Volume 2014, Issue 1 208717
Research Article
Open Access

Hybrid Algorithms for Solving Variational Inequalities, Variational Inclusions, Mixed Equilibria, and Fixed Point Problems

Lu-Chuan Ceng

Lu-Chuan Ceng

Department of Mathematics, Shanghai Normal University, Shanghai 200234, China shnu.edu.cn

Scientific Computing Key Laboratory of Shanghai Universities, Shanghai 200234, China

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Adrian Petrusel

Adrian Petrusel

Department of Applied Mathematics, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania ubbcluj.ro

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Mu-Ming Wong

Corresponding Author

Mu-Ming Wong

Department of Applied Mathematics and Center for Theoretical Science, Chung Yuan Christian University, Chung Li 32023, Taiwan cycu.edu.tw

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Jen-Chih Yao

Jen-Chih Yao

Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan kmu.edu.tw

Department of Mathematics, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia kau.edu.sa

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First published: 27 February 2014
Citations: 1
Academic Editor: Ngai-Ching Wong

Abstract

We present a hybrid iterative algorithm for finding a common element of the set of solutions of a finite family of generalized mixed equilibrium problems, the set of solutions of a finite family of variational inequalities for inverse strong monotone mappings, the set of fixed points of an infinite family of nonexpansive mappings, and the set of solutions of a variational inclusion in a real Hilbert space. Furthermore, we prove that the proposed hybrid iterative algorithm has strong convergence under some mild conditions imposed on algorithm parameters. Here, our hybrid algorithm is based on Korpelevič’s extragradient method, hybrid steepest-descent method, and viscosity approximation method.

1. Introduction

Throughout this paper, we assume that H is a real Hilbert space with inner product 〈·, ·〉 and norm ∥·∥, C is a nonempty closed convex subset of H, and PC is the metric projection of H onto C. Let S : CC be a self-mapping on C. We denote by Fix⁡(S) the set of fixed points of S and by R the set of all real numbers. A mapping A : CH is called ρ-Lipschitzian if there exists a constant ρ ≥ 0 such that
(1)
In particular, if ρ = 1, then A is called a nonexpansive mapping [1]; if ρ ∈ [0,1), then A is called ρ-contraction.
Recall that a mapping A : CH is called
  • (i)

    η-strongly monotone if there exists a constant η > 0 such that

    (2)

  • (ii)

    α-inverse strongly monotone if there exists a constant α > 0 such that

    (3)

It is obvious that if A is α-inverse strongly monotone, then A is monotone and 1/α-Lipschitz continuous. In addition, a mapping V is called strongly positive on H if there exists a constant μ > 0 such that
(4)
Let φ : CR be a real-valued function, let A : HH be a nonlinear mapping, and let Θ : C × CR be a bifunction. In 2008, Peng and Yao [2] introduced the following generalized mixed equilibrium problem (GMEP) of finding xC such that
(5)
We denote the set of solutions of GMEP (5) by GMEP⁡(Θ, φ, A). The GMEP (5) is very general in the sense that it includes, as special cases, optimization problems, variational inequalities, minimax problems, Nash equilibrium problems in noncooperative games, and others. The GMEP is further considered and studied in [38].

We present some special cases of GMEP (5) as follows.

If φ = 0, then GMEP (5) reduces to the generalized equilibrium problem (GEP) which is to find xC such that
(6)
which was introduced and studied by S. Takahashi and W. Takahashi [9]. The set of solutions of GEP is denoted by GEP⁡(Θ, A).
If A = 0, then GMEP (5) reduces to the mixed equilibrium problem (MEP) which is to find xC such that
(7)
which was considered and studied in [10]. The set of solutions of MEP is denoted by MEP⁡(Θ, φ).
If φ = 0, A = 0, then GMEP (5) reduces to the equilibrium problem (EP) which is to find xC such that
(8)
The set of solutions of EP is denoted by EP⁡(Θ). It is worth pointing out that the EP is a unified model of several problems, for instance, variational inequality problems, optimization problems, saddle point problems, complementarity problems, fixed point problems, Nash equilibrium problems, and so forth.
Throughout this paper, it is assumed as in [2] that Θ : C × CR is a bifunction satisfying conditions (A1)–(A4) and φ : CR is a lower semicontinuous and convex function with restriction (B1) or (B2), where
  • (A1)

    Θ(x, x) = 0 for all xC,

  • (A2)

    Θ is monotone, that is, Θ(x, y) + Θ(y, x) ≤ 0 for any x, yC,

  • (A3)

    Θ is upper hemicontinuous, that is, for each x, y, zC,

    (9)

  • (A4)

    Θ(x, ·) is convex and lower semicontinuous for each xC,

  • (B1)

    for each xH and r > 0, there exists a bounded subset DxC and yxC such that for any zCDx,

    (10)

  • (B2)

    C is a bounded set.

Let B be a single-valued mapping of C into H and R a multivalued mapping with D(R) = C. Consider the following variational inclusion: find a point xC such that
(11)
We denote by I(B, R) the solution set of the variational inclusion (11). In particular, if B = R = 0, then I(B, R) = C. If B = 0, then problem (11) becomes the inclusion problem introduced by Rockafellar [11]. It is known that problem (11) provides a convenient framework for the unified study of optimal solutions in many optimization related areas including mathematical programming, complementarity problems, variational inequalities, optimal control, mathematical economics, equilibria, and game theory.

In 1998, Huang [12] studied problem (11) in the case where R is maximal monotone and B is strongly monotone and Lipschitz continuous with D(R) = C = H. Subsequently, Zeng et al. [13] further studied problem (11) in the case which is more general than Huang’s one [12]. Moreover, the authors [13] obtained the same strong convergence conclusion as in Huang’s result [12]. In addition, the authors also gave the geometric convergence rate estimate for approximate solutions. Also, various types of iterative algorithms for solving variational inclusions have been further studied and developed; for more details, refer to [1417] and the references therein.

On the other hand, consider the following variational inequality problem (VIP): find a point xC such that
(12)
The solution set of VIP (12) is denoted by VI⁡  (C, A).
In 1976, Korpelevič [18] proposed an iterative algorithm for solving the VIP (12) in Euclidean space Rn:
(13)
with τ > 0 being a given number, which is known as the extragradient method (see also [19]). The literature on the VIP is vast and Korpelevič’s extragradient method has received great attention given by many authors, who improved it in various ways; see, for example, [2, 5, 6, 8, 17, 2028] and references therein, to name but a few.

VIP (12) was first discussed by Lions [29] and now is well known; there are a lot of different approaches towards solving VIP (12) in finite-dimensional and infinite-dimensional spaces, and the research is intensively continued. VIP (12) has many applications in computational mathematics, mathematical physics, operations research, mathematical economics, optimization theory, and other fields; see, for example, [3033]. It is well known that, if A is a strongly monotone and Lipschitz-continuous mapping on C, then VIP (12) has a unique solution. Not only the existence and uniqueness of solutions are important topics in the study of VIP (12) but also how to actually find a solution of VIP (12) is important.

Let C be a nonempty closed convex subset of a real Banach space X. Let be an infinite family of nonexpansive self-mappings on C and let be a sequence of nonnegative numbers in [0,1]. For any n ≥ 1, define a self-mapping Wn on C as follows:
(14)
Such a mapping Wn is called the W-mapping generated by Tn, Tn−1, …, T1 and λn, λn−1, …, λ1; see [34].

In 2008, Ceng and Yao [35] introduced and analyzed the following relaxed viscosity approximation method for finding a common fixed point of an infinite family of nonexpansive mappings in a strictly convex and reflexive Banach space.

Theorem 1 (see [35], Theorem 3.2.)Let X be a strictly convex and reflexive Banach space with a uniformly Gateaux differentiable norm, let C be a nonempty closed convex subset of X, let be an infinite family of nonexpansive self-mappings on C such that the common fixed point set , and let f : CC be a ρ-contraction with the contraction coefficient ρ ∈ (1/2, 1). Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1). For any given x1C, let be the iterative sequence defined by

(15)
where and are two sequences in (0,1) with αn + βn ≤ 1(n ≥ 1), is a sequence in [0,1], and Wn is the W-mapping defined by (14). Assume that
  • (i)

    lim⁡nαn = 0, and 0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1,

  • (ii)

    lim⁡n | γn+1γn | = 0 and lim⁡ sup⁡nγn < 1.

Then there hold the following:
  • (I)

    lim⁡nxn+1xn∥ = 0;

  • (II)

    the sequence converges strongly to some , provided lim⁡nγn = 0 and βnβ for some fixed β ∈ (0,1), which is the unique solution of the VIP:

    (16)

  • where J is the normalized duality mapping of X.

Furthermore, let λn,1, λn,2, …, λn,N ∈ (0,1], n ≥ 1. Given the nonexpansive mappings S1, S2, …, SN on H, Atsushiba and Takahashi [36] defined, for each n ≥ 1, mappings Un,1, Un,2, …, Un,N by

(17)

The Wn is called the W-mapping generated by S1, …, SN and λn,1, λn,2, …, λn,N. Note that the nonexpansivity of Si implies the nonexpansivity of Wn.

In 2008, Colao et al. [37] introduced and studied an iterative method for finding a common element of the set of solutions of an equilibrium problem and the set of fixed points of a finite family of nonexpansive mappings in a real Hilbert space H. Subsequently, combining Yamada’s hybrid steepest-descent method [38] and Colao et al.’s hybrid viscosity approximation method [37], Ceng et al. [7] proposed and analyzed the following hybrid iterative method for finding a common element of the set of solutions of GMEP⁡ (5) and the set of fixed points of a finite family of nonexpansive mappings .

Theorem 2 (see [7], Theorem 3.1.)Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ : C × CR be a bifunction satisfying assumptions (A1)–(A4) and let φ : CR be a lower semicontinuous and convex function with restriction (B1) or (B2). Let the mapping A : HH be δ-inverse strongly monotone, and let be a finite family of nonexpansive mappings on H such that . Let F : HH be a κ-Lipschitzian and η-strongly monotone operator with constants κ, η > 0 and f : HH a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let 0 < μ < 2η/κ2 and 0 ≤ γρ < τ, where . Suppose that {αn} and {βn} are two sequences in (0,1), {γn} is a sequence in (0,2δ], and is a sequence in [a, b] with 0 < ab < 1. For every n ≥ 1, let Wn be the W-mapping generated by S1, …, SN and λn,1, λn,2, …, λn,N. Given x1H arbitrarily, suppose that the sequences {xn} and {un} are generated iteratively by

(18)
where the sequences {αn}, {βn}, {rn} and the finite family of sequences satisfy the following conditions:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    0 < lim⁡ inf⁡nrn ≤ lim⁡ sup⁡nrn < 2δ and lim⁡n(rn+1rn) = 0;

  • (iv)

    lim⁡n(λn+1,iλn,i) = 0 for all i = 1,2, …, N.

Then, both {xn} and {un} converge strongly to , where is a unique solution of the variational inequality:
(19)

On the other hand, whenever C = H a real Hilbert space, Yao et al. [4] very recently introduced and analyzed an iterative algorithm for finding a common element of the set of solutions of GMEP⁡ (5), the set of solutions of the variational inclusion (11), and the set of fixed points of an infinite family of nonexpansive mappings.

Theorem 3 (see [4], Theorem 3.2.)Let φ : HR be a lower semicontinuous and convex function and let Θ : H × HR be a bifunction satisfying conditions (A1)–(A4) and (B1). Let V be a strongly positive bounded linear operator with coefficient μ > 0 and let R : H → 2H be a maximal monotone mapping. Let the mappings A, B : HH be α-inverse strongly monotone and β-inverse strongly monotone, respectively. Let f : HH be a ρ-contraction. Let r > 0, γ > 0, and λ > 0 be three constants such that r < 2α, λ < 2β, and 0 < γ < μ/ρ. Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and an infinite family of nonexpansive self-mappings on H such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(20)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14) (with X = H and C = H). Assume that the following conditions are satisfied:
  • (C1)

    lim⁡nαn = 0 and ;

  • (C2)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1.

Then, the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(γf(x*)+(IV)x*) is a unique solution of the VIP:
(21)

Motivated and inspired by the above facts, in this paper, we introduce and analyze a hybrid iterative algorithm for finding a common element of the set of solutions of a finite family of generalized mixed equilibrium problems, the set of solutions of a finite family of variational inequalities for inverse strong monotone mappings, the set of fixed points of an infinite family of nonexpansive mappings, and the set of solutions of the variational inclusion (11) in a real Hilbert space. Furthermore, it is proven that the proposed hybrid iterative algorithm is strongly convergent under some mild conditions imposed on algorithm parameters. Here our hybrid algorithm is based on Korpelevič’s extragradient method, hybrid steepest-descent method, and viscosity approximation method. The results obtained in this paper improve and extend the corresponding results announced by many others.

2. Preliminaries

Let H be a real Hilbert space whose inner product and norm are denoted by 〈·, ·〉 and ∥·∥, respectively. Let C be a nonempty closed convex subset of H. We write xnx to indicate that the sequence {xn} converges weakly to x and xnx to indicate that the sequence {xn} converges strongly to x. Moreover, we use ωw(xn) to denote the weak ω-limit set of the sequence {xn}; that is,
(22)
The metric (or nearest point) projection from H onto C is the mapping PC : HC which assigns to each point xH the unique point PCxC satisfying the property
(23)

Some important properties of projections are gathered in the following proposition.

Proposition 4 (see [31], [39].)For given xH and zC:

  • (i)

    z = PCx⇔〈xz, yz〉 ≤ 0, ∀yC;

  • (ii)

    z = PCx⇔∥xz2 ≤ ∥xy2 − ∥yz2, ∀yC;

  • (iii)

    , ∀yH.

Consequently, PC is nonexpansive and monotone.

If A is an α-inverse strongly monotone mapping (α-ism) of C into H, then it is obvious that A is 1/α-Lipschitzian. We also have that, for all u, vC and λ > 0,

(24)
So, if λ ≤ 2α, then IλA is a nonexpansive mapping from C to H.

It is also easy to see that a projection PC is 1-ism. Inverse strongly monotone (also referred to as cocoercive) operators have been applied widely in solving practical problems in various fields.

A set-valued mapping R : D(R) ⊂ H → 2H is called monotone if, for all x, yD(R), fR(x) and gR(y) imply

(25)
A set-valued mapping R is called maximal monotone if R is monotone and (I + λR)D(R) = H for each λ > 0, where I is the identity mapping of H. We denote by G(R) the graph of R. It is known that a monotone mapping R is maximal if and only if, for (x, f) ∈ H × H, 〈fg, xy〉≥0 for every (y, g) ∈ G(R) implies fR(x).

Let A : CH be a monotone, k-Lipschitzian mapping and let NCv be the normal cone to C at vC; that is,

(26)
Defining
(27)
then, T is maximal monotone and 0 ∈ Tv if and only if v ∈ VI (C, A); see [11].

Assume that R : D(R) ⊂ H → 2H is a maximal monotone mapping. Then, for λ > 0, associated with R, the resolvent operator JR,λ can be defined as

(28)
In terms of Huang [12] (see also [13]), there holds the following property for the resolvent operator .

Lemma 5. JR,λ is single-valued and firmly nonexpansive; that is,

(29)
Consequently, JR,λ is nonexpansive and monotone.

Lemma 6. Let C be a nonempty closed convex subset of H and A : CH a monotone mapping. In the context of the VIP (12), there holds the following relation:

(30)

Lemma 7 (see [17].)Let R be a maximal monotone mapping with D(R) = C. Then, for any given λ > 0, uC is a solution of problem (11) if and only if uC satisfies

(31)

Lemma 8 (see [13].)Let R be a maximal monotone mapping with D(R) = C and let B : CH be a strongly monotone, continuous, and single-valued mapping. Then, for each zH, the equation z ∈ (B + λR)x has a unique solution xλ for λ > 0.

Lemma 9 (see [17].)Let R be a maximal monotone mapping with D(R) = C and B : CH a monotone, continuous, and single-valued mapping. Then (I + λ(R + B))C = H for each λ > 0. In this case, R + B is maximal monotone.

Lemma 10 ([40], see also [10]). Assume that Θ : C × CR satisfies (A1)–(A4) and let φ : CR be a proper lower semicontinuous and convex function. Assume that either (B1) or (B2) holds. For r > 0 and xH, define a mapping as follows:

(32)
for all xH. Then the following hold:
  • (i)

    for each xH, ;

  • (ii)

    is single-valued;

  • (iii)

    is firmly nonexpansive, that is, for any x, yH,

    (33)

  • (iv)

    ;

  • (v)

    MEP⁡(Θ, φ) is closed and convex.

Proposition 11 (see [5], Proposition 2.1.)Let C, H, Θ, φ, and be as in Lemma 10. Then the following inequality holds:

(34)
for all s, t > 0 and xH.

Remark 12. From the conclusion of Proposition 11, it immediately follows that

(35)
for all s, t > 0 and xH.

Lemma 13 (see [41].)Let {xn} and {zn} be bounded sequences in a Banach space X and {βn} a sequence in [0,1] with

(36)
Suppose that xn+1 = (1 − βn)zn + βnxn for each n ≥ 1 and
(37)
Then lim⁡nznxn∥ = 0.

We have the following crucial lemmas concerning the W-mapping defined by (14).

Lemma 14 (see [42], Lemma 3.2.)Let C be a nonempty closed convex subset of a strictly convex Banach space X. Let be a sequence of nonexpansive self-mappings on C such that and let be a sequence of positive numbers in (0, b] for some b ∈ (0,1). Then, for every xC and k ≥ 1, the limit lim⁡nUn,kx exists.

Lemma 15 (see [42], Lemma 3.3.)Let C be a nonempty closed convex subset of a strictly convex Banach space X. Let be a sequence of nonexpansive self-mappings on C such that and let be a sequence of positive numbers in (0, b] for some b ∈ (0,1). Then, .

Remark 16. Using Lemma 14, we can define the mapping W : CC as follows:

(38)
Such a W is called the W-mapping generated by the sequences and . As pointed out in [43], if {xn} is a bounded sequence in C, then we have
(39)
Throughout this paper, we always assume that is a sequence of positive numbers in (0, b] for some b ∈ (0,1).

Lemma 17 (see [44].)Let {sn} be a sequence of nonnegative numbers satisfying the conditions

(40)
where {αn} and {βn} are sequences of real numbers such that
  • (i)

    {αn}⊂[0,1] and , or equivalently,

    (41)

  • (ii)

    lim⁡ sup⁡nβn ≤ 0, or .

Then lim⁡nsn = 0.

Lemma 18 (see [39], demiclosedness principle.)Let C be a nonempty closed convex subset of a real Hilbert space H. Let T be a nonexpansive self-mapping on C with Fix⁡(T) ≠ . Then IT is demiclosed. That is, whenever {xn} is a sequence in C weakly converging to some xC and the sequence {(IT)xn} strongly converges to some y, it follows that (IT)x = y. Here I is the identity operator of H.

The following lemma is an immediate consequence of an inner product.

Lemma 19. In a real Hilbert space H, there holds the inequality

(42)

Let C be a nonempty closed convex subset of a real Hilbert space H. We introduce some notations. Let λ be a number in (0,1] and let μ > 0. Associating with a nonexpansive mapping T : CC, we define the mapping Tλ : CH by

(43)
where F : CH is an operator such that, for some positive constants κ, η > 0, F is κ-Lipschitzian and η-strongly monotone on C; that is, F satisfies the conditions
(44)
for all x, yC.

Lemma 20 (see [44], Lemma 3.1.)Tλ is a contraction provided 0 < μ < 2η/κ2; that is,

(45)
where .

Remark 21. (i) Since F is κ-Lipschitzian and η-strongly monotone on C, we get 0 < ηκ. Hence, whenever 0 < μ < 2η/κ2, we have

(46)
which implies
(47)
So, .

(ii) In Lemma 20, put F = (1/2)I and μ = 2. Then we know that κ = η = 1/2, 0 < μ = 2 < 2η/κ2 = 4 and

(48)

3. A Strong Convergence Theorem

In this section, we will prove a strong convergence theorem for a hybrid iterative algorithm for finding a common element of the set of solutions of a finite family of generalized mixed equilibrium problems, the set of solutions of a finite family of variational inequalities for inverse strong monotone mappings, the set of fixed points of an infinite family of nonexpansive mappings, and the set of solutions of the variational inclusion (11) in a real Hilbert space.

Theorem 22. Let C be a nonempty closed convex subset of a real Hilbert space H. Let M, N be two integers. Let Θk be a bifunction from C × C to R satisfying (A1)–(A4) and let φk : CR ∪ {+} be a proper lower semicontinuous and convex function, where k ∈ {1,2, …, M}. Let Bk : HH and Ai : CH be μk-inverse strongly monotone and ηi-inverse strongly monotone, respectively, where k ∈ {1,2, …, M}, i ∈ {1,2, …, N}. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(49)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {λi,n}⊂[ai, bi]⊂(0,2ηi) and lim⁡n | λi,n+1λi,n | = 0 for all i ∈ {1,2, …, N};

  • (iv)

    {rk,n}⊂[ek, fk]⊂(0,2μk) and lim⁡n | rk,n+1rk,n | = 0 for all k ∈ {1,2, …, M}.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(50)

Proof. Let Q = PΩ. Note that F : CH is a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and f : HH is a ρ-Lipschitzian mapping with constant ρ ≥ 0. Then, we have

(51)
where , and hence
(52)
Since 0 ≤ γρ < τ ≤ 1, it is known that 1 − (τγρ)∈[0,1). Therefore, Q(IμF + γf) is a contraction of C into itself, which implies that there exists a unique element x*C such that x* = Q(IμF + γf)x* = PΩ(IμF + γf)x*.

We divide the remainder of the proof into several steps.

Step  1. Let us show that {xn} is bounded.

Indeed, taking into account the control conditions (i) and (ii), we may assume, without loss of generality, that αn ≤ 1 − βn for all n ≥ 1. Put

(53)
for all k ∈ {1,2, …, M} and n ≥ 1,
(54)
for all i ∈ {1,2, …, N} and n ≥ 1, and , where I is the identity mapping on H. Then we have that and . Take p ∈ Ω arbitrarily. Then from (24) and Lemma 10 we have
(55)
Similarly, we have
(56)
Combining (55) and (56), we have
(57)
Since the mapping B : CH is β-inverse strongly monotone with 0 < λ < 2β, we have
(58)
It is clear that, if 0 ≤ λ ≤ 2β, then IλB is nonexpansive. Set yn = JR,λ(vnλBvn) for each n ≥ 1. It follows that
(59)
which, together with (57), yields
(60)
Utilizing Lemma 20, from (49) we obtain
(61)
By induction, we get
(62)
Therefore, {xn} is bounded and hence {un}, {vn}, {yn}, {Wnyn}, {FWnyn}, and {f(xn)} are also bounded.

Step  2. Let us show that ∥xn+1xn∥ → 0 as n.

Indeed, define xn+1 = βnxn + (1 − βn)zn for each n ≥ 1. Then from the definition of zn we obtain

(63)
It follows that
(64)
Utilizing (14) and the nonexpansivity of Ti and Un,i, we obtain that for each n ≥ 1
(65)
where for some . Note that
(66)
Note that
(67)
where for some . Also, utilizing Lemma 10 and Proposition 11 we deduce that
(68)
where is a constant such that for each n ≥ 1
(69)
Combining (64)–(68), we get
(70)
Consequently, it follows from (70), {λn}⊂(0, b], and conditions (i)–(iv) that
(71)
Hence, by Lemma 13 we have
(72)
Consequently
(73)

Step  3. Let us show that ∥BvnBp∥ → 0, , and , k ∈ {1,2, …, M}, i ∈ {1,2, …, N}.

Indeed, we can rewrite (49) as follows:

(74)
It follows that
(75)
that is,
(76)
This, together with αn → 0 and (73), implies that
(77)
Also, from (24) it follows that for all i ∈ {1,2, …, N} and k ∈ {1,2, …, M}
(78)
So, from (57), (58), and (78), it follows that
(79)
By (49) and Lemma 20, we obtain
(80)
From (79) and (80), it follows that
(81)
and so
(82)
Since {λi,n}⊂[ai, bi]⊂(0,2ηi) and {rk,n}⊂[ek, fk]⊂(0,2μk) for all i ∈ {1,2, …, N} and k ∈ {1,2, …, M}, by (73), (77), and (82) we conclude immediately that
(83)
for all i ∈ {1,2, …, N} and k ∈ {1,2, …, M}.

Step  4. Let us show that ∥xnWxn∥ → 0.

Indeed, by Lemma 10 (iii) we obtain that for each k ∈ {1,2, …, M}

(84)
which implies that
(85)
Also, by Proposition 4 (iii), we obtain that for each i ∈ {1,2, …, N}
(86)
which implies that
(87)
Since JR,λ is 1-inverse strongly monotone, we have
(88)
which implies that
(89)
Thus, from (85)–(89) we get
(90)
Substituting (90) into (80), we have
(91)
that is,
(92)
So, from αn → 0, (73), (77), and (83) we immediately get
(93)
for all i ∈ {1,2, …, N} and k ∈ {1,2, …, M}. Note that
(94)
(95)
Thus, from (93) we have
(96)
It is easy to see that as n
(97)
Also, observe that
(98)
Hence, we have
(99)
Since
(100)
it follows from Remark 16 that
(101)
This, together with ∥xnyn∥ → 0, implies that
(102)

Step  5. Let us show that limsup⁡n〈(γfμF)x*, xnx*〉≤0 where x* = PΩ(IμF + γf)x*.

Indeed, as previously noted, it is known that PΩ(IμF + γf) is contractive and so PΩ(IμF + γf) has a unique fixed point, denoted by x*C. This implies that x* = PΩ(IμF + γf)x*.

First, we show that ωw(xn) ⊂ Ω. As a matter of fact, we note that there exists a subsequence of {xn} such that

(103)
Since is bounded, there exists a subsequence of which converges weakly to w. Without loss of generality, we may assume that . Note that ∥Wxnxn∥ → 0. Then, by the demiclosedness principle for nonexpansive mappings, we obtain . Furthermore, from (93) and (96), we have that , , , , and , where k ∈ {1,2, …, M} and m ∈ {1,2, …, N}.

Now we prove that . Let

(104)
where m ∈ {1,2, …, N}. Let (v, u) ∈ G(Tm). Since uAmvNCv and , we have
(105)
On the other hand, from and vC, we have
(106)
and hence
(107)
Therefore, we have
(108)
From (93) and since Am is Lipschitzian, we obtain that . From , {λm,n}⊂[am, bm]⊂(0,2ηm), ∀m ∈ {1,2, …, N}, and (93), we have
(109)
Since Tm is maximal monotone, we have and hence wVI⁡(C, Am), m = 1,2, …, N, which implies that .

Next we prove that . Since , n ≥ 1, k ∈ {1,2, …, M}, we have

(110)
By (A2), we have
(111)
Let zt = ty + (1 − t)w for all t ∈ (0,1] and yC. This implies that ztC. Then, we have
(112)
By (93) and the fact that Bk is Lipschitzian, we have as n. In addition, by the monotonicity of Bk, we obtain . Then, by (A4) and (112) we obtain
(113)
Utilizing (A1), (A4), and (113), we obtain
(114)
and hence
(115)
Letting t → 0, we have, for each yC,
(116)
This implies that wGMEP⁡(Θk, φk, Bk) and hence .

Further, we prove that w ∈ I(B, R). In fact, since B is β-inverse strongly monotone, B is monotone and Lipschitzian. It follows from Lemma 9 that R + B is maximal monotone. Let (v, g) ∈ G(R + B); that is, gBvRv. Again, since , we have ; that is, . By virtue of the monotonicity of R, we have

(117)
and so
(118)
Since ∥vnyn∥ → 0, ∥BvnByn∥ → 0, and , we have
(119)
It follows from the maximal monotonicity of B + R that 0 ∈ (R + B)w; that is, w ∈ I(B, R). Therefore, w ∈ Ω. This shows that ωw({xn}) ⊂ Ω. Consequently, it follows from (103) that
(120)

Step  6. Let us show that xnx* as n.

Indeed, from (49) and Lemma 20 it follows that

(121)
which immediately yields
(122)
where
(123)
It is easy to see that and lim⁡ sup⁡nσn ≤ 0. Hence, by Lemma 17 we conclude that the sequence {xn} converges strongly to x*. This completes the proof.

From Theorem 22, we can readily derive the following.

Corollary 23. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4) and let φ : CR ∪ {+} be a proper lower semicontinuous and convex function. Let : HH and Ai : CH be ζ-inverse strongly monotone and ηi-inverse strongly monotone, respectively, where i = 1,2. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and let be an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(124)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {λi,n}⊂[ai, bi]⊂(0,2ηi) and lim⁡n|λi,n+1λi,n| = 0 for i = 1,2;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(125)

Corollary 24. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4) and let φ : CR ∪ {+} be a proper lower semicontinuous and convex function. Let : HH and 𝒜 : CH be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and let be an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(126)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(127)

Corollary 25. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4) and let φ : CR ∪ {+} be a proper lower semicontinuous and convex function. Let 𝒜 : CH be ξ-inverse strongly monotone and f : HH a ρ-contractive mapping with constant ρ ∈ [0,1). Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β. Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and let be an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(128)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0, ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩf(x*) is a unique solution of the VIP:
(129)

Proof. In Corollary 24, put = 0, F = (1/2)I, μ = 2, and γ = 1. Then from Remark 16 (ii), we get τ = 1. Moreover, for {rn}⊂[e, f]⊂(0, ), we can choose a positive constant ζ > 0 such that {rn}⊂[e, f]⊂(0,2ζ). It is easy to see that is ζ-inverse strongly monotone. In addition, for the contraction f : HH, we have 0 ≤ γρ < τ. Hence, all the conditions of Corollary 24 are satisfied. Thus, in terms of Corollary 24, we obtain the desired result.

Corollary 26. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4) and let φ : CR ∪ {+} be a proper lower semicontinuous and convex function. Let 𝒜 : CH be ξ-inverse strongly monotone. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and let be an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(130)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0, ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(131)

Corollary 27. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4) and let φ : CR ∪ {+} be a proper lower semicontinuous and convex function. Let : HH and 𝒜 : CH be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Assume that Ω : = GMEP⁡(Θ, φ, )∩VI⁡(C, 𝒜)∩ I (B, R) ≠ . For arbitrarily given x1H, let the sequence {xn} be generated by

(132)
where {αn}, {βn} are two real sequences in [0,1]. Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(133)

Corollary 28. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4). Let : HH and 𝒜 : CH be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Let be a sequence of positive numbers in (0, b] for some b ∈ (0,1) and let be an infinite family of nonexpansive self-mappings on C such that . For arbitrarily given x1H, let the sequence {xn} be generated by

(134)
where {αn}, {βn} are two real sequences in [0,1] and Wn is the W-mapping defined by (14). Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(135)

Corollary 29. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4). Let : HH and 𝒜 : CH be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : CH be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : HH be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Assume that Ω : = GEP⁡(Θ, )∩VI⁡(C, 𝒜)∩ I (B, R) ≠ . For arbitrarily given x1H, let the sequence {xn} be generated by

(136)
where {αn}, {βn} are two real sequences in [0,1]. Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩ(IμF + γf)x* is a unique solution of the VIP:
(137)

Corollary 30. Let C be a nonempty closed convex subset of a real Hilbert space H. Let Θ be a bifunction from C × C to R satisfying (A1)–(A4). Let : HH and 𝒜 : CH be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let f : HH be a ρ-contractive mapping with constant ρ ∈ [0,1). Let R : C → 2H be a maximal monotone mapping and let the mapping B : CH be β-inverse strongly monotone. Let 0 < λ < 2β. Assume that Ω : = GEP⁡(Θ, )∩VI⁡(C, 𝒜)∩ I (B, R) ≠ . For arbitrarily given x1H, let the sequence {xn} be generated by

(138)
where {αn}, {βn} are two real sequences in [0,1]. Assume that the following conditions are satisfied:
  • (i)

    lim⁡nαn = 0 and ;

  • (ii)

    0 < lim⁡ inf⁡nβn ≤ lim⁡ sup⁡nβn < 1;

  • (iii)

    {ρn}⊂[a, b]⊂(0,2ξ) and lim⁡n|ρn+1ρn| = 0;

  • (iv)

    {rn}⊂[e, f]⊂(0,2ζ) and lim⁡n|rn+1rn| = 0.

Assume that either (B1) or (B2) holds. Then the sequence {xn} converges strongly to x* ∈ Ω, where x* = PΩf(x*) is a unique solution of the VIP:
(139)

Remark 31. Theorem 22 extends, improves, and supplements [4, Theorem 3.2] in the following aspects.

  • (i)

    The problem of finding a point in Theorem 22 is very different from the problem of finding a point in [4, Theorem 3.2] (i.e., Theorem 3 in this paper). There is no doubt that the problem of finding a point is more general and more subtle than the problem of finding a point in [4, Theorem 3.2].

  • (ii)

    If, in Corollary 24, C = H, 𝒜 = 0, rn = r > 0 (∀n ≥ 1), μF = V is a strongly positive bounded linear operator, and f is a contraction, then Corollary 24 reduces essentially to [4, Theorem 3.2]. This shows that Theorem 22 includes [4, Theorem 3.2] as a special case.

  • (iii)

    The iterative scheme in [4, Algorithm 3.1] is extended to develop the iterative scheme in Theorem 22 by virtue of Korpelevič’s extragradient method and hybrid steepest-descent method [38]. The iterative scheme in Theorem 22 is more advantageous and more flexible than the iterative scheme in [4, Algorithm 3.1] because it involves solving four problems: a finite family of GMEPs, a finite family of VIPs, the variational inclusion (11), and the fixed point problem of an infinite family of nonexpansive self-mappings.

  • (iv)

    The iterative scheme in Theorem 22 is very different from the iterative scheme in [4, Algorithm 3.1] because the iterative scheme in Theorem 22 involves Korpelevič’s extragradient method and hybrid steepest-descent method.

  • (v)

    The proof of Theorem 22 combines the proof for viscosity approximation method in [4, Theorem 3.2], the proof for Korpelevič’s extragradient method in [8, Theorem 3.1], and the proof for hybrid steepest-descent method in [44, Theorem 3.1].

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This research was partially supported by the National Science Foundation of China (11071169), the Innovation Program of Shanghai Municipal Education Commission (09ZZ133), and the Ph.D. Program Foundation of Ministry of Education of China (20123127110002). The work benefits from the financial support of a Grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, Project no. PN-II-ID-PCE-2011-3-0094. This research was partially supported by the Grant NSC 102-2115-M-033-002. This research was partially supported by the Grant NSC 102-2115-M-037-002-MY3.

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