Abstract
We propose two Mann-type subgradient-like extra gradient iterations with the line-search procedure for hierarchical variational inequality (HVI) with the common fixed-point problem (CFPP) constraint of finite family of nonexpansive mappings and an asymptotically nonexpansive mapping in a real Hilbert space. Our methods include combinations of the Mann iteration method, subgradient extra gradient method with the line-search process, and viscosity approximation method. Under suitable assumptions, we obtain the strong convergence results of sequence of iterates generated by our methods for a solution to HVI with the CFPP constraint.
1. Introduction
Let be the inner product and induced norm of a real Hilbert space H. Given a convex closed set with . Let be the nearest point projection from H onto C. Given , we denote the set by the fixed points set of T. We say that is asymptotically nonexpansive if there exists a sequence with such that the following is the case.
S is called nonexpansive if .
Suppose is a continuous mapping. The variational inequality problem (VIP) is to find such that . We denote by VI() the set of solutions to VIP. One of the popular methods for solving VIP is the extragradient method [1]: ,
with where L is the Lipschitz constant of A. If , then generated by (2) converges weakly. Extragradient method (2) has been studied by many authors (see, e.g., [2,3,4,5,6,7,8,9,10,11,12,13] and references therein).
In (2), one needs to compute projections onto C twice for each iteration, and hence a drawback emerges. In [3], Censor et al. modified (2) and introduced the subgradient extragradient method:
with where L is the Lipschitz constant of A. In 2018, by virtue of the inertial technique, Thong and Hieu [9] studied an inertial subgradient extragradient method, :
with where L is the Lipschitz constant of A. Under some conditions, a weak convergence of was obtained. Ceng and Shang in [11] introduced the hybrid inertial subgradient extragradient method with a linear-search process to solve VIP in which A is pseudomonotone and Lipschitz continuous and the common fixed-point problem (CFPP) of nonexpansive mappings and an asymptotically nonexpansive mapping T in a real Hilbert space H are present. Given a contraction with constant , and an -strongly monotone and -Lipschitzian mapping with for . Let and with . Moreover, one writes for integer with the mod function taking values in the set , i.e., if for some integers and , then if and if . Their algorithm is formulated below.
Under appropriate conditions, they proved the strong convergence of Algorithm 1 to an element of with . Meanwhile, Reich et al. [12] suggested the modified projection-type method for solving the VIP with the pseudomonotone and uniformly continuous mapping A given a sequence and a contraction with constant . Their algorithm is formulated below.
| Algorithm 1 (see [11]). Initialization: Choose . Take . |
| Iterative Steps: Compute in this manner: |
| Step 1. Set and compute , |
| where is chosen to be the largest satisfying |
| Step 2. Compute with . |
| Step 3. Compute . |
| Again set and go to Step 1. |
Under mild conditions, strong convergence of Algorithm 2 to an element of was given. Inspired by the above research works, we propose two Mann-type subgradient-like extragradient algorithms with linear-search process for solving a hierarchical variational inequality (HVI) with the common fixed-point problem (CFPP) constraint of family nonexpansive mappings and an asymptotically nonexpansive mapping in Hilbert spaces. We combine the Mann iteration method, subgradient extragradient method with linear-search process, and viscosity approximation method and provide strong convergence results under suitable conditions. We provide an illustration of our theory with an example.
We organize the paper as follows: Some definitions and preliminary results are given in Section 2. In Section 3, we provide convergence analysis of the proposed algorithms. In Section 4, our main results are applied to solve the VIP and CFPP in an illustrated example. Finally, some concluding remarks are given in Section 5.
| Algorithm 2(see [12]). |
| Initialization: Given . Let be arbitrary. |
| Iterative Steps: Given the current iterate , calculate as follows: |
| Step 1. Compute and . If , then stop; is a solution of . Otherwise, |
| Step 2. Compute , where and is the smallest nonnegative integer j satisfying |
| Step 3. Compute , where and . |
| Again set and go to Step 1. |
2. Preliminaries
A mapping is called the following:
- (a)
- L-Lipschitz continuous (or L-Lipschitzian) if such that ;
- (b)
- Monotone if ;
- (c)
- Pseudomonotone if ;
- (d)
- -strongly monotone if such that ;
- (e)
- Sequentially weakly continuous if , the relation holds: .
It is known that every monotone operator is pseudomonotone. However, the converse fails. For each , there exists a unique nearest point in C such that . Such a point is denoted by , called a metric projection of H onto C. According to [14], we know that the following holds:
- (a)
- ;
- (b)
- ;
- (c)
- ;
- (d)
- ;
- (e)
- with .
Lemma 1
(see [13]). Let and be two real Hilbert spaces. Suppose that is uniformly continuous on bounded subsets of and M is a bounded subset of , then is bounded.
The following inequality is an immediate consequence of the subdifferential inequality of the function :
Lemma 2
(see [15]). Let h be a real-valued function on H and define . If K is nonempty and h is Lipschitz continuous on C with modulus , then , where denotes the distance of x to K.
Lemma 3
(see [3], Lemma 1). Let be pseudomonotone and continuous. Then, is a solution to the VIP , if and only if .
Lemma 4
(see [16]). Let be a sequence of nonnegative numbers satisfying the following conditions: , where and are sequences of real numbers such that (i) and , and (ii) or . Then .
Lemma 5
(see [17]). Let X be a Banach space that admits a weakly continuous duality mapping, let C be a nonempty closed convex subset of X, and let be an asymptotically nonexpansive mapping with . Then, is demiclosed at zero, i.e., if is a sequence in C such that and , then , where I is the identity mapping of X.
3. Main Results
In this section, we assume the following.
is an asymptotically nonexpansive mapping and is a nonexpansive mapping for such that the sequence is defined as in Algorithm 1.
is pseudomonotone and uniformly continuous on C, s.t. for each with .
is a contraction with constant , and with .
and such that the following is the case:
- (i)
- and ;
- (ii)
- and ;
- (iii)
- and .
Lemma 6.
The Armijo-type search rule (5) is well defined, and the inequality holds: . Recall that the Armijo-type search rule is a backtracking line search that determines the amount to move along a given search direction and involves starting with a relatively large estimate of the step size for movement along the search direction and iteratively shrinking the step size until a decrease as given in (5) is observed.
Proof.
Since and A are uniformly continuous on C, one has . If , then it is clear that . If , then there exists an integer satisfying (5). □
Since is firmly nonexpansive, one knows that . Placing and , one obtains , and hence .
Lemma 7.
Let and let the function be defined by (6). Then, and . In particular, if , then .
Proof.
The first assertion of Lemma 6 is obvious. In what follows, let us show the second assertion. Indeed, let . Then, by Lemma 3 one has . Thus, the following is the case.
On the other hand, by (5) one has the following.
Thus, by Lemma 6, we obtain the following.
Combining (7) and (8), we obtain the following.
Consequently, , as asserted. □
Lemma 8.
Let be bounded sequences generated by Algorithm 3. If and and such that , then .
| Algorithm 3 Initialization: Given . Pick . |
| Iterative Steps: Given the current iterate , calculate as follows: |
| Step 1. Set , and compute and . |
| Step 2. Compute , where and is the smallest nonnegative integer j satisfying |
| Step 3. Compute and where and |
| Set and return to Step 1. |
Proof.
By Algorithm 3, , and hence . Utilizing the assumptions and , we have the following.
By Algorithm 3, we obtain , which immediately yields the following.
Since and are bounded, we obtain , which together with implies the following.
Moreover, from , we have , and hence the following is the case.
According to the uniform continuity of A on C, one knows that is bounded (due to Lemma 1). Note that is bounded as well. Thus, from (12), we obtain . Meantime, observe that . Since , from the uniform continuity of A we obtain , which together with (12) yields . □
Next we show that for . Indeed, note that for , the following is the case.
Hence, from (10) and the assumption , we obtain for . This immediately implies that the following is the case.
We now take a sequence satisfying as . For each , we denote by the smallest positive integer such that the following is the case.
Since is decreasing, it is clear that is increasing. Noticing that guarantees , we set , and we obtain . Thus, from (14), we obtain . Again from the pseudomonotonicity of A, we have . This immediately results in
We claim that . Indeed, from and , we obtain . Using the assumption on A, instead of the sequentially weak continuity of A, we obtained (otherwise, if , then z is a solution). Note that and as . Thus, it follows that . Hence, we obtain as .
Next, we show that . Indeed, from and , we obtain . From (13), we have for . Note that Lemma 5 guarantees the demiclosedness of at zero for . Thus, . Since r is an arbitrary element in the finite set , we obtain . Simultaneously, from and , we obtain . From (11), we have . From Lemma 5, it follows that is demiclosed at zero, and hence we obtain , i.e., . On the other hand, letting , we deduce that the right-hand side of (15) tends to zero by the uniform continuity of A, the boundedness of , and the limit . Thus, we obtain . By Lemma 3, we have . Therefore, .
Lemma 9.
Let be the sequence constructed by Algorithm 3. Then, the following is the case.
Proof.
To show the conclusion, we consider two cases. In the case when , we might assume that there exists a constant such that , which hence yields the following.
This together with results in . □
In the case, when , we might pick a subsequence of such that the following is the case.
Let . Then, . Since , we have the following.
From the step size rule (5) and the definition of , it follows that the following is the case.
Since A is uniformly continuous on bounded subsets of C, (18) ensures the following.
This, however, contradicts with (17). Thus, it follows that .
Theorem 1.
Let be the sequence constructed by Algorithm 3. Assume that . Then, the following is the case:
where is the unique solution of the VIP: .
Proof.
Since and , we may assume, without loss of generality, that and . We claim that is a contraction. Indeed, it is clear that , which implies that is a contraction. Banach’s Contraction Mapping Principle guarantees that has a unique fixed point. Say , that is, . Thus, there exists a unique solution of the VIP
□
If , then for and , together with Algorithm 3, imply the following.
Hence, using the continuity of A on C, we obtain that and the following is the case.
In addition, it is clear that the following is obtained.
Next, we show the sufficiency of the theorem. To this aim, we assume and divide the proof of sufficiency into several steps.
Step 1. We show that is bounded. Indeed, take an arbitrary . Then, and . We claim that the following inequality holds.
Indeed, one has the following.
Thus, the following is the case.
Then, the following is obtained:
which together with (22) yields the following.
Thus, from (23) and , the following is the case.
Therefore, we obtain . Thus, is bounded, and so are the sequences .
Step 2. We show that the following is the case.
To prove this, we first note that the following is the case:
where for some . On the other hand, from (23) one has the following.
Substituting (24) into (25), one obtains the following.
This immediately implies the following.
Step 3. We show the following.
Indeed, we claim that for some , the following obtains.
Since the sequence is bounded, there exists such that . This ensures that for all , the following is the case:
which hence implies that is L-Lipschitz continuous on . By Lemmas 2 and 7, we obtain
Combining (21) and (27), we obtain the following.
From Algorithm 3, (23), and (26), the following is obtained.
This immediately yields the following.
Step 4. Let us obtain the following.
Indeed, from Algorithm 3 and (23), one obtains the following.
Step 5. Let , we deduce from (28) that the following is the case.
We need to show that . Substituting , from Step 2, we obtain the following.
Since , and . From the boundedness of , one obtains the following.
Substituting , from Step 3, we obtain the following.
Since , , and (due to the assumption), from the boundedness of , one obtains the following.
Hence, by Lemma 9, we deduce the following.
Obviously, assumption together with (31) implies the following.
From the boundedness of , it follows that there exists a subsequence of such that the following is the case.
Since H is reflexive and is bounded, we may assume, without loss of generality, that . Thus, from (33), one obtains the following.
Thus, it follows from (due to (32)) and that . Since and , by Lemma 8, we infer that . Hence, from (20) and (34), one obtains the following:
which immediately results in the following.
Note that , and the following is the case.
Consequently, by applying Lemma 4 to (29), one has . This completes the proof.
Theorem 2.
Let be nonexpansive and the sequence be constructed by the modified version of Algorithm 3; that is, for any initial , the following is the case:
where for each , , and are chosen as in Algorithm 3. Then, the following is the case:
where is the unique solution of the VIP: .
Proof.
The necessity is obvious. Thus, we show the sufficiency. Assume and divide the rest of the proof into several steps. □
Step 1. is bounded: Indeed, using the same argument as in Step 1 of the proof of Theorem 1, we obtain the desired assertion.
Step 2. We obtain the following:
for some . Indeed, using the same arguments as in Step 2 of the proof of Theorem 1, we obtain the desired assertion.
Step 3. We prove that the following is the case.
Indeed, similar arguments similar to those in Step 3 of the proof of Theorem 1 provide the assertion.
Step 4. We show the following.
By Step 4 of the proof of Theorem 1, we obtain the desired conclusion.
Step 5. converges strongly to the unique solution of the VIP (20): substitute and we deduce from Step 4 that the following is the case.
We show that . Using the same arguments as those of (30) and (31), we obtain the following.
Now, the following is obtained.
From (38), , , and the boundedness of , it follows that, as , the following is the case.
Obviously, combining (38) and guarantees the following.
The rest of the proof is similar to the arguments in Step 5 of the proof of Theorem 1.
Next, we introduce modified Mann-type subgradient-like extragradient algorithm.
Note that Lemmas 6–9 are valid for Algorithm 4.
| Algorithm 4Initialization: Given . Let be arbitrary. |
| Iterative Steps: Given the current iterate , calculate as follows: |
| Step 1. Set , and compute and . |
| Step 2. Compute , where and is the smallest nonnegative integer j satisfying |
| Step 3. Compute and where and |
| Again set and go to Step 1. |
Theorem 3.
Let be the sequence constructed by Algorithm 4. Assume that . Then, the following is the case:
where is the unique solution of the VIP: .
Proof.
Using the same arguments as in the proof of Theorem 1, we deduce that there exists a unique solution of the VIP (20) and that the necessity of the theorem is valid. □
For sufficiency, assume and consider these steps.
Step 1. We show that is bounded. Indeed, using the same arguments as in Step 1 of the proof of Theorem 1, we obtain that inequalities (21)–(23) hold. Thus, from (23) and , the following is the case.
Hence, . Thus, is bounded.
Step 2. We show the following.
To prove this, we first note that the following is the case.
The desired conclusion follows from Step 2 of the proof of Theorem 1.
Step 3. We show the following.
Indeed, using the same argument as that of (28), we obtain that for some , the following is the case.
From Algorithm 4, (23), and (44), the following is obtained.
By rearranging, we obtain the desired inequality.
Step 4. We show the following.
Indeed, from Algorithm 4 and (23), one obtains the following:
which, hence, results in the desired assertion.
Step 5. We show that converges strongly to the unique solution of the VIP (20). Indeed, Step 5 of the proof of Theorem 1 provides the result.
Theorem 4.
Let be nonexpansive and the sequence be constructed by :
where for each , and are chosen in Algorithm 4. Then, the following is the case:
where is the unique solution of the VIP: .
Proof.
Similar arguments as in the proof of Theorem 2 and Step 5 of Theorem 3 provide the conclusions. □
Remark 1.
Our results complement the results in Kraikaew and Saejung [10], Ceng and Shang [11], and Reich et al. [12] in the following ways:
- (i)
- The problem of finding an element of in [10] is extended to develop our problem of finding an element of where is nonexpansive for , and is asymptotically nonexpansive. The Halpern subgradient extragradient method for solving VIP in [10] is extended to develop our Mann-type subgradient-like extragradient method with a line-search process for solving VIP and CFPP, which is based on Mann iteration method, subgradient extragradient method with line-search process, and viscosity approximation method.
- (ii)
- The results in [12] are extended to finding an element of . The modified projection-type method with linear-search process for solving the VIP in [12] is extended to develop our Mann-type subgradient-like extragradient method with line-search process for solving the VIP and CFPP, which is based on the Mann iteration method, subgradient extragradient method with line-search process, and viscosity approximation method.
- (iii)
- The problem of finding an element of with Lipschitz continuity and sequentially weak continuity mapping A in [11] is extended to finding an element of where A is uniformly continuous such that for each with . The hybrid inertial subgradient extragradient method with line-search process in [11] is generalized to Mann-type subgradient-like extragradient method with line-search process, e.g., the original inertial approach is replaced by our Mann iteration method , and the original iterative step is replaced by our simpler iterative one . It is worth mentioning that the definition of in the former formulation of is very different from the definition of in the latter formulation of .
- (iv)
- The method in [10] involves a combination of Halpern approximation method, subgradient extragradient method, and Mann iteration to find a common solution to variational inequalities and common fixed point problem involving quasi-nonexpansive mapping with strong convergence results obtained. The method in [11] solves a problem of finding a common solution to variational inequalities and common fixed point problem in which one of the operators is asymptotically nonexpansive and others are nonexpansive mappings. The method of [11] is a combination of the subgradient extragradient method, viscosity approximation and hybrid steepest-descent method, and strong convergence results obtained. In [12], a strongly convergent method that is a combination of projection-type method and viscosity approximation method is proposed to solve variational inequalities. Our proposed methods in this paper are proposed to solve variational inequalities and common fixed point problem for which one of the operators is asymptotically nonexpansive and others are nonexpansive, and A in the variational inequality is pseudomonotone and uniformly continuous (unlike [11] where A is Lipschitz continuous). One method involves a combination of the method proposed in [12] and viscosity approximation. In essence, our results in this paper reduce to the results in [12] when the operators in the common fixed point problem are identity mappings. Furthermore, our method does not involve the hybrid steepest-descent method and subgradient extragradient method used in [11]. Our results also serve as extensions of the results obtained in [10] in the setting of variational inequalities.
4. Applications
In this section, our main results are applied to solve the VIP and CFPP in an illustrated example. Substitute and .
We first provide an example of Lipschitz continuous and monotone mapping A, asymptotically nonexpansive mapping T, and nonexpansive mapping with . Let and with the inner product and induced norm . The initial point is randomly chosen in C. Take with . Let and be defined as , , and for all . Now, we first show that A is pseudomonotone and Lipschitz continuous. Indeed, for all , we have the following.
This implies that A is Lipschitz continuous. Next, we show that A is pseudomonotone. For each , it is easy to see that the following is the case.
Furthermore, it is easy to see that T is asymptotically nonexpansive with , such that as . Indeed, we observe that the following is the case:
and the following obtains.
It is clear that and
In addition, it is clear that is nonexpansive and . Therefore, . In this case, Algorithm 3 can be rewritten as follows:
where for each , and are chosen as in Algorithm 3. Then, by Theorem 1, we know that converges to if and only if as .
In particular, since is also nonexpansive, we consider the modified version of Algorithm 3:
where for each , and are chosen as stated above. Then, by Theorem 2, we know that converges to if and only if as .
5. Conclusions
We have introduced two Mann-type subgradient-like extra gradient algorithms that combine projection-type method, viscosity approximation, and Armijo-type line-search procedure to solve variational inequalities and common fixed-point problem of finitely many nonexpansive mappings and an asymptotically nonexpansive mapping in a real Hilbert space. We obtained strong convergence results of the sequences of iterates generated by our proposed methods under some standard conditions. We also gave some illustrative example to justify the theoretical analysis. Part of our future research is aimed to obtain strong convergence results for modifications of our proposed methods with Nesterov inertial extrapolation step and self-adaptive step sizes.
Author Contributions
All authors contributed equally to this work, which included mathematical theory and analysis. All authors have read and agreed to the published version of the manuscript.
Funding
Lu-Chuan Ceng is partially supported by the Innovation Program of Shanghai Municipal Education Commission (15ZZ068), Ph.D. Program Foundation of Ministry of Education of China (20123127110002), and Program for Outstanding Academic Leaders in Shanghai City (15XD1503100). The research of Jen-Chih Yao was supported by the grant MOST 108-2115-M-039- 005-MY3.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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