Abstract
We study a new algorithm for the common solutions of a generalized variational inequality system and the fixed points of an asymptotically non-expansive mapping in Banach spaces. Under some specific assumptions imposed on the control parameters, some strong convergence theorems for the sequence generated by our new viscosity iterative scheme to approximate their common solutions are proved. As an application of our main results, we solve the standard constrained convex optimization problem. The results here generalize and improve some other authors’ recently corresponding results.
Keywords:
asymptotically non-expansive mapping; general variational inequality system; strong convergence; Banach space MSC:
47H10; 47H09; 47H05; 47J25
1. Introduction
In the paper, we assume that X is a real Banach space and is the dual space of X. The normalized duality mapping is defined by
where denotes the duality pairing between X and . As we all know, the mapping J is well defined and J is identity mapping if and only if X is a Hilbert space. In general, J is multiple-valued and nonlinear,
A Banach space X is called strictly convex if for and . The modulus of convexity of X is defined by
for all . X is said to be uniformly convex if for all .
Let be the modulus of smoothness of X defined by
A Banach space X is called uniformly smooth if as . Moreover, X is uniformly smooth if the norm of X is uniformly Frchet differentiable.
Suppose is an operator and the set of fixed points of T is denoted by , that is . T is said to be asymptotically non-expansive, if there exists a real sequence with such that
If , then we call that T is non-expansive. T is said to be uniformly asymptotically regular iff . T is said to be a contraction when there exists a number such that .
A mapping is called inverse strongly accretive if there exists such that
A bounded linear operator on X is called strongly positive, if there exists a fixed number such that
Assume is a convex and closed set. An operator is called sunny when Q has the following relation:
for . A mapping is called retraction if . Moreover, Q is called sunny non-expansive retraction from C onto E iff Q is a sunny, retraction, and non-expansive operator from C onto E.
Let C be a nonempty closed convex subset of X and be two nonlinear mappings, respectively. Recall the classical variational inequality problem is to find a point such that
The set of the solutions of the variational inequality (1) is denoted by . Lots of problems in physics, optimization, differential equation (inclusion), finance and minimax problem reduce to find an element of (1) and relevant numerical analysis methods can be considered to solve the problems, see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].
In 2011, Katchang et al. [17] introduced the following system of general variational inequalities. They considered the problem of finding such that:
and proved a strong convergence theorem for the following sequence of problem (2):
where and are two positive real numbers, , is the sunny non-expansive retraction from X to C, f is a contractive mapping on C and is a non-expansive mapping.
In 2020, Wang et al. [18] investigated a new general variational inequality system:
and proposed the following iterative method:
where , and are two positive real numbers, is the sunny non-expansive retraction from X to C, f is a contractive mapping on C and is an asymptotically non-expansive mapping. Then they proved that converges to the common element of the fixed points of an asymptotically non-expansive mapping and the solutions of the general variational inequality system (3).
Inspired by the above mentioned results, we study the following new generalized variational inequality system in uniformly convex and uniformly smooth Banach spaces:
This is the main problem which we seek to solve in this work and construct the suitable abstract iterative method. The new generalized variational inequality system problem (4) contains (1)–(3) as special cases. We construct a new iterative algorithm to approximate the common solutions of the generalized system of variational inequality (4) and an asymptotically non-expansive mapping. Some strong convergence theorems that depend on some suitable conditions are proved. Eventually, we show an application for solving the standard constrained problem of convex optimization to illustrate the efficiency of our main theorem. Some other outcomes proposed by other authors are also improved, see [9,10,17,18,19,20,21,22].
2. Preliminaries
This section contains the useful lemmas that are indispensable for proving our convergence theorem in the following section. Generally speaking, we suppose that space X is uniformly smooth and uniformly convex in this section.
Lemma 1
([18]). Suppose that and C is a closed convex set. Let J be the normalized duality mapping and be a retraction. Then the followings are equivalent:
- (I)
- Q is sunny and non-expansive;
- (II)
- ;
- (III)
- .
Lemma 2
([23]). Assume X is uniformly convex and is a closed ball of X. Then there exists a continuous strictly increasing convex function with such that
for any and for with .
Lemma 3
([24]). Suppose and are two bounded sequences of X. Let be a number sequence with . Assume that and . Then .
Lemma 4
([25]). For , there holds the relation:
Lemma 5
([26]). Let be a nonnegative number sequence satisfying:
where
Then .
Lemma 6
([27]). Suppose that is a strongly positive linear bounded operator on X with coefficient and Then,
Lemma 7
([18]). Assume C is a nonempty convex and closed subset of X. Assume mapping is a -inverse strongly accretive. Then,
If , then is non-expansive.
Lemma 8
([28]). Let be a closed convex subset of X. Suppose is a sunny non-expansive mapping and is an accretive operator from C into X. Then,
where
Lemma 9
([18]). There exists a convex continuous strictly increasing function such that
Lemma 10
([9]). Assume is a closed convex set. Suppose is an asymptotically non-expansive mapping and . If X has a weakly sequentially continuous duality mapping then the mapping is demi-closed at zero, where I is the identity mapping, i.e., if and , then .
Lemma 11.
Assume is a closed convex set. Let be three mappings, respectively. When , , then the following statements are the same:
- (i)
- is a solution of the problem (4);
- (ii)
- t is a fixed point of the mapping L, i.e., , where is defined byand , .
3. Results
Theorem 1.
Suppose X is a uniformly smooth and uniformly convex Banach space and is a closed convex subset of X. Assume is a sunny non-expansive retraction. Let be -inverse strongly accretive, respectively. L is the mapping defined by (5) in Lemma 11 and . Assume is a bounded linear strongly positive operator on X with coefficient and Let g be an -contraction mapping on C and be an asymptotically non-expansive mapping with . Assume is defined as follows:
where , and the following coefficient conditions are satisfying:
- (i)
- (ii)
- ,
- (iii)
- (iv)
Then the sequence converges strongly to and is a solution of (4), where and with
Proof.
Our proof process can be divided into 6 steps.
Step 1: Let be -inverse strongly accretive mapping, respectively, and , with . From Lemma 7, we know that and are non-expansive mappings. Moveover, we attain that and are non-expansive mapping.
So, we have
Then, L is non-expansive.
Then we compute
It follows that
From conditions and in Theorem 1, we get
By induction, we readily obtain
This implies that is bounded, and so are .
Step 3: By (6) and Lemma 6, we gain that
Which implies that
By conditions – in Theorem 1, we have
where
We set
where
Since
we attain
Thus, we acquire
So, by conditions and in Theorem 1, we gain
Adopting Lemma 3, we gain
Taking notice of . Then
Step 4: Again by the definition of and Lemma 4, we get
By the non-expansiveness of Lemma 7 and (10), we have
This implies that
By conditions in Theorem 1 and (9), we can acquire
Applying Lemma 2 and (10), we obtain
Which implies that
Applying Lemma 1, Lemma 9 and (10), we gain
It follows that
Hence, we know that
Which implies that
Adopting condition in Theorem 1, we can get
and also
Since
We know that
Because T is an asymptotically non-expansive mapping, we attain
Step 5: We can take a subsequence of and it is satisfied with
Since X is a uniformly smooth Banach space and is bounded, there exists a subsequence as . By (15) and (23), we know
From (21), (24) and Lemma 10, we can acquire . It follows that (22), (24) and Lemma 10, we can attain . By (14), (24), Lemma 10 and Lemma 8, we get . So, we attain From (24) and property of , we get
Step 6: Finally, from Lemma 4 and Lemma 6, we acquire
and
It follows that
That is
Thus, from Lemma 5, we receive that So converges strongly to , and from Lemma 11, we have is a solution of the problem (4), where and The proof is completed. □
As an application of our main result Theorem 1, we can prove strong convergence theorems for approximating the solution of the standard convex optimization problem.
Assume C is a convex and closed subset of X. The standard constrained convex optimization problem is to find such that
where is a convex, Fechet differentiable function. is used to indicate the solution set of (26).
Lemma 12.
([21]) An essential condition of optimality for a point to be a solution of (26) is that is a solution of the variational inequality
Equivalently, is a fixed point of the mapping . In addition, if is convex, then the optimality condition (27) is also sufficient.
Theorem 2.
Assume that space X, mappings and T are the same as in Theorem 1. Suppose is a convex real-valued function with the gradient is -inverse strongly accretive. Assume that . Define the sequence in X as follows:
where , and δ satisfy the same coefficient conditions as in Theorem 1. Then converges strongly to and is a solution of (4), where and with
Proof.
From Lemma 12 and Theorem 1, we can gain Theorem 2, where and Then, the proof is completed. □
Remark 1.
In Theorem 2, we give the iterative approximation method and strong convergence results for the common elements of the solutions of the standard constrained convex optimization problem (26) and the fixed points of an asymptotically non-expansive mapping T.
As we all know, a Hilbert space H is a uniformly smooth and uniformly convex Banach space and the metric projection is sunny non-expansive retractive. So, when Hilbert space H takes the place of Banach space X and the sunny non-expansive retraction becomes to the metric projection , the results of Theorem 1 and Theorem 2 still hold. More specifically, if and in Theorem 2, we can attain the major results of Keerti et al. [19].
4. Numerical Examples
Someone may question whether the coefficients in Theorem 1 are complex enough to be not obtained. Now we give a concrete numerical example to show that the coefficients in Theorem 1 can be obtained.
Let R be the real number set and be a mapping from to R defined by , respectively. Put then the mapping defined by (5) in Lemma 11. We take and let be defined by , respectively. So, is generated by (6). Assume that T is an asymptotically non-expansive mapping defined by . Let , and is generated by (6) where and From the propose definition, the all conditions of Theorem 1 are satisfied. We know . Then is generated by (6) and we simplify the form of (6) to get
So, clearly, converges strongly to .
5. Conclusions
In this paper, we present a new generalized variational inequality system problem (4) which contains many special cases. In the case when we put , then the problem (4) reduces to problem (3); If putting in (4), we get
which is a variational inequality modified system by Ceng et al. [2]. In the sense that if we take in (29), then the problem (29) reduces to problem (2). We study the common elements of the set of solutions of an asymptotically non-expansive operation equation with the L mapping defined by (5) and the solution set of the generalized proposed system problem (4). The convergence analysis of a new way by using the generalized semi-closure principle supplied by us in Wang et al. [9] for finding the propose common elements in Banach spaces are investigated. Under the suitable conditions imposed on parameters, some strong convergence theorems are attained. We locally generalize some corresponding recent results of Keerti et al. [19] from Hilbert spaces to Banach spaces and from non-expansive mapping to asymptotically non-expansive mapping. As an application, we prove the strong convergence theorem of the standard constrained convex optimization problem by our main result. Eventually, we also give a real numerical example to show that the coefficients in Theorem 1 can be obtained and the theorems are reasonable and valid.
Author Contributions
Conceptualization, C.L.; data curation, C.L. and L.L.; formal analysis, Y.W. and C.L.; funding acquisition, Y.W. and C.L.; investigation, Y.W. and L.L.; methodology, Y.W., C.L. and L.L.; project administration, Y.W.; resources, C.L.; Software, L.L.; supervision, Y.W.; validation, L.L.; writing—original draft, C.L. and L.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant no. 11671365) and the Natural Science Foundation of Zhejiang Province (Grant no. LY14A010011).
Conflicts of Interest
The authors declare that they have no competing interests.
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