim n x n + 1 x n =0.

Using Proposition 2.1, Lemma 2.2(iv), and (3.8), we obtain for any yC

lim sup n ( x n y ) + ( x n T 2 y ) , y T 2 y = lim sup n ( T 2 x n y ) + ( T 2 x n T 2 y ) , y T 2 y = lim sup n ( T 2 x n T 2 y 2 T 2 x n y 2 ) lim sup n [ μ 2 ( μ + 2 ) 2 x n T 2 x n 2 + 2 μ ( μ + 2 ) T 2 x n y ] x n T 2 x n lim sup n [ μ 2 ( μ + 2 ) 2 + 2 μ ( μ + 2 ) ] M 3 x n T 2 x n ,

where M 3 =sup{ T 2 x n y, T 2 x n x n :nN}. Thus we obtain

lim sup n ( x n y ) + ( x n T 2 y ) , y T 2 y 0,yC.
(3.10)

Similarly, we have

lim sup n ( x n y ) + ( x n T 1 y ) , y T 1 y = lim sup n ( T 1 x n y ) + ( T 1 x n T 1 y ) , y T 1 y = lim sup n ( T 1 x n T 1 y 2 T 1 x n y 2 ) lim sup n [ λ 2 ( λ + 2 ) 2 + 2 λ ( λ + 2 ) ] M 4 x n T 1 x n ,

where M 4 =sup{ T 1 x n y, T 1 x n x n :nN}.

Thus we obtain

lim sup n ( x n y ) + ( x n T 1 y ) , y T 1 y 0,yC.
(3.11)

Since { x n } n N is bounded, there exists a subsequence { x n i } i N of { x n } n N such that x n i y, and

lim sup n x n u,zu= lim i x n i u,zu=yu,zu.

By Lemma 2.4, (3.10), and (3.11), yA( T 1 )A( T 2 ). By Lemma 2.1(ii), we show that

lim sup n x n + 1 u , z u lim sup n x n + 1 x n , z u + lim sup n x n u , z u = lim sup n x n u , z u = lim i x n i u , z u = y u , z u = y P A z , z P A z 0 .
(3.12)

Thus we have the desired result by (3.5) and Lemma 2.6.

Case 2. Suppose that there exists a subsequence { n i } i N of { n } n N such that

x n i u< x n i + 1 u

for all iN. Then, by Lemma 2.8, there exists a nondecreasing sequence { m k } k N N such that m k ,

u x m k u x m k + 1 andu x k u x m k + 1

for all kN. This, together with (3.7), imply that

β m k (1 β m k ) x m k T j x m k 2 x m k u 2 x m k + 1 u 2 + α m k M 2 α m k M 2

for all kN and j=1,2. By conditions (a), (c), and (d), we have

lim k x m k T j x m k =0,j=1,2.
(3.13)

By the same argument, as in Case 1, we arrive at

lim sup k x m k + 1 u,zu0.

It follows from (3.7) that

x m k + 1 u 2 (1 α m k ) x m k u 2 +2 α m k x m k + 1 u,zu.
(3.14)

Since x m k u x m k + 1 u, we have

α m k x m k u 2 x m k u 2 x m k + 1 u 2 + 2 α m k x m k + 1 u , z u 2 α m k x m k + 1 u , z u .
(3.15)

In particular, since α m k >0, we obtain

x m k u 2 2 x m k + 1 u,zu.

In view of (3.15), we deduce that

lim k x m k u=0.

This, together with (3.14), implies that

lim k x m k + 1 u=0.

On the other hand, we have x k u x m k + 1 u for all kN which implies that x k u as k. Thus, we have x n u as n. We thus complete the proof. □

Corollary 3.3 Let H be a Hilbert space and C be a convex subset of H and zC. Let T:CC a mapping satisfying condition (E) on C such that A(T). Let { α n } n N and { β n } n N be two sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    lim inf n β n (1 β n )>0.

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = ( 1 β n ) x n + β n T x n , x n + 1 = α n z + ( 1 α n ) y n .
(3.16)

Then the sequence { x n } n N defined in (3.16) converges strongly to P A ( T ) z, where P A ( T ) is the metric projection from H onto A(T).

Applying Theorem 3.5, we study the approximation of common fixed points of generalized nonexpansive mappings and derive a strong convergence theorem by a new iteration scheme for these mappings.

Theorem 3.6 Let H be a Hilbert space and C be a closed convex subset of H and zC. Let T 1 :CC be a mapping satisfying condition ( E λ ) on C and T 2 :CC a mapping satisfying condition ( E μ ) on C such that F:=F( T 1 )F( T 2 ). Let { α n } n N , { β n , 1 } n N , { β n , 2 } n N , { β n , 3 } n N be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    β n , 1 + β n , 2 + β n , 3 =1, nN;

  4. (d)

    lim inf n β n , j β n , 3 >0, j=1,2.

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n , x n + 1 = α n z + ( 1 α n ) y n .
(3.17)

Then the sequence { x n } n N defined in (3.17) converges strongly to some u 0 F, where

u 0 =arg min u F uz.

Proof Since T 1 and T 2 are mappings satisfying condition (E), for any xC and uF, we have

T 1 xuux

and

T 1 xuux.

This implies that FA( T 1 )A( T 2 ). Thus we obtain A:=A( T 1 )A( T 2 ). It follows from Theorem 3.6, that { x n } n N converges strongly to u 0 A. Since C is closed, we have u 0 C. We follow the same argument as in the proof of Theorem 3.3 [3], we can prove Theorem 3.7. □

Using Lemma 2.10 and Theorem 3.5, we can prove the following result.

Theorem 3.7 Let H be a Hilbert space and C be a convex subset of H and zC. For any jN, let T j :CC be a mapping satisfying condition ( E λ j ) on C such that A:= j = 1 A( T j ). Let { α n } n N , { β n , j } n N , j N { 0 } be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    j = 1 β n , j + β n , 0 =1, nN;

  4. (d)

    lim inf n β n , j β n , 0 >0, jN.

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = j = 1 β n , j T j x n + β n , 0 x n , x n + 1 = α n z + ( 1 α n ) y n .
(3.18)

Then the sequence { x n } n N defined in (3.18) converges strongly to P A z, where P A is the metric projection from H onto A.

Remark 3.2 Theorem 3.7 improves Theorem 1.1 and many fixed point results in the literature.

4 Applications to multiple sets split feasibility problems

Let H 1 , and H 2 be Hilbert spaces, Q 1 , and Q 2 be nonempty, closed convex subsets of H 1 , and H 2 , respectively. Let G: H 1 H 1 be a multivalued mapping. The effective domain of G is denoted by D(G), that is, D(G)={x H 1 :Gx}. Then G: H 1 H 1 is called

  1. (i)

    a monotone operator on Q 1 if xy,uv0 for all x,yD(G), uGx, and vGy;

  2. (ii)

    a maximal monotone operator on H 1 if G is a monotone operator on H 1 and its graph is not properly contained in the graph of any other monotone operator on H 1 .

A mapping V: Q 1 H 1 is called α-inverse strongly monotone on Q 1 (in short α-ism), if

xy,VxVyα V x V y 2 for all x,y Q 1  and α>0.

Let I and I 2 denote the identity functions on H 1 , and H 2 , respectively. For each iN, let

  1. (i)

    κ, κ i , and μ i >0, B i be a μ i -inverse strongly monotone mapping of Q 1 into H 1 , L i be a κ i -inverse strongly monotone mapping of Q 2 into H 2 , L be a κ-inverse strongly monotone mapping of Q 2 into H 2 ;

  2. (ii)

    M and M i be maximal monotone mappings on H 2 such that the domains of M and M i are included in Q 2 , G i be a maximal monotone mapping on H 1 such that the domain of G i includes Q 1 ;

  3. (iii)

    M i 1 0={x H i :0 M i x}, J λ n M i = ( I + λ n M i ) 1 , λ n >0;

  4. (iv)

    A: H 1 H 2 and A i : H 1 H 2 be bounded linear operators, A and A i be the adjoints of A and A i , respectively;

  5. (v)

    R and R i be the spectral radii of A A and A i A i , respectively.

Throughout this section, we use these notations and assumptions unless specified otherwise.

A mapping T: H 1 H 1 is said to be averaged if T=(1α)I+αS, where α(0,1) and S: H 1 H 1 is nonexpansive. In this case, we also say that T is α-averaged. A firmly nonexpansive mapping is 1 2 -averaged.

Lemma 4.1 ([21, 22])

Let C be a nonempty closed convex subset of a real Hilbert space H, and let T:CC be a mapping. Then the following are satisfied:

  1. (i)

    T is nonexpansive if and only if the complement (IT) is a 1/2-ism.

  2. (ii)

    If S is υ-ism, then for γ>0, γS is a υ/γ-ism.

  3. (iii)

    S is averaged if and only if the complement IS is a υ-ism for some υ>1/2.

  4. (iv)

    If S and T are both averaged, then the composite ST of S and T is averaged.

  5. (v)

    If the mappings { T i } i = 1 n are averaged and have a common fixed point, then i = 1 n Fix( T i )=Fix( T 1 T n ).

In order to study the convergence theorems for the solutions set of multiple sets split problems, we must give an essential result in this paper. We study the following essential problem (SFP-1):

Find  x ¯ H 1  such that A x ¯ ( L + M ) 1 (0).

Recently, Yu, Lin and Chuang [23] proved the following useful result.

Lemma 4.2 ([23])

Given any x ¯ H 1 , we have the following.

  1. (i)

    If x ¯ is a solution of (SFP-1), then (Iλ A ( I 2 U)A) x ¯ = x ¯ , where λ>0, U= J σ M ( I 2 σL), and σ>0.

  2. (ii)

    Suppose that U= J σ M ( I 2 σL), 0<λ< 1 R , 0<σ<2κ, then J σ M ( I 2 σL), and Iλ A ( I 2 U)A are averaged. We assume further that the solution set of (SFP-1) is nonempty, and (Iλ A ( I 2 U)A) x ¯ = x ¯ . Then x ¯ is a solution of (SFP-1).

In the following theorem, we study the following multiple sets variational inclusion problems (MSSVIP-1):

Find  x ¯ H 1  such that  x ¯ ( G 1 + B 1 ) 1 0 ( G 2 + B 2 ) 1 0 ( G m + B m ) 1 0

and

A 1 x ¯ ( M 1 + L 1 ) 1 0, A 2 x ¯ ( M 2 + L 2 ) 1 0,, A l x ¯ ( M l + L l ) 1 0,

where mN and lN.

Let Ω 1 denote the solution set of the problem (MSSVIP-1).

Theorem 4.1 Let z H 1 . Let T 1 : Q 1 Q 1 be a mapping defined by

T 1 = J λ 1 G 1 (I λ 1 B 1 ) J λ 2 G 2 (I λ 2 B 2 ) J λ m G m (I λ m B m ),

and T 2 : Q 1 Q 1 be a mapping defined by

T 2 = ( I σ 1 A 1 ( I 2 U 1 ) A 1 ) ( I σ 2 A 2 ( I 2 U 2 ) A 2 ) ( I σ l A l ( I 2 U l ) A l ) ,

where U i = J δ i M i ( I 2 δ i L i ), mN, iN and lN.

Suppose that Ω 1 .

Let { α n } n N , { β n , 1 } n N , { β n , 2 } n N , { β n , 3 } n N be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    β n , 1 + β n , 2 + β n , 3 =1, nN;

  4. (d)

    lim inf n β n , j β n , 3 >0, j=1,2;

  5. (e)

    for each iN, 0< δ i <2 κ i , 0< λ i <2 μ i , and 0< σ i < 1 R i .

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n , x n + 1 = α n z + ( 1 α n ) y n .
(4.1)

Then the sequence { x n } n N defined in (4.1) converges strongly to some u 0 Ω 1 , where u 0 =arg min u Ω 1 uz.

Proof By Lemma 4.2, for each iN, J λ i G i (I λ i B i ) and (I σ i A i ( I 2 U i )) are averaged. Since Ω 1 , there exists w Ω 1 , such that

w ( G 1 + B 1 ) 1 0 ( G 2 + B 2 ) 1 0 ( G m + B m ) 1 0

and

A 1 w ( M 1 + L 1 ) 1 0, A 2 w ( M 2 + L 2 ) 1 0,, A l w ( M l + L l ) 1 0,

mN and lN.

By Lemma 4.2,

wF ( I σ 1 A 1 ( I 2 U 1 ) A 1 ) F ( I σ 2 A 2 ( I 2 U 2 ) A 2 ) F ( I σ l A l ( I 2 U l ) A l ) .

We also see that

wF ( J λ 1 G 1 ( I λ 1 B 1 ) ) F ( J λ 2 G 2 ( I λ 2 B 2 ) ) F ( J λ m G m ( I λ m B m ) ) .

By Lemma 4.1, we see that

wF ( J λ 1 G 1 ( I λ 1 B 1 ) ) ( J λ 2 G 2 ( I λ 2 B 2 ) ) ( J λ m G m ( I λ m B m ) )

and

wF ( I σ 1 A 1 ( I 2 U 1 ) A 1 ) ( I σ 2 A 2 ( I 2 U 2 ) A 2 ) ( I σ l A l ( I 2 U l ) A l ) .

Therefore wF:=F( T 1 )F( T 2 ) and F.

By Lemma 4.1 again, we see that T 1 and T 2 are averaged. Therefore T 1 and T 2 are nonexpansive mapping. Then by Theorem 3.7, the sequence { x n } n N defined in (4.1) converges strongly to some u 0 F, where u 0 =arg min u F uz. Since Ω 1 , it follows from Lemma 4.1 that Ω 1 =F. This completes the proof. □

Remark 4.1 Moudafi [24] studied a weak convergence of split monotone variational inclusion problem, while Theorem 4.1 studied a strong convergence theorem for the multiple sets split monotone variational inclusion problem.

In the following theorem, we study the following multiple sets split inclusion problems for inverse strongly monotone mappings (MSSVIP-2):

Find  x ¯ H 1  such that  x ¯ B 1 1 0 B 2 1 0 B m 1 0

and

A 1 x ¯ L 1 1 0, A 2 x ¯ L 2 1 0,, A l x ¯ L l 1 0,

where mN and lN.

Let Ω 2 denote the solution set of the problem (MSSVIP-2).

Theorem 4.2 Let z H 1 . Let T 1 : Q 1 Q 1 be a mapping defined by

T 1 =(I λ 1 B 1 )(I λ 2 B 2 )(I λ m B m )

and T 2 : Q 1 Q 1 be a mapping defined by

T 2 = ( I σ 1 A 1 ( I 2 U 1 ) A 1 ) ( I σ 2 A 2 ( I 2 U 2 ) A 2 ) ( I σ l A l ( I 2 U l ) A l ) ,

where U i =( I 2 δ i L i ), mN, iN and lN.

Suppose that Ω 2 .

Let { α n } n N , { β n , 1 } n N , { β n , 2 } n N , { β n , 3 } n N be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    β n , 1 + β n , 2 + β n , 3 =1, nN;

  4. (d)

    lim inf n β n , j β n , 3 >0, j=1,2;

  5. (e)

    for each iN, 0< δ i <2 κ i , 0< λ i <2 μ i , and 0< σ i < 1 R i .

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n , x n + 1 = α n z + ( 1 α n ) y n .
(4.2)

Then the sequence { x n } n N defined in (4.1) converges strongly to some u 0 Ω 2 , where u 0 =arg min u Ω 2 uz.

Proof By Lemma 4.2, for each iN, (I λ i B i ) U i =( I 2 δ i L i ) are averaged and I 2 U i is k i inverse strongly monotone for some k i 1 2 . Following the same argument as in Theorem 3.1 [24], we can show that for each iN, A i ( I 2 U i ) A i , is k i R i -inverse strong monotone. Following the same argument as in Theorem 4.1, we show that the sequence { x n } n N defined in (4.1) converges strongly to some u 0 F, where u 0 =arg min u F uz. Since Ω 2 , it follows from Lemma 4.1, it is easy to show that Ω 2 =F. This completes the proof of Theorem 4.2. □

Remark 4.2 To the best of our knowledge, there are many results on inclusion problems for maximum monotone mappings, but there are no results on inclusion problem for inverse strongly monotone mappings or split inclusion problems for inverse strongly monotone mappings.

As an application of the split inclusion problem for inverse strongly monotone mappings, we study the following split optimization problem.

Let V 1 and V 2 be nonempty open convex sets in H 1 and H 2 , respectively, Q 1 V 1 , Q 2 V 2 . For each iN, let f i : V 1 R and g i : V 2 R be convex Gâteaux differential functions. In the following theorem, we study the following multiple sets split optimization problem (MSSVIP-3):

Find  x ¯ Q 1  such that  x ¯ arg min x Q 1 f 1 (x)arg min x Q 1 f 2 (x)arg min x Q l f l (x)

and

A 1 x ¯ arg min w Q 2 g 1 (w), A 2 x ¯ arg min w Q 2 g 2 (w),, A l x ¯ arg min w Q 2 g l (w),

where mN and lN.

Let Ω 3 denote the solution set of the problem (MSSVIP-3).

Theorem 4.3 In Theorem 4.2, we assume further that V 1 and V 2 are nonempty open convex sets in H 1 and H 2 , respectively, Q 1 V 1 , Q 2 V 2 . For each iN, let f i : V 1 R and g i : V 2 R be convex Gâteaux differential functions. For each iN, suppose that B i and L i are strongly monotone and Lipschitz continuous on Q 1 and Q 2 , respectively, and B i and L i be the Gâteaux derivatives of f i and g i , respectively. Then the sequence { x n } n N defined in (4.1) converges strongly to some x ¯ Ω 3 .

Proof Since for each iN, B i and L i are Lipschitz and strongly monotone, it is easy to see that B i and L i are inverse strongly monotone. By Theorem 4.2, the sequence { x n } n N defined in (4.1) converges strongly to some x ¯ Ω 2 . Therefore for each i=1,2,,m, j=1,2,,l, x ¯ B i 1 0, A j x ¯ L j 1 0. Since for each iN, f i : V 1 R and g i : V 2 R are convex Gâteaux differential functions with Gâteaux derivatives B i and L i , respectively, we obtain

0 = B i x ¯ , y x ¯ = lim t 0 f i ( x ¯ + t ( y x ¯ ) ) f i ( x ¯ ) t = lim t 0 f i ( ( 1 t ) x ¯ + t y ) f i ( x ¯ ) t lim t 0 ( 1 t ) f i ( x ¯ ) + t f i ( y ) f i ( x ¯ ) t = f i ( y ) f i ( x ¯ )

for all y V 1 .

Then, for each i=1,2,,m, f i (y) f i ( x ¯ ) for all y Q 1 and x ¯ arg min y Q 1 f i (y).

Similarly, for each j=1,2,,l, g j (w) g j ( A j x ¯ ) for all w Q 2 and A j x ¯ arg min w Q 2 g j (w).

This shows that x ¯ Ω 3 . □

Let f be a proper lower semicontinuous convex function of H 1 into (,). The subdifferential ∂f of f is defined as follows:

f(x)= { z H 1 : f ( x ) + z , y x f ( y ) , y H 1 }

for all x H 1 . From Rockafellar [25], we know that ∂f is a maximal monotone operator. Let C be a nonempty closed convex subset of a real Hilbert space H 1 , and i C be the indicator function of C, i.e.

i C x= { 0 if  x C , if  x C .

Then i C is a proper lower semicontinuous convex function on H, and the subdifferential i C of i C is a maximal monotone operator. We define the resolvent J λ i C x= ( I + λ i C ) 1 x for all xH. We have J λ i C = P C .

For each iN, and jN, let C 1 , C 2 ,, C m be nonempty closed convex subsets of H 1 and D 1 , D 2 ,, D l be nonempty closed convex subsets of H 2 .

In the following theorem, we study the following multiple sets split feasibility problems (MSSVIP-4):

Find  x ¯ H 1  such that  x ¯ C 1 C 2 C m

and A 1 x ¯ D 1 , A 2 x ¯ D 2 , …, A l x ¯ D l , where mN, lN.

Let Ω 4 denote the solution set of the problem (MSSVIP-4).

Theorem 4.4 For each iN, and jN, let C 1 , C 2 ,, C m be the nonempty closed convex subsets of H 1 and D 1 , D 2 ,, D l be nonempty closed convex subsets of H 2 . Let T 1 : Q 1 Q 1 be a mapping defined by

T 1 = P C 1 P C 2 ,, P C m

and T 2 : Q 1 Q 1 a mapping defined by

T 2 = ( I σ 1 A 1 ( I 2 P D 1 ) A 1 ) ( I σ 2 A 2 ( I 2 P D 2 ) A 2 ) ( I σ l A l ( I 2 P D l ) A l ) ,

where mN, and lN.

Suppose that Ω 4 .

Let { α n } n N , { β n , 1 } n N , { β n , 2 } n N , { β n , 3 } n N be sequences in [0,1] satisfying the following control conditions:

  1. (a)

    lim n α n =0;

  2. (b)

    n = 1 α n =;

  3. (c)

    β n , 1 + β n , 2 + β n , 3 =1, nN;

  4. (d)

    lim inf n β n , j β n , 3 >0, j=1,2;

  5. (e)

    for each iN, 0< σ i < 1 R i .

Let { x n } n N be a sequence generated by

{ x 1 C chosen arbitrarily , y n = β n , 1 T 1 x n + β n , 2 T 2 x n + β n , 3 x n , x n + 1 = α n z + ( 1 α n ) y n .
(4.3)

Then the sequence { x n } n N defined in (4.3) converges strongly to some u 0 Ω 4 , where

u 0 =arg min u Ω 4 uz.

Proof Let G i = i C i , B i =0, M j = i D j , L i =0 in Theorem 4.1. Then Theorem 4.4 follows from Theorem 4.1. □

Example 4.1 Let T:R[0,2] be defined by

Tx= { 0 if  x ( , 2 ) , 1 if  x [ 2 , ) .

Then, T is a nonspreading mapping. Indeed, for any x(,2) and y[2,), we have Tx=0 and Ty=1. Observe now that

2 | T x T y | 2 = 2 | 0 1 | 2 | x 1 | 2 + | y 0 | 2 = | x T y | 2 + | y T x | 2 .

Therefore, T satisfies condition E 2 with A(T)=(,0]. Let z=1, α n = 1 n , x 1 =2, then T x 1 =1, x 2 = 1 2 , …, x n + 1 = 1 n . We see that the sequence x n converges strongly to 0A(T).

References

  1. 1.

    Takahashi W, Takeuchi Y: Nonlinear ergodic theorem without convexity for generalized hybrid mappings in a Hilbert space. J. Nonlinear Convex Anal. 2011, 12: 399–406.

    MathSciNet  Google Scholar 

  2. 2.

    Halpern B: Fixed points of nonexpanding mappings. Bull. Am. Math. Soc. 1967, 73: 957–961. 10.1090/S0002-9904-1967-11864-0

    Article  Google Scholar 

  3. 3.

    Akashi S, Takahashi W: Strong convergence theorem for nonexpansive mappings on star-shaped sets in Hilbert spaces. Appl. Math. Comput. 2012, 219(4):2035–2040. 10.1016/j.amc.2012.08.046

    Article  MathSciNet  Google Scholar 

  4. 4.

    García-Falset J, Llorens-Fuster E, Suzuki T: Fixed point theory for a class of generalized nonexpansive mappings. J. Math. Anal. Appl. 2011, 375: 185–195. 10.1016/j.jmaa.2010.08.069

    Article  MathSciNet  Google Scholar 

  5. 5.

    Censor Y, Elfving T: A multiprojection algorithm using Bregman projection in a product space. Numer. Algorithms 1994, 8: 221–239. 10.1007/BF02142692

    Article  MathSciNet  Google Scholar 

  6. 6.

    Henry S: Image Recovery Theory and Applications. Academic Press, Orlando; 1987.

    Google Scholar 

  7. 7.

    Combettes PL: The convex feasible problem in image recovery. 95. In Advanced in Image and Electron Physics. Edited by: Hawkes P. Academic Press, New York; 1996:155–270.

    Google Scholar 

  8. 8.

    Byrne C: Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 2002, 18: 441–453. 10.1088/0266-5611/18/2/310

    Article  MathSciNet  Google Scholar 

  9. 9.

    Byrne C: A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 2004, 20: 103–120. 10.1088/0266-5611/20/1/006

    Article  MathSciNet  Google Scholar 

  10. 10.

    Lopez G, Martinez-Marquez V, Wang F, Xu HK: Solving the split feasibility problem without prior knowledge of matrix. Inverse Probl. 2012., 28: Article ID 085004

    Google Scholar 

  11. 11.

    Censor Y, Bortfeld T, Martin B, Trofimov A: A unified approach for inversion problems in intensity-modulated radiation therapy. Phys. Med. Biol. 2003, 51: 2353–2365.

    Article  Google Scholar 

  12. 12.

    Kohsaka F, Takahashi W: Fixed point theorems for a class of nonlinear mappings relate to maximal monotone operators in Banach spaces. Arch. Math. 2008, 91: 166–177. 10.1007/s00013-008-2545-8

    Article  MathSciNet  Google Scholar 

  13. 13.

    Suzuki T: Fixed point theorems and convergence theorems for some generalized mappings. J. Math. Anal. Appl. 2008, 340: 1088–1095. 10.1016/j.jmaa.2007.09.023

    Article  MathSciNet  Google Scholar 

  14. 14.

    Takahashi W: Nonlinear Functional Analysis. Fixed Point Theory and Its Applications. Yokahama Publishers, Yokahama; 2000.

    Google Scholar 

  15. 15.

    Geobel K, Kirk WA: Topics on metric fixed-point theory. 28. In Cambridge Studies in Advanced Mathematics. Cambridge University Press, Cambridge; 1990.

    Google Scholar 

  16. 16.

    Xu HK, Kim TH: Convergence of hybrid steepest-descent methods for variational inequalities. J. Optim. Theory Appl. 2003, 119(1):185–201.

    Article  MathSciNet  Google Scholar 

  17. 17.

    Aoyama K, Kimura Y, Takahashi W, Toyoda M: Approximation of common fixed points of a countable family of nonexpansive mappings in a Banach space. Nonlinear Anal. 2007, 67: 2350–2360. 10.1016/j.na.2006.08.032

    Article  MathSciNet  Google Scholar 

  18. 18.

    Maing PE: Strong convergence of projected subgradient methods for nonsmooth and nonstrictly convex minimization. Set-Valued Anal. 2008, 16: 899–912. 10.1007/s11228-008-0102-z

    Article  MathSciNet  Google Scholar 

  19. 19.

    Cho YJ, Zhou HY, Guo G: Weak and strong convergence theorems for three-step iterations with errors for asymptotically nonexpansive mappings. Comput. Math. Appl. 2004, 47: 707–717. 10.1016/S0898-1221(04)90058-2

    Article  MathSciNet  Google Scholar 

  20. 20.

    Chang SS, Kim JK, Wang XR: Modified block iterative algorithm for solving convex feasibility problems in Banach spaces. J. Inequal. Appl. 2010., 2010: Article ID 869684 10.1155/2010/86968

    Google Scholar 

  21. 21.

    Combettes PL: Solving monotone inclusions via compositions of nonexpansive averaged operators. Optimization 2004, 53(5–6):475–504. 10.1080/02331930412331327157

    Article  MathSciNet  Google Scholar 

  22. 22.

    Xu HK: Iterative methods for the split feasibility problem in infinite-dimensional Hilbert spaces. Inverse Probl. 2010., 26: Article ID 105018

    Google Scholar 

  23. 23.

    Yu ZT, Lin LJ, Chuang CS: Mathematical programming with multiple sets split monotone variational inclusion constraints. Fixed Point Theory Appl. 2014., 2014: Article ID 20

    Google Scholar 

  24. 24.

    Moudafi A: Split monotone variational inclusions. J. Optim. Theory Appl. 2011, 150: 275–283. 10.1007/s10957-011-9814-6

    Article  MathSciNet  Google Scholar 

  25. 25.

    Rockafellar RT: Monotone operators associated with saddle functions and minimax problems. 18. In Nonlinear Functional Analysis. Part I. Edited by: Browder FE. Am. Math. Soc., Providence; 1970:241–250.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lai-Jiu Lin.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors contributed equally to the writing of this paper. All authors read and approved the final manuscript.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Naraghirad, E., Lin, L. Strong convergence theorems for generalized nonexpansive mappings on star-shaped set with applications. Fixed Point Theory Appl 2014, 72 (2014). https://doi.org/10.1186/1687-1812-2014-72

Download citation

Keywords

  • nonspreading mapping
  • generalized nonexpansive mapping
  • fixed point
  • attractive point
  • multiple sets split monotone variational inclusion problem
  • multiple sets split optimization problem
  • multiple sets split feasibility problem