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Strong convergence of a new general iterative method for variational inequality problems in Hilbert spaces

  • Yan-Lai Song1, 2,
  • Hui-Ying Hu1,
  • Ya-Qin Wang3,
  • Lu-Chuan Zeng1, 4Email author and
  • Chang-Song Hu2
Fixed Point Theory and Applications20122012:46

https://doi.org/10.1186/1687-1812-2012-46

Received: 29 October 2011

Accepted: 23 March 2012

Published: 23 March 2012

Abstract

In this article, we introduce a new iterative scheme with Meir-Keeler contractions for strict pseudo-contractions in Hilbert spaces. We also discuss the strong convergence theorems of the new iterative scheme for variational inequality problems in Hilbert spaces. The methods in this article are interesting and are different from those given in many other articles. Our results improve and extend the corresponding results announced by many others.

MR(2000) Subject Classification: 47H09; 47H10.

Keywords

Hilbert spacek-strict pseudo-contractionsfixed pointMeir-Keeler contractions

1 Introduction

Let H be a real Hilbert space with inner product 〈·,·〉 and norm ·. Let C be a nonempty closed convex subset of H and let F : CC be a nonlinear operator. The variational inequality problem is such that
V I ( F , C ) : F x * , ν - x * 0 , ν C .
(1.1)

Variational inequalities were introduced and studied by Stampacchia [1] in 1964. It is now well known that variational inequalities cover as diverse disciplines as partial differential equations, optimal control, optimization, mathematical programming, mechanics and finance, see [125].

It is known that if F is a strongly monotone and Lipschitzian mapping on C, then the VI(F, C) has a unique solution. It is also known that the VI(F, C) is equivalent to the fixed point equation
x * = P C x * - μ F ( x * ) ,

where P C is the projection of H onto the closed convex set C and μ > 0 is an arbitrarily fixed constant. So, fixed point methods can be implemented to find a solution of the VI(F,C) provided F satisfies some conditions and μ > 0 is chosen appropriately. A great deal of effort has gone into finding an approximate solution of the VI(F,C) see [3, 5, 1519].

In 2001, Yamada [2] introduced the following hybrid iterative method for solving the variational inequality
x n + 1 = T x n - μ λ n F ( T x n ) , n 0 .
(1.2)
On the other hand, Yao et al. [6] modified Mann's iterative scheme by using the so-called viscosity approximation method which was introduced by Moudafi [7]. More precisely, Yao et al. [6] introduced and studied the following iterative algorithm:
x 0 = x C , y n = β n x n + 1 ( 1 - β n ) T x n , x n + 1 = α n f ( x n ) + ( 1 - α n ) y n , n 0 .
(1.3)

where T is a nonexpansive mapping of K into itself and f is a contraction on K. They obtained a strong convergence theorem under some mild restrictions on the parameters.

Zhou [8], Qin et al. [9] modified normal Mann's iterative process (1.3) for non-self-k-strictly pseudo-contractions to have strong convergence in Hilbert spaces. Qin et al. [9] introduced the following iterative algorithm scheme:
x 1 = x K , y n = P K [ β n x n + ( 1 - β n ) T x n ] , x n + 1 = α n f ( x n ) + ( 1 - α n A ) y n , n 1 .
(1.4)

where T is non-self-k-strictly pseudo-contraction, f is a contraction and A is a strong positive linear bounded operator. They prove, under certain appropriate assumptions on the sequences {α n } and {β n }, that {x n } defined by (1.4) converges strongly to a fixed point of the k-strictly pseudo-contraction, which solves some variational inequality.

The following famous theorem is referred to as the Banach contraction principle.

Theorem 1. (Banach [10]) Let (X, d) be a complete metric space and let f be a contraction on X, i.e., there exists r (0,1) such that d(f(x), f(y)) ≤ rd(x, y) for all x, y X. Then f has a unique fixed point.

Theorem 2. (Meir and Keeler [11]) Let (X,d) be a complete metric space and let ϕ be a Meir-Keeler contraction (MKC) on X, i.e., for every ε > 0, there exists δ > 0 such that d(x, y) < ε + δ implies d(ϕ(x),ϕ(y)) < ε for all x, y X. Then ϕ has a unique fixed point.

Remark 1. Theorem 2 is one of generalizations of Theorem 1, because contractions are MKCs.

Question 1. Can Theorem 1 of Yao [6], Theorem 3.2 of Zhou [8], Theorem 2.1 of Qin [9], and so on be extended from one or finite k i -strictly pseudo-contraction to infinite k i -strictly pseudo-contraction?

Question 2. We know that the MKC is more general than the contraction. What happens if the contraction is replaced by the MKC?

Question 3. We know that the η-strongly monotone and L-Lipschitzian operator is more general than the strong positive linear bounded operator. What happens if the strong positive linear bounded operator is replaced by the η-strongly monotone and L-Lipschitzian operator?

Question 4. Can the restrictions imposed on the parameters {α n }, {β n } and {λ n } in [9] be relaxed?

The purpose of this article is to give the affirmative answers to these questions mentioned above. Motivated by the above works, in this article we suggest and analyze a hybrid iterative algorithm as follows:
x 1 = x C , y n = P C [ β n x n + ( 1 - β n ) i = 1 μ i ( n ) T i x n ] , x n + 1 = α n ϕ ( x n ) + γ n x n + ( ( 1 - γ n ) I - α n F ) y n , n 1 .
(1.5)

where T i is a non-self-k i -strictly pseudo-contraction, ϕ is an MKC contraction and F : CC is a L-Lipschitzian and η-strongly monotone mapping in Hilbert space. Under certain appropriate assumptions on the sequences {α n }, {β n }, {γ n }, and { μ i n } , that {x n } defined by (1.5) converges strongly to a common fixed point of an infinite family of k i -strictly pseudo-contractions, which solves some variational inequality.

2 Preliminaries

In this section, we first recall some notations. Let C be a nonempty closed convex subset of a real Hilbert space H. Let F : CC be an operator. F is called L-Lipschitzian if there exists a positive constant L such that
F x - F y L x - y ,
for all x, y C, F is said to be η-strongly monotone if there exists a positive constant η such that
F x - F y , x - y η x - y 2 ,

for all x, y C. Without loss of generality, we can assume that η (0, 1] and L [1, ∞). Under these conditions, it is well known that the variational inequality problem VI(F, C) has a unique solution x* C.

A self-mapping f : CC is a contraction on C if there exists a constant α (0,1) such that f(x) - f(y)αx - y; x,y C. We use Π C to denote the collection of all contractions on C. That is, Π C = {f|f : CC a contraction}. We use F(T) to denote the fixed point set of the mapping T and P C to denote the metric projection of H onto its closed convex subset C.

A mapping T is said to be non-expansive, if
T x - T y x - y for all x , y C .
T is said to be a k-strict pseudo-contraction in the terminology of Browder and Petryshyn [12], if there exists a constant k [0,1) such that
T x - T y 2 x - y 2 + k ( I - T ) x - ( I - T ) y 2 , x , y C .
It is clear that it is equivalent to
T x - T y , x - y x - y 2 - 1 - k 2 ( I - T ) x - ( I - T ) y 2 , x , y C ,
or is equivalent to
( I - T ) x - ( I - T ) y , x - y 1 - k 2 ( I - T ) x - ( I - T ) y 2 , x , y C
An operator A be a strongly positive bounded linear operator on H, that is, there exists a constant γ ̄ > 0 such that
A x , x γ ̄ x 2 , x H .

Remark 2. From the definition of A, we note that a strongly positive bounded linear operator A is a A-Lipschitzian and γ ̄ -strongly monotone operator.

In order to prove our main results, we need the following lemmas.

Lemma 2.1 (Zhou [8]). Let H be a Hilbert space and C be a closed convex subset of H. If T is a k-strictly pseudo-contractive mapping on C, then the fixed point set F(T) is closed convex, so that the projection PF(T)is well defined.

Lemma 2.2 (Zhou [8]). Let H be a Hilbert space and C be a closed convex subset of H. Let T : CH be a k-strictly pseudo-contractive mapping with F ( T ) . Then F(P C T) = F(T).

Lemma 2.3 (Browder and Petryshyn [12]). Let H be a Hilbert space, C be a closed convex subset of H, and T : CH be a k-strictly pseudo-contractive mapping. Define a mapping J : CH by Jx = αx +(1-α)Tx for all x C. Then, as α [k, 1), J is a non-expansive mapping such that F(J) = F(T).

Lemma 2.4. (see [13]). Let {x n }, {z n } be bounded sequences in a Banach space E and {β n } be a sequence in [0,1] which satisfies the following condition: 0 < lim infn→∞β n ≤ lim supn→∞β n < 1. Suppose that xn+1= (1 - β n )x n + β n z n for all n ≥ 0 and limsupn→∞(zn+1- z n - xn+1-x n ) ≤ 0. then lim n→∞z n - x n = 0.

Lemma 2.5 (Xu [14]). Assume that {α n } is a sequence of non-negative real numbers such that αn + 1≤ (1 - γ n )α n + δ n , where γ n is a sequence in (0, 1) and δ n is a sequence in such that
  1. (i)

    n = 1 γ n = ,

     
  2. (ii)

    lim sup n δ n γ n 0 or n = 1 δ n <

     

Then lim n → ∞α n = 0.

Lemma 2.6 ([23] Demiclosedness Principle). Let H be a Hilbert space, K a closed convex subset of H, and T:KK a non-expansive mapping with F i x ( T ) . If {x n } is a sequence in K weakly converging to x and if {(I - T)x n } converges strongly to y, then (I - T)x = y.

Lemma 2.7 Let F be a L-Lipschitzian and η-strongly monotone operator on a nonempty closed convex subset C of a real Hilbert space H with 0 < ηL and 0 < t < 2η/L2. Then S = (I - tF): CC is a contraction with contraction coefficient τ t = 1 - t ( η - t L 2 2 ) .

Proof. From the definition of η-strongly monotone and L-Lipschitzian operator, we have
S x - S y 2 x - y - t ( F x - F y ) 2 x - y 2 + t 2 F x - F y 2 - 2 t F x - F y , x - y x - y 2 + t 2 L 2 x - y 2 - 2 t η x - y 2 = [ 1 - t ( 2 η - t L 2 ) ] x - y 2 1 - t η - t L 2 2 2 x - y 2 .
for all x, y C. From 0 < ηL and 0 < t < 2η/L2, we have 0 < 1 - t ( η - t L 2 2 ) < 1 and
S x - S y τ t x - y ,

where τ t = 1 - t ( η - t L 2 2 ) ( 0 , 1 ) . Hence, S is a contraction with contraction coefficient τ t .

Lemma 2.8 ([23] Lemma 2.3). Let ϕ be an MKC on a convex subset C of a Banach space E. Then for each ε > 0, there exists r (0,1) such that x - yε implies ϕx - ϕyr x - y for all x, y C.

Lemma 2.9. Let C be a closed convex subset of a Hilbert space H. Let S : CC be a non-expansive mapping and ϕ be an MKC on C. Suppose F: CC be a η-strongly monotone and L-Lipschitzian mapping with coefficient η and η > γ > 0. Then the sequence {x t } define by
x t = t γ ϕ ( x t ) + ( 1 - t F ) S x t
converges strongly as t → 0 to a fixed point x ̃ of S which solves the variational inequality:
( F - γ ϕ ) x ̃ , x ̃ - z 0 , z F ( S ) .
(2.1)
Proof. The definition of {x t } is well definition. Indeed, From the definition of MKC, we can see MKC is also a non-expansive mapping. Consider a mapping S t on C defined by
S t x = t γ ϕ ( x ) + ( I - t F ) S x , x C .
It is easy to see that S t is a contraction, when 0 < t < 2 ( η - γ ) L 2 Indeed, by Lemmas 2.7 and 2.8, we have
S t x - S t y t γ ϕ ( x ) - ϕ ( y ) + ( I - t F ) S x - ( I - t F ) S y t γ ϕ ( x ) - ϕ ( y ) + τ t S x - S y t γ x - y + τ t x - y θ t x - y .
where θ t = + τ t (0,1). Hence, S t has a unique fixed point, denoted by x t , which uniquely solves the fixed point equation
x t = t γ ϕ ( x t ) + ( I - t F ) S x t .
We next show the uniqueness of a solution of the variational inequality (2.1). Suppose both x ̃ F ( S ) and x ^ F ( S ) are solutions to (2.1). Not lost generality, we may assume there is a positive number ε such that x ^ - x ̃ ε . Then, by Lemma 2.8, there is a number r (0,1) such that ϕ x ^ - ϕ x ̃ r x ^ - x ̃ . From (2.1), we know
( F - γ ϕ ) x ̃ , x ̃ - x ^ 0 .
(2.2)
and
( F - γ ϕ ) x ^ , x ^ - x ̃ 0 .
(2.3)
Adding up (2.2) and (2.3) gets
( F - γ ϕ ) x ^ - ( F - γ ϕ ) x ̃ , x ^ - x ̃ 0 .
Noticing that
( F - γ ϕ ) x ^ - ( F - γ ϕ ) x ̃ , x ^ - x ̃ = F x ^ - F x ̃ , x ^ - x ̃ - γ ϕ x ^ - ϕ x ̃ , x ^ - x ̃ η x ^ - x ̃ 2 - γ ϕ x ^ - ϕ x ̃ x ^ - x ̃ η x ^ - x ̃ 2 - γ r x ^ - x ̃ 2 ( η - γ r ) x ^ - x ̃ 2 ( η - γ r ) ε 2 > 0 .

Therefore, x ^ = x ̃ and the uniqueness is proved. Below we use x ̃ to denote the unique solution of (2.1).

We observe that {x t } is bounded. Indeed, We may assume 0 < t < η - γ L 2 . For p F(S), fixed ε1, for each t

Case 1. x t - p < ε1; In this case, we can see easily that {x t } is bounded.

Case 2. x t - pε1. In this case, by Lemma 2.8, there is a number r 1 (0,1) such that
ϕ ( x t ) - ϕ ( p ) r 1 x t - p .
x t - p = t γ ϕ ( x t ) + ( I - t F ) S x t - p = t ( γ ϕ ( x t ) - F p ) + ( I - t F ) S x t - ( I - t F ) p t γ ϕ ( x t ) - F p + τ t x t - p t γ ϕ ( x t ) - γ ϕ ( p ) + t γ ϕ ( p ) - F p + τ t x t - p t γ r 1 x t - p + t γ ϕ ( p ) - F p + τ t x t - p

therefore, x t - p 2 γ ϕ ( p ) - F p η - γ . This implies the {x t } is bounded.

Next, we prove that x t x ̃ as t → 0.

Since {x t } is bounded and H is reflexive, there exists a subsequence { x t n } of {x t } such that x t n x * . By x t - Sx t = t(γϕ(x t ) - FSx t ), we have x t n - S x t n 0 , as t n → 0. It follows from Lemma 2.6 that x* F(S).

We claim
x t n - x * 0 .
By contradiction, there is a number ε0 and a subsequence { x t m } of { x t n } such that x t m - x * ε 0 . From Lemma 2.8, there is a number r ε 0 > 0 such that ϕ ( x t m ) - ϕ ( x * ) r ε 0 x t m - x * , we write
x t m - x * = t m ( γ ϕ ( x t m ) - F x * ) + ( I - t m F ) S x t m - ( I - t m F ) x * ,
to derive that
x t m - x * 2 = t m γ ϕ ( x t m ) - F x * , x t m - x * + ( I - t m F ) S x t m - ( I - t m F ) x * , x t m - x * t m γ ϕ ( x t m ) - F x * , x t m - x * + τ t m x t m - x * 2 .
(2.4)
It follows that
x t m - x * 2 η - t m L 2 2 - 1 γ ϕ ( x t m ) - F x * , x t m - x * = η - t m L 2 2 - 1 γ ϕ ( x t m ) - γ ϕ ( x * ) , x t m - x * + γ ϕ ( x * ) - F x * , x t m - x * η - t m L 2 2 - 1 γ r ε 0 x t m - x * 2 + γ ϕ ( x * ) - F x * , x t m - x * .
Therefore,
x t m - x * 2 1 η - γ ε 0 - t m L 2 2 γ ϕ ( x * ) - F x * , x t m - x * .
(2.5)

By (2.5), we get that x t m x * . It is a contradiction. Hence, we have x t n x * .

We next prove that x* solves the variational inequality (2.1). Since
x t = t γ ϕ ( x t ) + ( I - t F ) S x t
we derive that
( F - γ ϕ ) x t = - 1 t [ ( I - S ) x t - t ( F x t - F S x t ) ] .
Notice
( I - S ) x t - ( I - S ) z , x t - z x t - z 2 - S x t - S z x t - z x t - z 2 - x t - z 2 = 0 .
It follows that, for z F(S),
( F - γ ϕ ) x t , x t - z = - 1 t ( I - S ) x t - t ( F x t - F S x t ) , x t - z = - 1 t ( I - S ) x t - ( I - S ) z , x t - z + F x t - F ( S x t ) , x t - z L x t - S x t x t - z .
(2.6)
Noticing
x t - S x t = t [ γ ϕ ( x t ) - F S x t ] .
Hence, we have
x t - S x t 0 , a s t 0 .

Now replacing t in (2.6) with t n and letting n → ∞, noticing ( I - S ) x t n ( I - S ) x * = 0 for x* F(S), we obtain 〈(F - γϕ)x*, x* - z〉 ≤ 0. That is, x* F(S) is a solution of (2.1); Hence, x ̃ = x * by uniqueness. We have shown that each cluster point of x t (at t → 0) equals x ̃ . Therefore, x t x ̃ as t → 0.

Lemma 2.10. Let H be a Hilbert space and C be a nonempty convex subset of H. Assume that T i : CE is a countable family of k i -strict pseudo-contraction for some 0 ≤ k i < 1 and sup{k i : i } < 1 such that i = 1 F ( T i ) . Assume that {μ i } is a positive sequence such that i = 1 μ i = 1 . Then i = 1 μ i T i : C E is a k-strict pseudo-contraction with k = sup{k i : i } and F ( i = 1 μ i T i ) = i = 1 F ( T i ) .

Proof. Let
G n x = μ 1 T 1 x + μ 2 T 2 x + + μ n T n x
and i = 1 n μ i = 1 . Then, G n : CE is a k i -strict pseudo-contraction with k = max{k i : 1 ≤ in}. Indeed, we can firstly see the case of n = 2.
I - G 2 x - I - G 2 y , x - y = μ 1 I - T 1 x + μ 2 I - T 2 x - μ 1 I - T 1 y - μ 2 I - T 2 y , x - y = μ 1 I - T 1 x - I - T 1 y , x - y + μ 2 I - T 2 x - I - T 2 y , x - y μ 1 1 - k 1 2 I - T 1 x - I - T 1 y 2 + μ 2 1 - k 2 2 I - T 2 x - I - T 2 y 2 1 - k 2 μ 1 I - T 1 x - I - T 1 y 2 + μ 2 I - T 2 x - I - T 2 y 2 1 - k 2 I - G 2 x - I - G 2 y 2 ,

which shows that G2 : CE is a k-strict pseudo-contraction with k = max{k i : i = 1,2}. By the same way, our proof method easily carries over to the general finite case.

Next, we prove the infinite case. From the definition of k-strict pseudo-contraction, we know
I - T n x - I - T n y , x - y 1 - k 2 I - T n x - I - T n y 2 .
Hence, we can get
I - T n x - I - T n y 2 1 - k x - y .
(2.7)
Taking p F(T n ), from 2.7 we have
I - T n x = I - T n x - I - T n p 2 1 - k x - p .

Consequently, for x H, if i = 1 F ( T i ) , μ i > 0 and i = 1 μ i = 1 , then i = 1 μ i T i strongly converges.

Let
T x = i = 1 μ i T i x ,
we have
T x = i = 1 μ i T i x = lim n i = 1 n μ i T i x = lim n 1 i = 1 n μ i i = 1 n μ i T i x .
Hence,
I - T x - I - T y , x - y = lim n I - 1 i = 1 n μ i i = 1 n μ i T i x + I - 1 i = 1 n μ i i = 1 n μ i T i y , x - y = lim n 1 i = 1 n μ i i = 1 n μ i I - T i x - I - T i y , x - y lim n 1 i = 1 n μ i i = 1 n μ i 1 - k 2 I - T i x - I - T i y 2 1 - k 2 lim n I - 1 i = 1 n μ i i = 1 n μ i T i x - I - 1 i = 1 n μ i i = 1 n μ i T i y 2 = 1 - k 2 I - T x - I - T y 2 .

So, we get T is k-strict pseudo-contraction.

Finally, we show F i = 1 μ i T i = i = 1 F T i . Suppose that x = i = 1 μ i T i x , it is sufficient to show that x i = 1 F T i . Indeed, for p i = 1 F T i , we have
x - p 2 = x - p , x - p = i = 1 μ i T i x - p , x - p = i = 1 μ i T i x - p , x - p x - p 2 - 1 - k 2 i = 1 μ i x - T i x 2 ,

where k = sup{k i : i }. Hence, we get x = T i x, this means that x i = 1 F T i .

3 Main results

Lemma 3.1. Let C be a closed convex subset of a real Hilbert space E such that C + C C. Let ϕ be a MKC on C. Suppose F : CC be a η-strongly monotone and L-Lipschitzian operator and 0 < γ < η and T i : CE be k i -strictly pseudo-contractive non-self-mapping such that i = 1 F ( T i ) . Assume k = sup {k i : i } < 1. Let {x n } be a sequence of C generated by (1.5) with the sequences {α n }, {β n } and {γ n } in [0,1], assume for each n, μ i ( n ) is a infinity sequence of positive number such that i = 1 μ i ( n ) = 1 .

The following control conditions are satisfied

(i) i = 1 α n = , lim n α n = 0

(ii) kβ n < 1,

(iii) lim n β n + 1 - β n = 0 , lim n i = 1 μ i ( n + 1 ) - μ i ( n ) = 0

(iv) 0 < lim inf n γ n lim sup n γ n < 1 . .

Then limn → ∞xn + 1- x n = 0.

Proof. Write, for each n ≥ 0, B n = i = 1 μ i ( n ) T i . By Lemma 2.10, each B n is a k-strict pseudo-contraction on C and F B n = i = 1 F ( T i ) for all n and the algorithm (1.5) can be rewritten as
x 1 = x C , y n = P C β n x n + 1 - β n B n x n x n + 1 = α n γ ϕ ( x n ) + γ n x n + 1 - γ n I - α n F y n , n 1 . ,
(3.1)

The rest of the proof will now be split into two parts.

Step 1. First, we show that sequences {x n } and {y n } are bounded. Define a mapping
L n x : P C β n x + 1 - β n B n x .
Then from the control condition (ii), Lemma 2.3, we obtain L n : CC is non-expansive. Taking a point p i = 1 F T i , by Lemma 2.2, we can get L n p = p. Hence, we have
y n - p = L n x n - p x n - p .
Not lose generality, we can assume r n b < 1, and 0 < α n < ( η - γ ) ( 1 - b ) L 2 . From definition of MKC and Lemma 2.8, for any ε > 0 there is a number r ε (0, 1), if x t - p < ε then ϕ(x t ) - ϕ(p) < ε; If x t -pε then ϕ(x t ) - ϕ(p)r ε x t -p. It follows 3.1 and Lemma 2.7 that
x n + 1 - p = α n γ ϕ ( x n ) + γ n x n + 1 - γ n I - α n F y n - p = α n γ ϕ ( x n ) - F p + γ n ( x n - p ) + 1 - γ n I - α n F y n - 1 - γ n I - α n F p 1 - γ n - α n η - α n L 2 2 ( 1 - γ n ) x n - p + γ n x n - p + α n γ ϕ ( x n ) - F p 1 - α n η - α n L 2 2 1 - γ n x n - p + α n γ max r ε x n - p , ε + α n γ ϕ ( p ) - F p = max 1 - α n η - α n L 2 2 1 - γ n x n - p + α n γ r ε x n - p + α n γ ϕ ( p ) - F p , 1 - α n η - α n L 2 2 1 - γ n x n - p + α n γ ε + α n γ ϕ ( p ) - F p = max 1 - α n η - α n L 2 2 1 - γ n - γ r ε x n - p + α n γ ϕ ( p ) - F p , 1 - α n η - α n L 2 2 1 - γ n x n - p + α n γ ε + α n γ ϕ ( p ) - F p .
By induction, we have
x n + 1 - p max 1 - α n η - α n L 2 2 1 - γ n - γ r ε x n - p + α n η - α n L 2 2 1 - γ n - γ r ε γ ϕ ( p ) - F p η - α n L 2 2 1 - γ n - γ r ε , 1 - α n η - α n L 2 2 1 - γ n x n - p + α n η - α n L 2 2 1 - γ n γ ε + γ ϕ ( p ) - F p η - α n L 2 2 1 - γ n max 1 - α n η - α n L 2 2 1 - γ n - γ r ε x n - p + α n η - α n L 2 2 1 - γ n - γ r ε 2 γ ϕ ( p ) - F p η - γ , 1 - α n η - α n L 2 2 1 - γ n x n - p + α n η - α n L 2 2 1 - γ n 2 γ ε + γ ϕ ( p ) - F p η - γ .
Hence, we have
x n - p max x 0 - p , 2 γ ε + γ ϕ ( p ) - F p η + γ r ε , n 0 ,

which gives that the sequence {x n } is bounded, so are {y n } and {L n x n }.

Step 2. In this part, we shall claim that xn + 1- x n → 0, as n → ∞. From(3.1), we get
x n + 1 = α n γ ϕ ( x n ) + γ n x n + 1 - γ n I - α n F L n x n .
(3.2)
Define
x n + 1 = 1 - γ n l n + γ n x n , n 0 ,
(3.3)
where
l n = x n + 1 - γ n x n 1 - γ n .
It follows that
l n + 1 - l n = α n + 1 γ ϕ ( x n + 1 ) + γ n + 1 x n + 1 + 1 - γ n + 1 I - α n + 1 F L n + 1 x n + 1 - γ n + 1 x n + 1 1 - γ n + 1 - α n γ ϕ ( x n ) + γ n x n + 1 - γ n I - α n F L n x n - γ n x n 1 - γ n = α n + 1 γ ϕ ( x n + 1 ) - F L n + 1 x n + 1 1 - γ n + 1 - α n γ ϕ ( x n ) - F L n x n 1 - γ n + L n + 1 x n + 1 - L n x n
which yields that
l n + 1 - l n α n + 1 γ ϕ x n + 1 - F L n + 1 x n + 1 1 - γ n + 1 + α n γ ϕ ( x n ) - F L n x n 1 - γ n + L n + 1 x n + 1 - L n x n α n + 1 γ ϕ ( x n + 1 ) - F L n + 1 x n + 1 1 - γ n + 1 + α n γ ϕ ( x n ) - F L n x n 1 - γ n + L n + 1 x n + 1 - L n + 1 x n + L n + 1 x n - L n x n α n + 1 γ ϕ ( x n + 1 ) - F L n + 1 x n + 1 1 - γ n + 1 + α n γ ϕ ( x n ) - F L n x n 1 - γ n + x n + 1 - x n + L n + 1 x n - L n x n .
(3.4)
Next, we estimate Ln + 1x n - L n x n . Notice that
L n + 1 x n - L n x n = P C β n + 1 x n + 1 - β n + 1 B n + 1 x n - P C β n x n + 1 - β n B n x n β n + 1 x n + 1 - β n + 1 B n + 1 x n - β n x n + 1 - β n B n x n x n - B n + 1 x n β n + 1 - β n + 1 - β n B n + 1 x n - B n x n x n - B n + 1 x n β n + 1 - β n + 1 - β n i = 1 μ i ( n + 1 ) - μ i ( n ) T i x n .
(3.5)
Substituting (3.5) into (3.4), we have
l n + 1 - l n α n + 1 γ ϕ ( x n + 1 ) - F L n + 1 x n + 1 1 - γ n + 1 + α n γ ϕ ( x n ) - F L n x n 1 - γ n + x n + 1 - x n + x n - B n + 1 x n β n + 1 - β n + 1 - β n i = 1 μ i ( n + 1 ) - μ i ( n ) T i x n .
Hence, we have
l n + 1 - l n - x n + 1 - x n α n + 1 γ ϕ ( x n + 1 ) - F L n + 1 x n + 1 1 - γ n + 1 + α n γ ϕ ( x n ) - F L n x n 1 - γ n + x n - B n + 1 x n β n + 1 - β n + 1 - β n i = 1 μ i ( n + 1 ) - μ i ( n ) T i x n .
Observing conditions (i), (iii), (iv), and the boundedness of {x n } it follows that
lim sup n l n + 1 - l n - x n + 1 - x n 0 .

Thus by Lemma 2.4, we have limn → ∞l n - x n = 0.

From (3.3), we have
x n + 1 - x n = 1 - γ n l n - x n .
Therefore,
lim n x n + 1 - x n = 0 .
(3.6)
Theorem 3.2.. Let H be a real Hilbert space and C is a closed convex subset of H such that C + C C. Let ϕ be an MKC on C. Suppose F: CC is η-strongly monotone and L-Lipschitzian operator and η > γ > 0. Let T i : CE be k i -strictly pseudo-contractive non-self-mapping such that i = 1 F ( T i ) . Assume k = sup{k i : i } < 1. Let {x n } be a sequence of C generated by (1.5) with the sequences {α n }, {β n } and {γ n } in [0,1]. Assume for each n, i = 1 μ i ( n ) = 1 for all n and μ i ( n ) > 0 for all i . They satisfy the conditions (i), (ii), (iii), (iv) of Lemma 3.1 and (v) limn → ∞β n = α, lim n i = 1 μ i n - μ i = 0 and i = 1 μ i = 1 μ i > 0 . Then {x n } converges strongly to x ̃ F , which also solves the following variational inequality
γ ϕ ( x ̃ ) - F x ̃ , p - x ̃ 0 , p i = 1 F ( T i ) .
Proof. From (3.1), we obtain
L n x n - x n x n - x n + 1 + x n + 1 - L n x n = x n - x n + 1 + α n γ ϕ ( x n ) + γ n x n - L n x n - α n F L n x n x n - x n + 1 + α n γ ϕ x n + F L n x n + γ n x n - L n x n .
So L n x n - x n 1 1 - γ n x n - x n + 1 + α n γ ϕ ( x n ) + F L n x n , which together with the condition (i), (iv) and Lemma 3.1 implies
lim n L n x n - x n = 0 .
(3.7)
Define B = i = 1 μ i T i , then B : C → E is a k-strict pseudo-contraction such that F B = i = 1 F ( T i ) by Lemma 2.10, furthermore B n xBx as n → ∞ for all x C by (v). Defines T: CE by
T x = α x + ( 1 - α ) B x .
Then, T is non-expansive with F(T) = F(B) by Lemma 2.3. It follows from Lemma 2.2 that F(P C T) = F(T). Notice that
P C T x n - x n x n - L n x n + L n x n - P C T x n x n - L n x n + β n x n + ( 1 - β n ) B n x n - α x n + ( 1 - α ) B x n x n - L n x n + β n - α x n - B n x n + ( 1 - α ) B n x n - B x n x n - L n x n + β n - a x n - B n x n + ( 1 - α ) B n x n - B x n
which combines with (3.7) yielding that
lim n P C T x n - x n = 0 .
(3.8)
Next, we show that
lim sup n γ ϕ ( x ̃ ) - F x ̃ , x n - x ̃ 0 ,
(3.9)
where x ̃ = lim t 0 x t with x t being the fixed point of the contraction
x t γ ϕ ( x ) + ( 1 - t F ) P C T x .
To see this, we take a subsequence x n k of {x n } such that
lim sup n γ ϕ ( x ̃ ) - F x ̃ , x n - x ̃ = lim k γ ϕ ( x ̃ ) - F x ̃ , x n k - x ̃ .
We may also assume that x n k q . Note that q F(T) in virtue of Lemmas 2.6, 2.2, and (3.8). It follows from Lemma 2.9, we can get that
lim sup n γ ϕ ( x ̃ ) - F x ̃ , x n - x ̃ = lim k γ ϕ ( x ̃ ) - F x ̃ , x n k - x ̃ = γ ϕ ( x ̃ ) - F x ̃ , q - x ̃ 0 .
Finally, we show x n - x ̃ 0 . By contradiction, there is a number ε0 such that
lim sup n x n - x ̃ ε 0 .

Case 1. Fixed ε1 (ε1 < ε0), if for some nN such that x n - x ̃ ε 0 - ε 1 , and for the other nN such that x n - x ̃ < ε 0 - ε 1 .

Let
M n = 2 γ ϕ ( x ̃ ) - F x ̃ , x n + 1 - x ̃ ε 0 - ε 1 2 .
From 3.9, we know lim supn → ∞M n ≤ 0. Hence, there are two numbers h and N, when n > N we have M n h, where h = min η - α n L 2 2 ( 1 - γ n ) - γ . From the above introduction, we can extract a number n0 > N satisfying x n 0 - x ̃ < ε 0 - ε 1 , then we estimate x n 0 + 1 - x ̃ . From Lemma 2.7 and (3.1), we have
x n 0 + 1 - x ̃ 2 = α n 0 γ ϕ x n 0 + γ n 0 x n 0 + 1 - γ n 0 I - α n 0 F y n 0 - x ̃ 2 = 1 - γ n 0 I - α n 0 F y n 0 - 1 - γ n 0 I - α n 0 F x ̃ + α n 0 γ ϕ x n 0 - F x ̃ + γ n 0 x n 0 - x ̃ 2 = 1 - γ n 0 I - α n 0 F y n 0 - 1 - γ n 0 I - α n 0 F x ̃ + α n 0 γ ϕ x n 0 - F x ̃ + γ n 0 x n 0 - x ̃ , x n 0 + 1 - x ̃ = 1 - γ n 0 I - α n 0 F y n 0 - 1 - γ n 0 I - α n 0 F x ̃ , x n 0 + 1 - x ̃ + α n 0 γ ϕ x n 0 - F x ̃ , x n 0 + 1 - x ̃ + γ n 0 x n 0 - x ̃ , x n 0 + 1 - x ̃ = 1 - γ n 0 I - α n 0 F y n 0 - 1 - γ n 0 I - α n 0 F x ̃ , x n 0 + 1 - x ̃ + α n 0 γ ϕ x n 0 - γ ϕ x ̃ , x n 0 + 1 - x ̃ + α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃ + γ n 0 x n 0 - x ̃ , x n 0 + 1 - x ̃ 1 - γ n 0 I - α n 0 F y n 0 - 1 - γ n 0 I - α n 0 F x ̃ x n 0 + 1 - x ̃ + α n 0 γ x n 0 - x ̃ x n 0 + 1 - x ̃ + α n 0 γ ϕ x ̃ - F x ̃ , x n 0 + 1 - x ̃ + γ n 0 x n 0 - x ̃ x n 0 + 1 - x ̃ 1 - γ n 0 - α n 0 η - α n 0 L 2 2 1 - γ n 0 x n 0 - x ̃ x n 0 + 1 - x ̃ + α n 0 γ x n 0 - x ̃ x n 0 + 1 - x ̃ + α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃ + γ n 0 x n 0 - x ̃ x n 0 + 1 - x ̃ = 1 - α n 0 η - α n 0 L 2 2 1 - γ n 0 - γ x n 0 - x ̃ x n 0 + 1 - x ̃ + α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃ 1 2 1 - α n 0 η - α n 0 L 2 2 ( 1 - γ n ) - γ x n - x ̃ 2 + 1 2 x n 0 + 1 - x ̃ 2 + α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃ 1 2 1 - α n 0 η - α n 0 L 2 2 ( 1 - γ n ) - γ ε 0 - ε 2 + 1 2 x n 0 + 1 - x ̃ 2 + α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃
which implies that
x n 0 + 1 - x ̃ 2 < 1 - α n 0 η - α n 0 L 2 2 ( 1 - γ n 0 ) - γ ε 0 - ε 2 + 2 α n 0 γ ϕ ( x ̃ ) - F x ̃ , x n 0 + 1 - x ̃ = 1 - α n η - α n 0 L 2 2 ( 1 - γ n 0 ) - γ - M n 0 ε 0 - ε 1 2 ε 0 - ε 1 2
Hence, we have
x n 0 + 1 - x ̃ < ε 0 - ε 1 .
In the same way, we can get
x n - x ̃ < ε 0 - ε 1 , n n 0 .

It contradict the lim sup n x n - x ̃ ε 0 .

Case 2. Fixed ε1 (ε1 < ε0), if x n - x ̃ ε 0 - ε 1 for all nN . In this case from Lemma 2.8, there is a number r (0,1), such that
ϕ ( x n ) - ϕ ( x ̃ ) r x n - x ̃ , n N .
It follow 3.1 that
x n + 1 - x ̃ 2 = 1 - γ n I - α n F y n - 1 - γ n I - α n F x ̃ , x n + 1 - x ̃ + α n γ ϕ x n - γ ϕ ( x ̃ ) , x n + 1 - x ̃ + α n γ ϕ ( x ̃ ) - F x ̃ , x n + 1 - x ̃ + γ n x n - x ̃ , x n + 1 - x ̃ 1 - γ n - α n η - α n L 2 2 1 - γ n x n - x ̃ x n + 1 - x ̃ + α n γ r x n - x ̃ x n + 1 - x ̃ + α n γ ϕ ( x ̃ ) - F x ̃ , x n + 1 - x ̃ + γ n x n - x ̃ x n + 1 - x ̃ 1 2 1 - α n η - α n L 2 2 ( 1 - γ n ) - γ r x n - x ̃ 2 + 1 2 x n + 1 - x ̃ 2 + 1 2 x n + 1 - x ̃ 2 + α n γ ϕ ( x ̃ ) - F x ̃ , x n + 1 - x ̃
which implies that
x n + 1 - x ̃ 2 1 - α n η - α n L 2 2 1 - γ n - γ r x n - x ̃ 2 + α n η - α n L 2 2 ( 1 - γ n ) - γ r 2 γ ϕ ( x ̃ ) - F x ̃ , x n + 1