Hybrid Algorithms for Solving Variational Inequalities, Variational Inclusions, Mixed Equilibria, and Fixed Point Problems
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
- (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)
We present some special cases of GMEP (5) as follows.
- (A1)
Θ(x, x) = 0 for all x ∈ C,
- (A2)
Θ is monotone, that is, Θ(x, y) + Θ(y, x) ≤ 0 for any x, y ∈ C,
- (A3)
Θ is upper hemicontinuous, that is, for each x, y, z ∈ C,
(9) - (A4)
Θ(x, ·) is convex and lower semicontinuous for each x ∈ C,
- (B1)
for each x ∈ H and r > 0, there exists a bounded subset Dx ⊂ C and yx ∈ C such that for any z ∈ C∖Dx,
(10) - (B2)
C is a bounded set.
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 [14–17] and the references therein.
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, [30–33]. 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.
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 : C → C 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 x1 ∈ C, let be the iterative sequence defined by
- (i)
limn→∞αn = 0, and 0 < lim infn→∞βn ≤ lim supn→∞βn < 1,
- (ii)
limn→∞ | γn+1 − γn | = 0 and lim supn→∞γn < 1.
- (I)
limn→∞∥xn+1 − xn∥ = 0;
- (II)
the sequence converges strongly to some , provided limn→∞γn = 0 and βn ≡ β 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
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 × C → R be a bifunction satisfying assumptions (A1)–(A4) and let φ : C → R be a lower semicontinuous and convex function with restriction (B1) or (B2). Let the mapping A : H → H be δ-inverse strongly monotone, and let be a finite family of nonexpansive mappings on H such that . Let F : H → H be a κ-Lipschitzian and η-strongly monotone operator with constants κ, η > 0 and f : H → H 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 < a ≤ b < 1. For every n ≥ 1, let Wn be the W-mapping generated by S1, …, SN and λn,1, λn,2, …, λn,N. Given x1 ∈ H arbitrarily, suppose that the sequences {xn} and {un} are generated iteratively by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
0 < lim infn→∞rn ≤ lim supn→∞rn < 2δ and limn→∞(rn+1 − rn) = 0;
- (iv)
limn→∞(λn+1,i − λn,i) = 0 for all i = 1,2, …, N.
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 : H → R be a lower semicontinuous and convex function and let Θ : H × H → R 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 : H → H be α-inverse strongly monotone and β-inverse strongly monotone, respectively. Let f : H → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (C1)
limn→∞αn = 0 and ;
- (C2)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1.
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
Some important properties of projections are gathered in the following proposition.
Proposition 4 (see [31], [39].)For given x ∈ H and z ∈ C:
- (i)
z = PCx⇔〈x − z, y − z〉 ≤ 0, ∀y ∈ C;
- (ii)
z = PCx⇔∥x−z∥2 ≤ ∥x−y∥2 − ∥y−z∥2, ∀y ∈ C;
- (iii)
, ∀y ∈ H.
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, v ∈ C and λ > 0,
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, y ∈ D(R), f ∈ R(x) and g ∈ R(y) imply
Let A : C → H be a monotone, k-Lipschitzian mapping and let NCv be the normal cone to C at v ∈ C; that is,
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
Lemma 5. JR,λ is single-valued and firmly nonexpansive; that is,
Lemma 6. Let C be a nonempty closed convex subset of H and A : C → H a monotone mapping. In the context of the VIP (12), there holds the following relation:
Lemma 7 (see [17].)Let R be a maximal monotone mapping with D(R) = C. Then, for any given λ > 0, u ∈ C is a solution of problem (11) if and only if u ∈ C satisfies
Lemma 8 (see [13].)Let R be a maximal monotone mapping with D(R) = C and let B : C → H be a strongly monotone, continuous, and single-valued mapping. Then, for each z ∈ H, 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 : C → H 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 × C → R satisfies (A1)–(A4) and let φ : C → R be a proper lower semicontinuous and convex function. Assume that either (B1) or (B2) holds. For r > 0 and x ∈ H, define a mapping as follows:
- (i)
for each x ∈ H, ;
- (ii)
is single-valued;
- (iii)
is firmly nonexpansive, that is, for any x, y ∈ H,
(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:
Remark 12. From the conclusion of Proposition 11, it immediately follows that
Lemma 13 (see [41].)Let {xn} and {zn} be bounded sequences in a Banach space X and {βn} a sequence in [0,1] with
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 x ∈ C and k ≥ 1, the limit limn→∞Un,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 : C → C as follows:
Lemma 17 (see [44].)Let {sn} be a sequence of nonnegative numbers satisfying the conditions
- (i)
{αn}⊂[0,1] and , or equivalently,
(41) - (ii)
lim supn→∞βn ≤ 0, or .
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 I − T is demiclosed. That is, whenever {xn} is a sequence in C weakly converging to some x ∈ C and the sequence {(I − T)xn} strongly converges to some y, it follows that (I − T)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
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 : C → C, we define the mapping Tλ : C → H by
Lemma 20 (see [44], Lemma 3.1.)Tλ is a contraction provided 0 < μ < 2η/κ2; that is,
Remark 21. (i) Since F is κ-Lipschitzian and η-strongly monotone on C, we get 0 < η ≤ κ. Hence, whenever 0 < μ < 2η/κ2, we have
(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
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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function, where k ∈ {1,2, …, M}. Let Bk : H → H and Ai : C → H be μk-inverse strongly monotone and ηi-inverse strongly monotone, respectively, where k ∈ {1,2, …, M}, i ∈ {1,2, …, N}. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{λi,n}⊂[ai, bi]⊂(0,2ηi) and limn→∞ | λi,n+1 − λi,n | = 0 for all i ∈ {1,2, …, N};
- (iv)
{rk,n}⊂[ek, fk]⊂(0,2μk) and limn→∞ | rk,n+1 − rk,n | = 0 for all k ∈ {1,2, …, M}.
Proof. Let Q = PΩ. Note that F : C → H is a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and f : H → H is a ρ-Lipschitzian mapping with constant ρ ≥ 0. Then, we have
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
Step 2. Let us show that ∥xn+1 − xn∥ → 0 as n → ∞.
Indeed, define xn+1 = βnxn + (1 − βn)zn for each n ≥ 1. Then from the definition of zn we obtain
Step 3. Let us show that ∥Bvn − Bp∥ → 0, , and , k ∈ {1,2, …, M}, i ∈ {1,2, …, N}.
Indeed, we can rewrite (49) as follows:
Step 4. Let us show that ∥xn − Wxn∥ → 0.
Indeed, by Lemma 10 (iii) we obtain that for each k ∈ {1,2, …, M}
Step 5. Let us show that limsupn→∞〈(γf − μF)x*, xn − x*〉≤0 where x* = PΩ(I − μF + γf)x*.
Indeed, as previously noted, it is known that PΩ(I − μF + γf) is contractive and so PΩ(I − μF + γf) has a unique fixed point, denoted by x* ∈ C. This implies that x* = PΩ(I − μF + 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
Now we prove that . Let
Next we prove that . Since , n ≥ 1, k ∈ {1,2, …, M}, we have
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, g − Bv ∈ Rv. Again, since , we have ; that is, . By virtue of the monotonicity of R, we have
Step 6. Let us show that xn → x* as n → ∞.
Indeed, from (49) and Lemma 20 it follows that
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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function. Let ℬ : H → H and Ai : C → H be ζ-inverse strongly monotone and ηi-inverse strongly monotone, respectively, where i = 1,2. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{λi,n}⊂[ai, bi]⊂(0,2ηi) and limn→∞|λi,n+1 − λi,n| = 0 for i = 1,2;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function. Let ℬ : H → H and 𝒜 : C → H be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function. Let 𝒜 : C → H be ξ-inverse strongly monotone and f : H → H a ρ-contractive mapping with constant ρ ∈ [0,1). Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0, ∞) and limn→∞|rn+1 − rn| = 0.
Proof. In Corollary 24, put ℬ = 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 ℬ is ζ-inverse strongly monotone. In addition, for the contraction f : H → H, 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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function. Let 𝒜 : C → H be ξ-inverse strongly monotone. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0, ∞) and limn→∞|rn+1 − rn| = 0.
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 : C → R ∪ {+∞} be a proper lower semicontinuous and convex function. Let ℬ : H → H and 𝒜 : C → H be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Assume that Ω : = GMEP(Θ, φ, ℬ)∩VI(C, 𝒜)∩ I (B, R) ≠ ∅. For arbitrarily given x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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 ℬ : H → H and 𝒜 : C → H be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H 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 x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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 ℬ : H → H and 𝒜 : C → H be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let F : C → H be a κ-Lipschitzian and η-strongly monotone operator with positive constants κ, η > 0 and let f : H → H be a ρ-Lipschitzian mapping with constant ρ ≥ 0. Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H be β-inverse strongly monotone. Let 0 < λ < 2β, 0 < μ < 2η/κ2, and 0 ≤ γρ < τ, where . Assume that Ω : = GEP(Θ, ℬ)∩VI(C, 𝒜)∩ I (B, R) ≠ ∅. For arbitrarily given x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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 ℬ : H → H and 𝒜 : C → H be ζ-inverse strongly monotone and ξ-inverse strongly monotone, respectively. Let f : H → H be a ρ-contractive mapping with constant ρ ∈ [0,1). Let R : C → 2H be a maximal monotone mapping and let the mapping B : C → H be β-inverse strongly monotone. Let 0 < λ < 2β. Assume that Ω : = GEP(Θ, ℬ)∩VI(C, 𝒜)∩ I (B, R) ≠ ∅. For arbitrarily given x1 ∈ H, let the sequence {xn} be generated by
- (i)
limn→∞αn = 0 and ;
- (ii)
0 < lim infn→∞βn ≤ lim supn→∞βn < 1;
- (iii)
{ρn}⊂[a, b]⊂(0,2ξ) and limn→∞|ρn+1 − ρn| = 0;
- (iv)
{rn}⊂[e, f]⊂(0,2ζ) and limn→∞|rn+1 − rn| = 0.
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.