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A general composite iterative method for generalized mixed equilibrium problems, variational inequality problems and optimization problems
Journal of Inequalities and Applications volume 2011, Article number: 51 (2011)
Abstract
In this article, we introduce a new general composite iterative scheme for finding a common element of the set of solutions of a generalized mixed equilibrium problem, the set of fixed points of an infinite family of nonexpansive mappings and the set of solutions of a variational inequality problem for an inverse-strongly monotone mapping in Hilbert spaces. It is shown that the sequence generated by the proposed iterative scheme converges strongly to a common element of the above three sets under suitable control conditions, which solves a certain optimization problem. The results of this article substantially improve, develop, and complement the previous well-known results in this area.
2010 Mathematics Subject Classifications: 49J30; 49J40; 47H09; 47H10; 47J20; 47J25; 47J05; 49M05.
1 Introduction
Let H be a real Hilbert space with inner product 〈·, ·〉 and induced norm || · ||. Let C be a nonempty closed convex subset of H and S : C → C be a self-mapping on C. Let us denote by F(S) the set of fixed points of S and by P C the metric projection of H onto C.
Let B : C → H be a nonlinear mapping and φ : C → ℝ be a function, and Θ be a bifunction of C × C into ℝ, where ℝ is the set of real numbers.
Then, we consider the following generalized mixed equilibrium problem of finding x ∈ C such that
which was recently introduced by Peng and Yao [1]. The set of solutions of the problem (1.1) is denoted by GMEP(Θ, φ, B). Here, some special cases of the problem (1.1) are stated as follows:
If B = 0, then the problem (1.1) reduces the following mixed equilibrium problem of finding x ∈ C such that
which was studied by Ceng and Yao [2] (see also [3]). The set of solutions of the problem (1.2) is denoted by MEP (Θ,φ).
If φ = 0 and B = 0, then the problem (1.1) reduces the following equilibrium problem of finding x ∈ C such that
The set of solutions of the problem (1.3) is denoted by EP(Θ).
If φ = 0 and Θ(x, y) = 0 for all x, y ∈ C, then the problem (1.1) reduces the following variational inequality problem of finding x ∈ C such that
The set of solutions of the problem (1.4) is denoted by V I(C, B).
The problem (1.1) is very general in the sense that it includes, as special cases, fixed point problems, optimization problems, variational inequality problems, minmax problems, Nash equilibrium problems in noncooperative games, and others; see [2, 4–6].
Recently, in order to study the problem (1.3) coupled with the fixed point problem, many authors have introduced some iterative schemes for finding a common element of the set of the solutions of the problem (1.3) and the set of fixed points of a countable family of nonexpansive mappings; see [7–16] and the references therein.
In 2008, Su et al. [17] gave an iterative scheme for the problem (1.3), the problem (1.4) for an inverse-strongly monotone mapping, and fixed point problems of non-expansive mappings. In 2009, Yao et al. [18] considered an iterative scheme for the problem (1.2), the problem (1.4) for a Lipschitz and relaxed-cocoercive mapping and fixed point problems of nonexpansive mappings, and in 2008, Peng and Yao [1] studied an iterative scheme for the problem (1.1), the problem (1.4) for a monotone, and Lipschitz continuous mapping and fixed point problems of nonexpansive mappings.
In particular, in 2010, Jung [9] introduced the following new composite iterative scheme for finding a common element of the set of solutions of the problem (1.3) and the set of fixed points of a nonexpansive mapping: x1 ∈ C and
where T is a nonexpansive mapping, f is a contraction with constant k ∈ (0, 1), {α n }, {β n }⊂ [0, 1], and {r n } ⊂ (0, ∞). He showed that the sequences {x n } and {u n } generated by (1.5) converge strongly to a point in F(T ) ∩ EP (Θ) under suitable conditions.
On the other hand, the following optimization problem has been studied extensively by many authors:
where are infinitely many closed convex subsets of H such that u ∈ H, μ ≥ 0 is a real number, A is a strongly positive bounded linear operator on H (i.e., there is a constant such that , ∀x ∈ H) and h is a potential function for γ f (i.e., h'(x) = γ f(x) for all x ∈ H). For this kind of optimization problems, see, for example, Bauschke and Borwein [19], Combettes [20], Deutsch and Yamada [21], Jung [22], and Xu [23] when ; and h(x) = 〈x, b〉 for a given point b in H.
In 2009, Yao et al. [3] considered the following iterative scheme for the problem (1.2) and optimization problems:
where u ∈ H; {α n } and {β n } are two sequences in (0,1), μ > 0, r > 0, γ > 0; K'(x) is the Fréchet derivative of a functional K : H → ℝ at x; and W n is the so-called W-mapping related to a sequence {T n } of nonexpansive mappings. They showed that under appropriate conditions, the sequences {x n } and {y n } generated by (1.6) converge strongly to a solution of the optimization problem:
In 2010, using the method of Yao et al. [3], Jaiboon and Kumam [24] also introduced a general iterative method for finding a common element of the set of solutions of the problem (1.2), the set of fixed points of a sequence {T n } of nonexpansive mappings, and the set of solutions of the problem (1.4) for a α-inverse-strongly monotone mapping. We point out that in the main results of [3, 24], the condition of the sequentially continuity from the weak topology to the strong topology for the derivative K' of the function K : C → ℝ is very strong. Even if , then K' (x) = x is not sequentially continuous from the weak topology to the strong topology.
In this article, inspired and motivated by above mentioned results, we introduce a new iterative method for finding a common element of the set of solutions of a generalized mixed equilibrium problem (1.1), the set of fixed points of a countable family of nonexpansive mappings, and the set of solutions of the variational inequality problem (1.4) for an inverse-strongly monotone mapping in a Hilbert space. We show that under suitable conditions, the sequence generated by the proposed iterative scheme converges strongly to a common element of the above three sets, which is a solution of a certain optimization problem. The results of this article can be viewed as an improvement and complement of the recent results in this direction.
2 Preliminaries and lemmas
Let H be a real Hilbert space, and let C be a nonempty closed convex subset of H. In the following, we write x n ⇀ x to indicate that the sequence {x n } converges weakly to x. x n → x implies that {x n } converges strongly to x.
First, we know that a mapping f : C → C is a contraction on C if there exists a constant k ∈ (0, 1) such that ||f(x) - f(y)|| ≤ k||x - y||, x, y ∈ C. A mapping T : C → C is called nonexpansive if ||Tx - Ty|| ≤ ||x - y||, x, y ∈ C.
In a real Hilbert space H, we have
for all x, y ∈ H and λ ∈ ℝ. For every point x ∈ H, there exists the unique nearest point in C, denoted by P C (x), such that
for all y ∈ C. P C is called the metric projection of H onto C. It is well known that P C is nonexpansive and P C satisfies
for every x, y ∈ H. Moreover, P C (x) is characterized by the properties:
and
In the context of the variational inequality problem for a nonlinear mapping F, this implies that
It is also well known that H satisfies the Opial condition, that is, for any sequence {x n } with x n ⇀ x, the inequality
holds for every y ∈ H with y ≠ x.
A mapping F of C into H is called α-inverse-strongly monotone if there exists a constant α > 0 such that
We know that if F = I - T, where T is a nonexpansive mapping of C into itself and I is the identity mapping of H, then F is -inverse-strongly monotone and V I (C, F ) = F(T ). A mapping F of C into H is called strongly monotone if there exists a positive real number η such that
In such a case, we say F is η-strongly monotone. If F is η-strongly monotone and κ-Lipschitz continuous, that is, ||Fx - Fy|| ≤ κ||x - y|| for all x, y ∈ C, then F is -inverse-strongly monotone. If F is an α-inverse-strongly monotone mapping of C into H, then it is obvious that F is -Lipschitz continuous. We also have that for all x, y ∈ C and λ > 0,
Hence, if λ ≤ 2α, then I - λF is a nonexpansive mapping of C into H. The following result for the existence of solutions of the variational inequality problem for inverse-strongly monotone mappings was given in Takahashi and Toyoda [25].
Proposition Let C be a bounded closed convex subset of a real Hilbert space, and F be an α-inverse-strongly monotone mapping of C into H. Then, V I(C, F) is nonempty.
A set-valued mapping Q : H → 2 H is called monotone if for all x, y ∈ H, f ∈ Qx and g ∈ Qy imply 〈x - y, f - g 〉 ≥ 0. A monotone mapping Q : H → 2 H is maximal if the graph G(Q) of Q is not properly contained in the graph of any other monotone mapping. It is known that a monotone mapping Q is maximal if and only if for (x, f ) ∈ H × H, 〈x - y, f - g〉 ≥ 0 for every (y, g) ∈ G(Q) implies f ∈ Qx. Let F be an inverse-strongly monotone mapping of C into H, and let N C v be the normal cone to C at v, that is, N C v = {w ∈ H : 〈v - u, w〉 ≥ 0, for all u ∈ C}, and define
Then, Q is maximal monotone and 0 ∈ Qv if and only if v ∈ V I(C, F ); see [26, 27].
For solving the equilibrium problem for a bifunction Θ : C × C → ℝ, let us assume that Θ and φ satisfy the following conditions:
(A1) Θ(x, x) = 0 for all x ∈ C;
(A2) Θ is monotone, that is, Θ(x, y) + Θ (y, x) ≤ 0 for all x, y ∈ C;
(A3) for each x, y, z ∈ C,
(A4) for each x ∈ C, y α Θ (x, y) is convex and lower semicontinuous;
(A5) For each y ∈ C, x α Θ (x, y) is weakly upper semicontinuous;
(B1) For each x ∈ H and r > 0, there exists a bounded subset D x ⊆ C and y x ∈ C such that for any z ∈ C \D x ,
(B2) C is a bounded set;
The following lemmas were given in [1, 4].
Lemma 2.1 ([4]) Let C be a nonempty closed convex subset of H, and Θ be a bifunction of C × C into ℝ satisfying (A1)-(A4). Let r > 0 and x ∈ H. Then, there exists z ∈ C such that
Lemma 2.2 ([1]) Let C be a nonempty closed convex subset of H. Let Θ be a bifunction form C × C to ℝ satisfying (A1)-(A5) and φ : C → ℝ be a proper lower semicontinuous and convex function. For r > 0 and x ∈ H, define a mapping S r : H → C as follows:
for all z ∈ H. Assume that either (B1) or (B2) holds. Then, the following hold:
-
(1)
For each x ∈ H, S r (x) ≠ ∅;
-
(2)
S r is single-valued;
-
(3)
S r is firmly nonexpansive, that is, for any x, y ∈ H,
-
(4)
F(S r ) = MEP (Θ, φ);
-
(5)
MEP (Θ, φ) is closed and convex.
We also need the following lemmas for the proof of our main results.
Lemma 2.3 ([23]) Let {s n } be a sequence of non-negative real numbers satisfying
where {λ n } and {β n } satisfy the following conditions:
-
(i)
{λ n } ⊂ [0, 1] and or, equivalently, ,
-
(ii)
or ,
Then, limn→∞s n = 0.
Lemma 2.4 In a Hilbert space, there holds the inequality
Lemma 2.5 (Aoyama et al. [28]) Let C be a nonempty closed convex subset of H and {T n } be a sequence of nonexpansive mappings of C into itself. Suppose that
Then, for each y ∈ C, {T n y} converges strongly to some point of C. Moreover, let T be a mapping of C into itself defined by Ty = limn→∞T n y for all y ∈ C. Then, limn→∞sup{||Tz - T n z|| : z ∈ C} = 0.
The following lemma can be found in [3](see also Lemma 2.1 in [22]).
Lemma 2.6 Let C be a nonempty closed convex subset of a real Hilbert space H and g : C → ℝ ∪{∞} be a proper lower semicontinuous differentiable convex function. If x* is a solution to the minimization problem
then
In particular, if x* solves the optimization problem
then
where h is a potential function for γ f.
3 Main results
In this section, we introduce a new composite iterative scheme for finding a common point of the set of solutions of the problem (1.1), the set of fixed points of a countable family of nonexpansive mappings, and the set of solutions of the problem (1.4) for an inverse-strongly monotone mapping.
Theorem 3.1 Let C be a nonempty closed convex subset of a real Hilbert space H such that C ± C ⊂ C. Let Θ be a bifunction from C × C to ℝ satisfying (A1)-(A5) and φ : C → ℝ be a lower semicontinuous and convex function. Let F, B be two α, β-inverse-strongly monotone mappings of C into H, respectively. Let {T n } be a sequence of nonexpansive mappings of C into itself such that. Let μ > 0 and γ > 0 be real numbers. Let f be a contraction of C into itself with constant k ∈ (0, 1) and A be a strongly positive bounded linear operator on C with constant such that . Assume that either (B1) or (B2) holds. Let u ∈ C, and let {x n },{y n }, and {u n } be sequences generated by x1 ∈ C and
where {α n }, {β n } ⊂ [0, 1], λ n ∈ [a, b] ⊂ (0, 2α) and r n ∈ [c, d] ⊂ (0, 2β). Let {α n }, {λ n } and {β n } satisfy the following conditions:
(C1) α n → 0 (n → ∞);;
(C2) β n ⊂ [0, a) for all n ≥ 0 and for some a ∈ (0, 1);
(C3) , , , .
Suppose that for any bounded subset D of C. Let T be a mapping of C into itself defined by Tz = limn→∞T n z for all z ∈ C and suppose that. Then {x n } and {u n } converge strongly to q ∈ Ω1, which is a solution of the optimization problem:
where h is a potential function for γ f.
Proof First, from α n → 0 (n → ∞) in the condition (C1), we assume, without loss of generality, that α n ≤ (1 + μ||A||)-1 and for n ≥ 1. We know that if A is bounded linear self-adjoint operator on H, then
Observe that
which is to say I - α n (I + μA) is positive. It follows that
Let us divide the proof into several steps. From now on, we put z n = P C (u n -λ n Fu n ) and w n = P C (y n - λ n Fy n ).
Step 1: We show that {x n } is bounded. To this end, let and be a sequence of mappings defined as in Lemma 2.2. Then and p = P C (p - λ n Fp) from (2.2). From z n = P C (u n - λ n Fu n ) and the fact that P C and I - λ n F are nonexpansive, it follows that
Also, by and the β-inverse-strongly monotonicity of B, we have with r n ∈ (0, 2β),
that is, ||u n - p|| ≤ ||x n - p||, and so
Similarly, we have
Now, set . Then, from (IS) and (3.1), we obtain
From (3.2) and (3.3), it follows that
By induction, it follows from (3.4) that
Therefore, {x n } is bounded. Hence {u n }, {y n }, {z n }, {w n }, {f(x n )}, {Fu n }, {Fy n }, and are bounded. Moreover, since ||T n z n - p|| ≤ ||x n - p|| and ||T n w n - p|| ≤ ||y n - p||, {T n z n } and {T n w n } are also bounded, and since α n → 0 in the condition (C1), we have
Step 2: We show that limn→∞||xn+1- x n || = 0. Indeed, since I - λ n F and P C are nonexpansive, we have
Similarly, we get
On the other hand, from and , it follows that
and
Substituting y = u n into (3.8) and y = un - 1into (3.9), we obtain
and
From (A2), we have
and then
Hence, it follows that
Without loss of generality, let us assume that there exists a real number c such that r n > c > 0 for all n ≥ 1. Then, by (3.10) and the fact that (I - rn-1B) is nonexpansive, we have
which implies that
where M1 = sup {||u n - x n || : n ≥ 1}. Substituting (3.11) into (3.6), we have
Simple calculations show that
Hence, by (3.11) and (3.12), we obtain
where D1 is a bounded subset of C containing {z n }. Also observe that
By (3.7), (3.13) and (3.14), we have
where D2 is a bounded subset of C containing {z n } and {w n }, , M3 = sup{||Fy n || + ||Fu n || : n ≥ 1}, and M4 = sup{||T n w n || + ||y n || : n ≥ 1}. From the conditions (C1) and (C3) and the condition for any bounded subset D of C, it is easy to see that
and
Applying Lemma 2.3 to (3.15), we obtain
Moreover, from (3.11), it follows that
From (3.12) and (3.13), we also have
Step 3: We show that limn-∞||x n - u n || = 0. To this end, let p ∈ Ω1. Since is firmly nonexpansive and , we have
Hence,
On the other hand, since z n = P C (u n - λ n Fu n ), we get
From (3.16), (3.17), and the convexity of || ||2, we obtain
On the another hand, we note that
Using the convexity of || ||2, (3.2), (3.18), and (3.19), we have
Hence, we have
From the condition (C1), {r n } ⊂ [c, d] ⊂ (0, 2β) and Step 2, it follows that
Also, by (3.16) and (3.20), we have
and so
where . Since ||xn+1- x n || → 0, α n → 0, and ||Bx n - Bp|| → 0 as n → ∞, we obtain
Moreover, since lim infn→∞r n > 0, we also have
Step 4: We show that limn→∞||x n - T n z n || = 0 and limn-∞||x n - y n || = 0. Indeed, since z n = P C (u n - λ n Fu n ) and w n = P C (y n - λ n Fy n ), we obtain with the condition (C2)
which implies that
Thus, from (3.5), Step 2, and Step 3, we have
Also we have
Since limn→∞||y n - T n z n || = 0 by (3.5) in Step 1, we obtain
Step 5: We show that limn→∞||T n z n - z n || = 0. Let p ∈ Ω1. Using the convexity of || ||2, we compute
Using (3.24), we obtain
Hence, we have
where . From the condition (C1), λ n ∈ [a, b] ⊂ (0, 2α), and Step 2, it follows that
On the other hand, using z n = P C (u n - λ n Fu n ) and (2.1), we observe that
and so
Thus, from (3.26), we have
Hence, we obtain
which implies that
From ||xn+1- x n || → 0, α n → 0 and ||Fu n - Fp|| → 0 as n → ∞, it follows that
Since ||T n z n - z n || ≤ ||T n z n - x n || + ||x n - u n || + ||u n - z n ||, from Step 3, Step 4, and (3.28), we conclude that
We notice that by the assumption on T, (3.29) and Lemma 2.5,
Step 6: We show that
where q is a solution of the optimization problem (OP1). To this end, first we prove that
Since {z n } is bounded, we can choose a subsequence of {z n } such that
Without loss of generality, we may assume that converges weakly to z ∈ C. From ||Tz n - z n || → 0 in (3.30), it follows that .
Now, we will show that z ∈ Ω1. First, we show that. Assume that z ∉ F(T). Since and Tz ≠ z, by the Opial condition, we obtain
which is a contradiction. Thus we have .
Next, we prove that z ∈ V I(C, F ). Let
where N C v is normal cone to C at v. We already know that, in this case, the mapping Q is maximal monotone, and 0 ∈ Qv if and only if v ∈ V I(C, F). Let (v, w) ∈ G(Q). Since w - Fv ∈ N C v and z n ∈ C, we have
On the other hand, from z n = P C (u n - λ n Fu n ), we have
that is,
Thus, we obtain
Since ||z n - u n || → 0 in (3.28) and F is α-inverse-strongly monotone, it follows from (3.32) that
Since Q is maximal monotone, we have z ∈ Q−10 and hence z ∈ V I(C, F).
Finally, we show that z ∈ GMEP(Θ, φ, B). By ; we know that
It follows from (A2) that
Hence
For t with 0 < t ≤ 1 and y ∈ C, let y t = ty + (1 - t)z. Since y ∈ C and z ∈ C, we have y t ∈ C and hence Θ(y t, z) ≤ 0. Hence, from (3.33), we have
Since from Step 3, we have and . Also by in (3.28), we have . Moreover, from the inverse-strongly monotonicity of B, we have . Hence, from (A4) and the weak lower semicontinuity of φ, if follows that
From (A1), (A4), and (3.34), we also obtain
and hence
Letting t → 0 in (3.35), we have for each y ∈ C
This implies that z ∈ GMEP(Θ, φ, B). Therefore z ∈ Ω1.
Now, from Lemma 2.6 and (3.31), we obtain
Since limn→∞||x n - Tz n || ≤ ||x n - T n z n || + ||T n z n - Tz n || → 0, from Step 4 and Lemma 2.5, and from (3.36), we conclude that
Step 7: We show that limn→∞||x n - q|| = 0 and limn→∞||u n - q|| = 0, where q is a solution of the optimization problem (OP1). Indeed, from (IS) and Lemma 2.4, we have
that is,
where and
From the condition (C1), ||y n - x n || → 0 in Step 4, and Step 6, it is easily seen that , , and . Hence, by Lemma 2.3, we conclude x n → q as n → ∞. Moreover, from Step 3, we obtain that u n → q as n → ∞. This completes the proof. □
Corollary 3.2 Let H, C, Θ, φ, F, A, f, μ, γ, and k be as in Theorem 3.1. Let {T n } be a sequence of nonexpansive mappings of C into itself such that. Assume that either (B1) or (B2) holds. Let u ∈ C and let {x n } and {u n } be sequences generated by x1 ∈ C and
where {α n }, {β n } ⊂ [0, 1], λ n ∈ [a, b] ⊂ (0, 2α), and r n ∈ [c, d] ⊂ (0, 2β). Let {α n }, {β n }, {λ n }, and {r n } satisfy the conditions (C1), (C2), and (C3) in Theorem 3.1. Suppose thatfor any bounded subset D of C. Let T be a mapping of C into itself defined by Tz = limn→∞T n z for all z ∈ C and suppose that. Then {x n } and {u n } converge strongly to q ∈ Ω2, which is a solution of the optimization problem:
where h is a potential function for γf.
Proof Putting B = 0 in Theorem 3.1, we obtain the required result. □
As direct consequences of Theorem 3.1, we can also obtain the following new strong convergence theorems for the problem (1.1) and fixed point problem.
Corollary 3.3 Let H, C, Θ, φ, B, A, f, μ, γ, , and k be as in Theorem 3.1. Let T be a nonexpansive mapping of C into itself such that Ω3 := F(T) ∩ GMEP(Θ, φ, B) ≠ Ø. Assume that either (B1) or (B2) holds. Let u ∈ C and let {x n } and {u n } be sequences generated by x1 ∈ C and
where {α n } ⊂ [0, 1], and r n ∈ [c, d] ⊂ (0; 2β). Let {α n } and {r n } satisfy the following conditions:
-
(i)
α n → 0 (n → ∞); ;
-
(ii)
;
Then {x n } and {u n } converge strongly to q ∈ Ω3, which is a solution of the optimization problem:
where h is a potential function for γf.
Proof Putting F = 0, β n = 0, and T n = T for n ≥ 1 in Theorem 3.1, we obtain the required result. □
A mapping S : C → C is called strictly pseudocontractive if there exists r with 0 ≤ r < 1 such that
Such a mapping S is called r-strictly pseudocontractive. Putting F = I - S, we know that
see, for instance, [12, 29]. Hence, we have the following result.
Corollary 3.4 Let H, C, Θ, φ, B, A, f, μ, γ, , and k be as in Theorem 3.1. Let S be a r-strictly pseudocontractive mappings of C into itself. Let T be a nonexpansive mapping of C into itself such that Ω4 := F(T) ∩ F(S) ∩ GMEP(Θ,φ, B) ≠ Ø. Assume that either (B1) or (B2) holds. Let u ∈ C and let {x n } and {u n } be sequences generated by x1 ∈ C and
where {α n },{λ n } ⊂ [0, 1], λ n ∈ [a, b] ⊂ (0, 1 -r), and r n ∈ [c, d] ⊂ (0, 2β). Let {α n }, {λ n }, and {r n } satisfy the following conditions:
-
(i)
α n → 0 (n → ∞); ;
-
(ii)
; ; .
Then {x n } and {u n } converge strongly to q ∈ Ω4, which is a solution of the optimization problem
where h is a potential function for γf.
Proof Putting F = I - S, β n = 0, and T n = T for n ≥ 1, from Theorem 3.1, we obtain the required result. □
Remark 3.1
-
(1)
Theorem 3.1, Corollary 3.2, Corollary 3.3, and Corollary 3.4 improve and develop the corresponding results, which were obtained recently by many authors in various references, for example, see [2, 3, 7–9, 11–18, 24, 30]. In particular, we mention that the iterative scheme (3.1) in Theorem 3.1 of [24] is not well defined without the assumption C ± C ⊂ C on a nonempty closed convex subset C of H.
-
(2)
The condition imposed on {α n } can be replaced by the perturbed control condition |α n+1- α n | < o(α n+1) + σ n , or the condition α n ∈ (0, 1], n ≥ 1, and limn→∞ α n /α n+1 = 1.
-
(3)
Some special cases of the generalized mixed equilibrium problem (1.1) are known as follows:
-
(i)
If φ = 0, then the problem (1.1) reduced the following generalized equilibrium problem (GEP ) of finding x ∈ C such that
(3.31a)
which was studied by Takahashi and Takahashi [30].
-
(ii)
If Θ(x, y) = 0 for all x, y ∈ C, then the problem (1.1) reduces the following generalized variational inequality problem (GVI) of finding x ∈ C such that
(3.32a) -
(iii)
If B = 0 and Θ(x, y) = 0 for all x, y ∈ C, then the problem (1.1) reduces the following minimization problem of finding x ∈ C such that
(3.33a)
Applying Theorem 3.1 together with the one in the following assumptions instead of (B1), we can also establish the new corresponding results for the above mentioned problems:
(B2) C is a bounded set;
(B3) For each x ∈ H and r > 0, there exist a bounded subset D x ⊆ C and
y x ∈ C such that for any z ∈ C\D x ,
(B4) For each x ∈ H and r > 0, there exist a bounded subset D x ⊆ C and y x ∈ C such that for any z ∈ C\D x ,
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Acknowledgements
The author would like to thank the anonymous referees for their valuable comments and suggestions, which improved the presentation of this manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0017007).
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Jung, J.S. A general composite iterative method for generalized mixed equilibrium problems, variational inequality problems and optimization problems. J Inequal Appl 2011, 51 (2011). https://doi.org/10.1186/1029-242X-2011-51
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DOI: https://doi.org/10.1186/1029-242X-2011-51