Abstract
In this paper, a generalized variational inequality and fixed points problem is presented. An iterative algorithm is introduced for finding a solution of the generalized variational inequalities and fixed point of two quasi-pseudocontractive operators under a nonlinear transformation. Strong convergence of the suggested algorithm is demonstrated.
MSC:
49J53; 90C25
1. Introduction
Let be a real Hilbert space equipped with an inner product and induced norm , respectively. Let be a closed convex set. For the given two nonlinear operators and , the generalized variational inequality (GVI) aims to find an element such that
We use to denote the solution set of Equation (1).
If , then GVI (1) can be reduced to find an element such that
We use to denote the solution set of Equation (2).
Variational inequalities were introduced by Stampacchia [1] and provide a useful tool for researching a large variety of interesting problems arising in physics, economics, finance, elasticity, optimization, network analysis, medical images, water resources, and structural analysis [2,3,4,5,6,7,8]. For some related work, please refer to References [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27].
Iterative computing fixed points of nonlinear operators is nowadays an active research field [28,29,30,31,32,33,34,35]. The interest in pseudocontractive operators is due mainly to their usefulness as an additional assumption to Lipschitz type conditions in proving convergence of fixed point iterative procedures and their connection with the important class of nonlinear monotone (accretive) operators.
Recall that an operator is called pseudocontractive if
for all .
Iterative algorithms for finding the fixed points of pseudocontractive operators have been studied by many mathematicians, see, for example, References [36,37,38,39,40]. In this article, we focus on a general class of quasi-pseudocontractive operators. Recall that a mapping is called quasi-pseudocontractive if
for all and , where stands for the set of fixed points of S, i.e., .
Now it is well-known that the quasi-pseudocontractive operators include the directed operators and the demicontractive operators as special cases [19]. In this paper, we consider the following generalized variational inequalities and fixed points problems of finding an element such that
where S and T are two quasi-pseudocontractive operators.
In order to solve Equation (5), we introduce a new iterative algorithm. Under some mild restrictions, we will demonstrate the strong convergence analysis of the presented algorithm.
2. Notation and Lemmas
Let be a real Hilbert space. Let be a closed convex set. Recall that an operator is called L-Lipschitz if for all where is a constant.
Definition 1.
An operator is said to be
- Monotone if ,
- Strongly monotone if , , where is a constant.
- α-inverse strongly monotone if , , where is a constant.
- α-inverse strongly φ-monotone if , , where is a nonlinear operator and is a constant.
An operator is called monotone on if and only if for all , , and . A monotone operator R on is called maximal monotone if the graph of R is a maximal monotone set.
We use to denote the nearest point projection from onto , that is, for , , for all . Now it is known that the operator is firmly nonexpansive, that is,
Consequently,
Recall that an operator S is said to be demiclosed if weakly and strongly, implies . We collect several lemmas for our main results in the next section.
Lemma 1
([41]). Let be a real Hilbert space. Let be a closed convex set. Let be an L-Lipschitz quasi-pseudocontractive operator. Then, we have
for all and when .
Lemma 2
([41]). Let be a real Hilbert space. Let be a closed convex set. If the operator is L-Lipschitz with , then we have
where .
Lemma 3
([41]). Let be a nonempty closed convex subset of a real Hilbert space . If the operator is L-Lipschitz with and is demiclosed at 0, then the composition operator is also demiclosed at 0 provided .
Lemma 4
([42]). Suppose , , and are three real number sequences satisfying
- (i)
- ;
- (ii)
- (iii)
- or .
Then, .
Lemma 5
([43]). Let be a sequence of real numbers. Assume there exists at least a subsequence of such that for all . For every , define an integer sequence as
Then, as and for all , we have
3. Main Results
Let be a real Hilbert space. Let be a closed convex set. Let be an L-Lipschitz operator. Let be a -strongly monotone and weakly continuous operator such that its rang . Let the operator be -inverse strongly -monotone. Let be an -Lipschitzian quasi-pseudocontractive operator with and be an -Lipschitzian quasi-pseudocontractive operator with . Denote the solution set of Equation (5) by , that is, . In what follows, assume . Next, we firstly suggest the following algorithm for solving the problem in Equation (5).
For initial guess , define the sequence by the following form
where is a constant, , , , , , and are six sequences in and is a sequence in .
Theorem 1.
Suppose and are demiclosed at 0. Assume the following conditions are satisfied:
- (i)
- and ;
- (ii)
- and ;
- (iii)
- ;
- (iv)
- and .
Then, the iterative sequence defined by Equation (7) strongly converges to which solves the variational inequality
Proof.
Since is -strongly monotone, we deduce
Note that VI (8) has a unique solution which is denoted by . Thus, and . By virtue of Equation (6), we get for all . Note that is -inverse strongly -monotone. By Definition 1, we have
According to Equation (10), we get
and
From Equations (7), (9), and (11), we have
By Equations (11) and (13), we obtain
In view of Lemma 1, we deduce
and
Combining Equations (10), (14), (15) with (16), we obtain
An induction to derive
It follows that
Hence, and are all bounded.
By Equation (7), we get
It follows that
Observe that
and
By virtue of Equations (19)–(21), we deduce
Combining Equations (18) with (22), we have
Returning to Equation (13), we get
There exists two possible cases. Case 1. There exists such that is decreasing when . Thus, exists. From Equations (23) and (24), we have
This together with assumptions and implies that
Furthermore, it follows from Equation (18) that
By Equation (14), we have
Hence,
This, together with assumption , implies that
Set for all n. Applying Equation (6), we get
It follows that
In the light of Equations (27) and (30), we have
Then,
According to , Equations (26) and (28), we easily deduce
In view of Equations (15) and (16), we get
It follows from Equations (25), (31), and (32) that
and
Note that . Therefore,
Next, we prove . Let be a subsequence of such that
Note that is bounded. We can choose a subsequence of such that weakly. Assume without loss of generality. This indicates that due to the weak continuity of . Thus, and .
Apply Lemmas 2 and 6 to Equations (33) and (34) to deduce and , respectively. That is, . Next, we show . Let
According to Reference [32], we can deduce that R is maximal -monotone. Let . Since and , we have . Noting that , we get
It follows that
Thus,
By virtue of Equation (37), we derive that due to and . By the maximal -monotonicity of R, . So, . Therefore, .
From Equation (36), we obtain
Applying Equation (6), we obtain
It follows that
Therefore,
We can therefore apply Lemma 4 to Equation (39) to conclude that and .
Case 2. There exists such that . At this case, we set . Then, we have . For , we define a sequence by
We can show easily that is a non-decreasing sequence such that
and
According to techniques similar to Equations (36) and (39), we obtain
and
Since , we have from Equation (41) that
Combining Equations (41) with (42), we have
and thus
By Equations (40) and (41), we also get
The last inequality together with Equation (43) imply that
Applying Lemma 5 to get
Therefore, , i.e., . The proof is completed. □
For initial guess , define a sequence by the following form
where is a constant, , , , , , and are six sequences in and is a sequence in .
Corollary 1.
Suppose and are demiclosed at 0. Assume the following restrictions are satisfied:
- (i)
- and ;
- (ii)
- and ;
- (iii)
- ;
- (iv)
- and .
Then, the sequence defined by Equation (44) strongly converges to which solves the following variational inequality
4. Examples and Applications
In this section, we provide some examples and applications of our suggested algorithms and theorems.
Let be a real Hilbert space. Let be a closed convex set. Let be an L-Lipschitz operator. Let be a -strongly monotone and weakly continuous operator such that its rang . Let the operator be -inverse strongly -monotone. Let be an -Lipschitzian pseudocontractive operator with . Set .
For the initial guess , define the sequence by the following form
where is a constant, , , , , , and are six sequences in and is a sequence in .
Lemma 6
([40]). Let be a real Hilbert space, a closed convex subset of . Let be a continuous pseudocontractive operator. Then, is demi-closed at zero.
Theorem 2.
Assume the following conditions are satisfied:
- (i)
- and ;
- (ii)
- and ;
- (iii)
- ;
- (iv)
- and .
Then, the iterative sequence defined by Equation (45) strongly converges to which solves the variational inequality
Remark 1.
Algorithm (45) and Theorem 2 include the corresponding algorithm and theorem in Reference [18] as special cases, respectively.
Let be an -Lipschitzian monotone operator with and be an -Lipschitzian monotone operator with . Set .
For initial guess , define the sequence by the following form
where is a constant, , , , , , and are six sequences in and is a sequence in .
Theorem 3.
Assume the following conditions are satisfied:
- (i)
- and ;
- (ii)
- and ;
- (iii)
- ;
- (iv)
- and .
Then, the iterative sequence defined by Equation (46) strongly converges to which solves the variational inequality
5. Conclusions
In this paper, we investigated a generalized variational inequality and fixed points problems. We presented an iterative algorithm for finding a solution of the generalized variational inequality and fixed point of two quasi-pseudocontractive operators under a nonlinear transformation. We demonstrated the strong convergence of the suggested algorithm under some mild conditions, noting that in our suggested iterative sequence (Equation (7)), the involved operator requires some form of strong monotonicity. A natural question arises: how to weaken this assumption?
Author Contributions
All the authors have contributed equally to this paper. All the authors have read and approved the final manuscript.
Funding
Jen-Chih Yao was partially supported by the Grant MOST 106-2923-E-039-001-MY3.
Conflicts of Interest
The authors declare no conflict of interest.
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