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A new hybrid general iterative algorithm for common solutions of generalized mixed equilibrium problems and variational inclusions
Journal of Inequalities and Applications volume 2012, Article number: 138 (2012)
Abstract
In this article, we introduce a new general iterative method for finding a common element of the set of solutions generalized for mixed equilibrium problems, the set of solution for fixed point for nonexpansive mappings and the set of solutions for the variational inclusions for β1, β2-inverse-strongly monotone mappings in a real Hilbert space. We prove that the sequence converges strongly to a common element of the above sets under some suitable conditions. Our results improve and extend the corresponding results of Marino and Xu, Su et al., Tan and Chang and some authors.
Mathematics subject Classification: 46C05; 47H09; 47H10.
1. Introduction
Let C be a closed convex subset of a real Hilbert space H with the inner product 〈·, ·〉 and the norm || · ||. Let F be a bifunction of C × C into , where
is the set of real numbers, Ψ : C → H be a mapping and be a real-valued function. The generalized mixed equilibrium problem for finding x ∈ C such that
The set of solutions of (1.1) is denoted by GMEP(F, φ, Ψ), that is
If F ≡ 0, the problem (1.1) is reduced into the mixed variational inequality of Browder type[1] for finding x ∈ C such that
The set of solutions of (1.2) is denoted by MVI(C, φ, Ψ).
If Ψ ≡ 0, the problem (1.1) is reduced into the mixed equilibrium problem for finding x ∈ C such that
The set of solutions of (1.3) is denoted by MEP(F, φ).
If φ ≡ 0, the problem (1.3) is reduced into the equilibrium problem[2] for finding x ∈ C such that
The set of solutions of (1.4) is denoted by EP(F). See, e.g. [3–6] and the references therein.
If F ≡ 0 and φ ≡ 0, the problem (1.1) is reduced into the Hartmann-Stampacchia variational inequality[7] for finding x ∈ C such that
The set of solutions of (1.5) is denoted by VI(C, Ψ).
If F ≡ 0 and Ψ ≡ 0, the problem (1.1) is reduced into the minimize problem for finding x ∈ C such that
The set of solutions of (1.6) is denoted by Argmin(φ).
Iterative methods for nonexpansive mappings have recently been applied to solve convex minimization problems. Convex minimization problems have a great impact and influence in the development of almost all branches of pure and applied sciences. A typical problem is to minimize a quadratic function over the set of the fixed points of a nonexpansive mapping on a real Hilbert space H:
where A is a linear bounded operator, F(S) is the fixed point set of a nonexpansive mapping S and y is a given point in H[8].
Recall, a mapping S : C → C is said to be nonexpansive if
If C is bounded closed convex and S is a nonexpansive mapping of C into itself, then F(S) is nonempty [9]. A mapping S : C → C is said to be a k-strictly pseudo-contraction[10] if there exists 0 ≤ k < 1 such that
where I denotes the identity operator on C. A mapping A of C into H is called monotone if
A mapping A of C into H is called an α-inverse-strongly monotone if there exists a positive real number α such that
A mapping A of C into H is called α-strongly monotone if there exists a positive real number α such that
A linear bounded operator A is called strongly positive if there exists a constant with the property
A self mapping f : C → C is called contraction on C if there exists a constant α ∈ (0, 1) such that
Let B : H → H be a single-valued nonlinear mapping and M : H → 2Hbe a set-valued mapping. The variational inclusion problem is to find x ∈ H such that
where θ is the zero vector in H. The set of solutions of problem (1.8) is denoted by I(B, M). The variational inclusion has been extensively studied in the literature. See, e.g. [11–15] and the reference therein.
A set-valued mapping M : H → 2His called monotone if for all x, y ∈ H, f ∈ M(x) and g ∈ M(y) imply 〈x - y, f - g〉 ≥ 0. A monotone mapping M is maximal if its graph G(M) := {(f, x) ∈ H × H : f ∈ M(x)} of M is not properly contained in the graph of any other monotone mapping. It is known that a monotone mapping M is maximal if and only if for (x, f) ∈ H × H, 〈x - y, f - g〉 > 0 for all (y, g) ∈ G(M) imply f ∈ M(x).
Let B be an inverse-strongly monotone mapping of C into H and let N C v be normal cone to C at v ∈ C, i.e., N C v = {w ∈ H : 〈v - u, w〉 ≥ 0, ∀u ∈ C}, and define
Then M is a maximal monotone and θ ∈ Mν if and only if v ∈ VI(C, B) [16].
Let M : H → 2Hbe a set-valued maximal monotone mapping, then the single-valued mapping JM,λ: H → H defined by
is called the resolvent operator associated with M, where λ is any positive number and I is the identity mapping. In the worth mentioning that the resolvent operator is nonexpansive, 1-inverse-strongly monotone and that a solution of problem (1.8) is a fixed point of the operator JM,λ(I - λB) for all λ > 0 (see also [17]).
In 2000, Moudafi [18] introduced the viscosity approximation method for nonexpansive mapping and proved that if H is a real Hilbert space, the sequence {x n } defined by the iterative method below, with the initial guess x0 ∈ C is chosen arbitrarily,
where {α n } ⊂ (0, 1) satisfies certain conditions, converge strongly to a fixed point of S (say ) which is the unique solution of the following variational inequality.
In 2005, Iiduka and Takahashi [19] introduced following iterative process x0 ∈ C,
where u ∈ C, {α n } ⊂ (0, 1) and {λ n } ⊂ [a, b] for some a, b with 0 < a < b < 2β. They proved that under certain appropriate conditions imposed on {α n } and {λ n }, the sequence {x n } converges strongly to a common element of the set of fixed points of nonexpansive mapping and the set of solutions of the variational inequality for an inverse-strongly monotone mapping (say ) which solve some variational inequality.
In 2006, Marino and Xu [8] introduced a general iterative method for nonexpansive mapping. They defined the sequence {x n } generated by the algorithm x0 ∈ C,
where {α n } ⊂ (0, 1) and A is a strongly positive linear bounded operator. They proved that if C = H then the sequence {x n } converges strongly to a fixed point of S (say ) which is the unique solution of the following variational inequality.
In 2008, Su et al. [20] introduced the following iterative scheme by the viscosity approximation method in a real Hilbert space: x1, u n ∈ H
for all n ∈ ℕ, where {α n } ⊂ [0, 1) and {r n } ⊂ (0, ∞) satisfy some appropriate conditions. Furthermore, they proved {x n } and {u n } converge strongly to the same point z, where z = PF(S)∩VI(C,A)∩ EP(F)f(z).
In 2011, Tan and Chang [14] introduced following iterative process for {T n : C → C} be a sequence of nonexpansive mappings. Let {x n } be the sequence defined by
where {α n } ⊂ (0, 1), λ ∈ (0, 2α] and μ ∈ (0, 2β]. The sequence {x n } converges strongly to a common element of the set of fixed points of nonexpansive mapping, the set of solutions of the variational inequality and the generalized equilibrium problem.
In this article, we modify by Marino and Xu [8], Su et al. [20] and Tan and Chang [14], the purpose of this article, we show that under some control conditions the sequence {x n } converges strongly to a common element of the set of fixed points of nonexpansive mappings, the solution of the generalized mixed equilibrium problems and the solution of the variational inclusions in a real Hilbert space.
2. Preliminaries
Let H be a real Hilbert space and C be a nonempty closed convex subset of H. We denote weak convergence and strong convergence by notations ⇀ and →, respectively. Recall that the metric (nearest point) projection P C from H onto C assigns to each x ∈ H, the unique point in P C x ∈ C satisfying the property
The following characterizes the projection P C . We recall some lemmas which will be needed in the rest of this article.
Lemma 2.1. The function u ∈ C is a solution of the variational inequality (1.5) if and only if u ∈ C satisfies the relation u = P C (u - λ Ψu) for all λ > 0.
Lemma 2.2. For a given z ∈ H, u ∈ C, u = P C z ⇔ 〈u - z, v - u〉 ≥ 0, ∀v ∈ C.
It is well known that P C is a firmly nonexpansive mapping of H onto C and satisfies
Moreover, P C x is characterized by the following properties: P C x ∈ C and for all x ∈ H, y ∈ C,
Lemma 2.3. [21]Let M : H → 2Hbe a maximal monotone mapping and let B : H → H be a monotone and Lipshitz continuous mapping. Then the mapping L = M + B : H → 2His a maximal monotone mapping.
Lemma 2.4. [22]Each Hilbert space H satisfies Opial's condition, that is, for any sequence {x n } ⊂ H with x n ⇀ x, the inequality lim infn→∞||x n - x|| < lim infn→∞||x n - y||, hold for each y ∈ H with y ≠ x.
Lemma 2.5. [23]Assume {a n } is a sequence of nonnegative real numbers such that
where {γ
n
} ⊂ (0, 1) and {δ
n
} is a sequence insuch that
-
(i)
.
-
(ii)
or .
Then limn→∞a n = 0.
Lemma 2.6. [24]Let C be a closed convex subset of a real Hilbert space H and let S : C → C be a nonexpansive mapping. Then I - S is demiclosed at zero, that is,
implies x = Sx.
For solving the mixed equilibrium problem, let us assume that the bifunction and satisfies the following conditions:
(A1) F(x, x) = 0 for all x ∈ C;
(A2) F is monotone, i.e., F(x, y) + F(y, x) ≤ 0 for any x, y ∈ C;
(A3) for each fixed y ∈ C, x ↦ F(x, y) is weakly upper semicontinuous;
(A4) for each fixed x ∈ C, y ↦ F(x, y) is convex and lower semicontinuous;
(B1) for each x ∈ C and r > 0, there exist a bounded subset D x ⊆ C and y x ∈ C such that for any z ∈ C \ D x ,
(B2) C is a bounded set.
Lemma 2.7. [25]Let C be a nonempty closed convex subset of a real Hilbert space H. Letbe a bifunction mapping satisfies (A1)-(A4) and letis convex and lower semicontinuous such that. Assume that either (B1) or (B2) holds. For r > 0 and x ∈ H, then there exists z ∈ C such that
Define a mapping as follows:
for all x ∈ H. Then, the following hold:
-
(i)
is single-valued;
-
(ii)
is firmly nonexpansive, i.e., for any x, y ∈ H,
-
(iii)
;
-
(iv)
MEP(F, φ) is closed and convex.
Lemma 2.8. [8]Assume A is a strongly positive linear bounded operator on a Hilbert space H with coefficientand 0 < ρ ≤ ||A||-1, then.
Lemma 2.9. [26]Let H be a real Hilbert space and A : H → H a mapping.
-
(i)
If A is a δ-strongly monotone and μ-strictly pseudo-contraction with δ + μ > 1, then I - A is a contraction with constant .
-
(ii)
If A is a δ-strongly monotone and μ-strictly pseudo-contraction with δ + μ > 1, then for any fixed number τ ∈ (0, 1), I - τA is a contraction with constant .
3. Strong convergence theorems
In this section, we show a strong convergence theorem which solves the problem of finding a common element of F(S), GMEP(F1, φ1, B1), GMEP(F2, φ2, B2), I(A1, M1) and I(A2, M2).
Theorem 3.1. Let H be a real Hilbert space, C be a closed convex subset of H. Let F1, F2be bifunctions of C × C intosatisfying (A1)-(A4) and A1, A2, B1, B2 : C → H be β1, β2, η, ρ-inverse-strongly monotone mappings, be convex and lower semicontinuous functions, f : C → C be a contraction with coefficient α (0 < α < 1), M1, M2 : H → 2Hbe maximal monotone mappings and A is a δ-strongly monotone and μ-strictly pseudo-contraction with δ + μ > 1, γ is a positive real number such that. Assume that either (B1) or (B2) holds. Let S be a nonexpansive mapping of C into itself such that
Suppose {x n } is a sequences generated by the following algorithm x0 ∈ C arbitrarily:
where {α n }, {ξ n } ⊂ (0, 1), λ1 ∈ (0, 2β1) such that 0 < a1 ≤ λ1 < b1 < 2β1, λ2 ∈ (0, 2β2) such that 0 < a1 ≤ λ2 ≤ b2 < 2β2, r n ∈ (0, 2η) with 0 < c ≤ d ≤ 1 - η and s n ∈ (0, 2ρ) with 0 < e ≤ f ≤ 1 - ρ satisfy the following conditions:
(C1): limn→∞α n = 0, ,
(C2): 0 < lim infn→∞ξ n < lim supn→∞ξ n < 1, ,
(C3): lim infn→∞r n > 0 and limn→∞|rn+1- r n | = 0,
(C4): lim infn→∞s n > 0 and limn→∞|sn+1- s n | = 0.
Then {x n } converges strongly to q ∈ Θ, where q = PΘ(γf + I - A)(q) which solves the following variational inequality:
which is the optimality condition for the minimization problem
where h is a potential function for γf (i.e., h'(q) = γf(q) for q ∈ H).
Proof. Since A1, A2 are β1, β2-inverse-strongly monotone mappings, we have
In similar way, we can obtain
And B1, B2 are η, ρ-inverse-strongly monotone mappings, we have
In similar way, we can obtain
It is clear that if 0 < λ1 < 2β1, 0 < λ2 < 2β2, 0 < r n < 2η, 0 < s n ≤ 2ρ then I - λ1A1, I - λ2A2, I - r n B1, I - s n B2 are all nonexpansive. We will divide the proof into six steps.
Step 1. We will show {x n } is bounded. Put for all n ≥ 0 and for all n ≥ 0. It follows that
In similar way, we can obtain
Put . It follows that
By Lemma 2.7, we have for all n ≥ 0. Then, we have
In similar way, we can obtain
Put z n = P C [α n γf(x n ) + (I - α n A)SP C y n ] for all n ≥ 0. From (3.1) and by Lemma 2.9
(ii), we deduce that
It follows from induction that
Therefore {x n } is bounded, so are {y n },{z n },{P C w n },{SP C y n },{f(x n )} and {ASP C y n }.
Step 2. We claim that limn→∞||xn+2- xn+1|| = 0. From (3.1), we have
Since I - λ2A2 be nonexpansive, we have
On the other hand, from and , it follows that
and
Substituting y = v n in (3.11) and y = vn-1in (3.12), we get
and
From (A2), we obtain
and then
so
It follows that
Without loss of generality, let us assume that there exists a real number e such that sn- 1> e > 0, for all n ∈ ℕ. Then, we have
and hence
where M1 = sup{||v n - x n || : n ∈ ℕ}. Substituting (3.13) into (3.9) and (3.10) that
By Lemma 2.9 (ii), it follow that
Since I - λ1A1 be nonexpansive, we have
On the other hand, from and , it follows that
and
Substituting y = u n in (3.17) and y = un-1in (3.18), we get
and
From (A2), we obtain
and then
so
It follows that
Without loss of generality, let us assume that there exists a real number c such that rn- 1> c > 0, for all n∈ℕ. Then, we have
and hence
where M2 = sup{||u n - x n || : n ∈ ℕ}. Substituting (3.19) into (3.16), we have
Substituting (3.20) into (3.15), we obtain that
And substituting (3.10), (3.13), (3.21) into (3.9), we get
where M3 > 0 is a constant satisfying
This together with (C1)-(C4) and Lemma 2.5, imply that
From (3.20) and (C3), we also have ||yn+1- y n || → 0 as n → ∞.
Step 3. We show the followings:
-
(i)
limn→∞||A 1 u n - A 1 q|| = 0;
-
(ii)
limn→∞||A 2 v n - A 2 q|| = 0;
-
(iii)
limn→∞||B 1 x n - B 1 q|| = 0;
-
(iv)
limn→∞||B 2 x n - B 2 q|| = 0.
For q ∈ Θ and , then we get
Using (3.5), it follows that
By the convexity of the norm ||·||, we have
Substituting (3.4), (3.7), (3.24) into (3.25), we obtain
So, we obtain
where . Since conditions (C1), (C2) and limn→∞||xn+1-x n || = 0, then we obtain that ||A1u n - A1q|| → 0 as n → ∞. For q ∈ Θ and , then we get
Substituting (3.24), (3.26) into (3.25), we obtain
So, we obtain
where . Since conditions (C1), (C2), limn→∞||xn+1- x n || = 0 and limn→∞||A1u n - A1q|| → 0 then we obtain that ||A2v n - A2q|| → 0 as n → ∞. We consider this inequality in (3.24) that
Substituting (3.3) and (3.6) into (3.27), we have
Substituting (3.4), (3.7) and (3.28) into (3.25), we obtain