Abstract
In this paper, we design two inertial-type subgradient extragradient algorithms with the linear-search process for resolving the two pseudomonotone variational inequality problems (VIPs) of and the common fixed point problem (CFPP) of finite Bregman relatively nonexpansive operators and Bregman relatively demicontractive operators in Banach spaces of both p-uniform convexity and uniform smoothness, which are more general than Hilbert ones. By the aid of suitable restrictions, it is shown that the sequences fabricated by the suggested schemes converge weakly and strongly to a solution of a pair of VIPs with a CFPP constraint, respectively. Additionally, the illustrative instance is furnished to back up the practicability and implementability of the suggested methods. This paper reveals the competitive advantage of the proposed algorithms over the existing algorithms; that is, the existing hybrid projection method for a single VIP with an FPP constraint is extended to develop the modified inertial-type subgradient extragradient method for a pair of VIPs with an CFPP constraint.
Keywords:
modified inertial-type subgradient extragradient method; variational inequality problem; finite Bregman relatively nonexpansive mappings; Bregman relatively demicontractive mapping; Bregman distance; Bregman projection MSC:
47H05; 47H10; 65K15; 65Y05; 68W25
1. Introduction
Suppose that the real Hilbert space H has both inner product and induced norm , and let be the metric projection of H onto a nonempty, convex and closed given a nonlinear operator . We denote by the fixed-point set of S. Also, the and → are used to represent the real-number set, the weak convergence and the strong convergence, respectively. A mapping is said to be strictly pseudocontractive (see [1]) if s.t. . In particular, in case , S reduces to a nonexpansive mapping. Moreover, S is said to be demicontractive if and s.t. . In particular, in case , S reduces to a quasi-nonexpansive mapping. During the past few decades, the fixed point theory has played a vital part in solving many problems arising in nonlinear analysis and optimization theory, such as differential hemivariational inequalities (see [2]), monotone bilevel equilibrium problems (see [3]), fractional set-valued projected dynamical systems (see [4]) and so on.
Let be a mapping. Consider the classical variational inequality problem (VIP) of finding s.t. . The solution set of the VIP is denoted by . In 1976, to seek a point in , via relative weak conditions, Korpelevich [5] put forward an extragradient approach below; i.e., for any initial , the sequence is generated by
with . If , then the sequence converges weakly to an element in . To the best of our knowledge, the Korpelevich extragradient approach is one of the most effective methods for solving the VIP at present. The literature on the VIP is vast and the Korpelevich extragradient approach has attained wide attention paid by many scholars, who ameliorated it in various forms; see, e.g., [1,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26].
Furthermore, in 2018, Thong and Hieu [18] first put forward the inertial subgradient extragradient method; that is, for any initial , the sequence is generated by
with constant . Under suitable conditions, they proved the weak convergence of to an element of . Subsequently, Ceng et al. [14] proposed the modification of an inertial subgradient extragradient approach for settling the VIP of pseudomonotonicity and common fixed-point problem (CFPP) of finite nonexpansive mappings. Let be nonexpansive for , be of both L-Lipschitz continuity and pseudomonotonicity on H, and of sequentially weak continuity on C, s.t. . Let f be -contractive self-mapping on H for and the self-mapping F on H be of both -strong monotone and -Lipschitz continuity s.t. with . Presume that are positive sequences s.t. and . 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 .
Under appropriate conditions, they proved the strong convergence of to an element of . In addition, combining the subgradient extragradient method and Halpern’s iteration method, Kraikaew and Saejung [19] proposed the Halpern subgradient extragradient rule for solving the VIP in 2014. They proved the strong convergence of the proposed method to an element in . In 2021, Reich et al. [23] invented two gradient-projection schemes for settling the VIP for uniformly continuous pseudomonotone mapping. In particular, they used a novel Armijo-type line search to acquire a hyperplane that strictly separates the current iterate from the solutions of the VIP under consideration. They proved that the sequences generated by two schemes converge weakly and strongly to a point in for uniformly continuous pseudomonotone mapping A, respectively.
On the other hand, let for , and suppose that E is a Banach space of both p-uniform convexity and uniform smoothness and the nonempty is of both convexity and closedness. The dual space of E is denoted by . The norm and the duality pairing between E and are denoted by and , respectively. Let and be the duality mappings of E and , respectively. Let , be the Bregman distance with respect to (w.r.t) and the surjective be the Bregman’s projection w.r.t. , and presume that s.t. , and . Assume that is uniformly continuous and pseudo-monotone mapping and is Bregman relatively nonexpansive mapping. Very recently, inspired by the research outcomes in [23], Eskandani et al. [25] invented the hybrid projection method with linear search term in order to seek a solution of a VIP with an FPP constraint of S.
By the aid of mild restrictions, it was proven in [23] that the sequence converges strongly to . Motivated by the existing outcomes as above, we design two inertial-type subgradient extragradient algorithms with a linear-search process for resolving the two pseudomonotone VIPs and the CFPP of finite Bregman’s relative nonexpansivity operators and a Bregman’s relative demicontractivity operator in Banach spaces of both p-uniform convexity and uniform smoothness. With the help of appropriate assumptions, it is proven that the sequences fabricated by the suggested algorithms converge weakly and strongly to a solution of a pair of VIPs with a CFPP constraint, respectively. Additionally, an illustrative instance is furnished to back up the practicability and implementability of the proposed approaches.
The structure for the paper is built as follows: Section 2 releases certain terminologies and preliminary results. In Section 3, we discuss the convergent behavior of the sequences generated by the proposed approaches. In Section 4, the major outcomes are employed to deal with a pair of VIPs with a CFPP constraint in an illustrative instance. Our algorithms are of both advantage and flexibility over Algorithms 1 and 2 as above due to their solving a pair of VIPs with a CFPP constraint. Our outcomes are the improvement and extension of the existing ones in the literature; see, e.g., [14,23,25].
| Algorithm 1: ([14], Algorithm 3) |
Inertial subgradient extragradient method. Initialization: Given . Let be arbitrary. Iterative steps: Calculate as follows: Step 1. Given the iterates and , choose s.t. , where
Step 2. Compute and . Step 3. Construct the half-space , and compute . Step 4. Calculate , and update
Set and go to Step 1. |
| Algorithm 2: ([25]) |
Hybrid projection method. Initial step: Let positive , and put arbitrarily. Iterations: Compute below: Step 1. Calculate and . If and , then stop; . Otherwise, Step 2. Compute , where and is the smallest nonnegative integer k satisfying . Step 3. Compute and , with and . Again set and go to Step 1. |
In the end, it is worthy to mention that the existing method in [25] is most closely related to our proposed method; that is, the hybrid projection method for resolving a single VIP with an FPP constraint in [25] is extended to develop our modified inertial-type subgradient extragradient method for resolving a pair of VIPs with a CFPP constraint. Compared with the corresponding results in [25], our results improve, extend and develop them in the two aspects below: (i) the problem of finding a solution of a single VIP with an FPP constraint (involving a Bregman relatively nonexpansive mapping) in [25] is extended to develop our problem of finding a solution of a pair of VIPs with a CFPP constraint (involving finite Bregman relatively nonexpansive mappings and a Bregman relatively demicontractive mapping); (ii) the hybrid projection method with line-search process in [25] is extended to develop our modified inertial-type subgradient extragradient method with the line-search process.
| Algorithm 3: The 1st modified inertial-type subgradient extragradient method |
The 1st modified inertial-type subgradient extragradient method. Initialization: Given arbitrarily and let for . Choose and s.t. , and . Moreover, given the iterates and , choose s.t. , where
Iterative steps: Calculate as follows: Step 1. Calculate and calculate , , and , with and being the smallest s.t.
Step 2. Calculate , with and
Step 3. Calculate , and , with and is the smallest s.t.
Step 4. Calculate and , with , and
Again set and go to Step 1. |
In Section 4, we have provided a numerical example to show the competitive advantage of our proposed algorithms over the existing algorithms, e.g., the ones in [25]. In fact, we have provided the illustrative example of a pair of VIPs with a CFPP constraint in Section 4. Note that the existing method in [25] is only utilized for solving a single VIP with an FPP constraint. So, there is no way for this method to handle the numerical example in Section 4; that is, it is invalid for a pair of VIPs with a CFPP constraint. However, our suggested method can settle the illustrative example in Section 4. This ensures the competitive advantage of our proposed algorithms over the existing algorithms.
2. Preliminaries
Let the real Banach space () possess the dual space . The (resp., ) is used to stand for the strong (resp., weak) convergence of to . Moreover, the set of weak cluster points of is denoted by , i.e., . Let and with . A Banach space E is referred to as being strictly convex if, for each with , one has . E is referred to as being of uniform convexity if , s.t. with , one has . It is known that a uniformly convex Banach space is reflexive and strictly convex. The modulus of convexity of E is the function defined by . It is also known that E is uniformly convex if and only if . Moreover, E is referred to as being p-uniformly convex if s.t. .
A function is the modulus of smoothness iff it is written as . E is said to be uniformly smooth if , and q-uniformly smooth if s.t. . It is known that E is p-uniformly convex if and only if is q-uniformly smooth. For example, see [27] for more details. Putting for each , we say that is uniformly convex on bounded sets (see [25]) if , where is specified below
for all . is called the gauge function of f with uniform convexity. It is obvious that the function is nondecreasing.
Let be a convex function. If the limit exists for each , then f is referred to as being of Gâteaux differentiability at y. In this case, the gradient of f at y is the linear function , which is defined by for each . The function f is referred to as being of Gâteaux’s differentiability if it is of Gâteaux’s differentiability at each . Whenever is achieved uniformly for any , one says that f is of Fréchet’s differentiability at y. Furthermore, f is termed as being of uniform Fréchet differentiability on if is achieved uniformly for . A Banach space E is called smooth if its norm is Gâteaux differentiable.
Let for . The duality mapping is specified as follows
Recall that E is of smoothness iff the duality mapping is single-valued. Also, E is of reflexivity iff is of surjectivity, and E is of strict convexity iff is an injection. So, it follows that, if E is smooth, strictly convex and reflexive Banach space, then is a single-valued bijection, and, in this case, . Furthermore, E is of uniform smoothness if and only if function is of uniform Fréchet differentiability on any bounded set if and only if the single-valued is of uniform continuity on any bounded set. It is easy to see that E is of uniform convexity if and only if function is of uniform convexity (see [27]).
Let the convex function be of Gâteaux’s differentiability. Bregman’s distance w.r.t. f is specified below
It is worth mentioning that Bregman’s distance does not become a metric in the common sense of the terminology. Evidently, , but cannot yield . Generally, is of no symmetry and does not fulfill the triangle inequality. But, fulfills the three point identity
See [28] for more details on Bregman functions and distances.
It is noteworthy that, if E is a smooth Banach space, then is Gâteaux’s derivative of . Then, Bregman’s distance w.r.t. is specified below
In the Banach space E of both smoothness and p-uniformly convexity for , there holds the following relationship between the metric and Bregman distance:
where is some fixed number (see [29]). Using (5), one knows that, for any bounded ,
Let the Banach space E be of reflexivity, smoothness and strict convexity. Let with C be of convexity and closedness. Bregman’s projections are formulated as minimizers of Bregman’s distances. The Bregman’s projection of onto C w.r.t. is only a point s.t. . In Hilbert spaces, the Bregman projection w.r.t. reduces to the metric projection.
Using Corollary 4.4 in [30] and Theorem 2.1 [31] in Banach spaces of uniform convexity, Bregman projections can be featured as the relation below:
Moreover, this inequality is equivalent to the descent property
In case , the duality mapping reduces to the normalized duality mapping and is denoted by J. The function is formulated below
and .
In terms of [25], the w.r.t. is specified below
So, . Moreover, by the subdifferential inequality, we obtain
In addition, is convex in the second variable. Thus, one has
Lemma 1
([31]). Suppose that the Banach space E is of uniform convexity and are two sequences in E such that the first one is bounded. If , then .
Let be a mapping. We denote by the set of fixed points of S; that is, . A point is referred to as an asymptotic fixed point of S if s.t. and . We denote by the set of asymptotic fixed points of S. The notion of asymptotic fixed points was invented in Reich [32]. A self-mapping S on C is termed as Bregman’s relatively -demicontractive operator w.r.t. iff , and s.t. for each bounded satisfying ; the following holds:
with . In particular, putting for each , one has
In addition, if , then S reduces to a mapping of Bregman’s relative nonexpansivity w.r.t. ; that is, S is named as a mapping of Bregman’s relative nonexpansivity w.r.t. if and .
Definition 1.
Let C be a nonempty closed convex subset of E. A mapping is known as being
- (i)
- of monotonicity iff ;
- (ii)
- of pseudo-monotonicity iff ;
- (iii)
- ℓ-Lipschitz continuous or ℓ-Lipschitzian iff s.t. ;
- (iv)
- of weakly sequential continuity iff ; the relation holds: .
Lemma 2
([25]). Given a constant . If the function is of uniform convexity on any bounded subset of a Banach space E, then
and with , where is the gauge of uniform convexity of f.
Proof.
It is easy to show the conclusion. □
Lemma 3
([24]). Let and be two Banach spaces. Suppose that is uniformly continuous on bounded subsets of and D is a bounded subset of . Then, is bounded.
Lemma 4
([33]). Let with C being closed and convex in a Banach space E and let be of both pseudo-monotonicity and continuity. Given . Then, .
Lemma 5.
Given . Suppose that the Banach space E is of both smoothness and p-uniform convexity s.t. the duality mapping is of sequentially weak continuity. Let and . If exists for each , and . Then, is weakly convergent to an element of Ω.
Proof.
Using (5), we obtain . This ensures that is of boundedness. Hence, from the reflexivity of E, we have . Also, let us show the weak convergence of to a point in . Indeed, let with . Then, and s.t. and . From the sequentially weak continuity of , we obtain and . Note that . So, exploiting the convergence of the sequences and , we deduce that
which, hence, yields . From (5), we obtain . This arrives at a contradiction. Consequently, the sequence converges weakly to a point in . □
The lemma below was put forth in by [34]. It is easy to verify that the proof of the lemma in a Banach space E is actually the same as in . Here, we present the lemma but drop its demonstration.
Lemma 6.
Assume the nonempty with C being convex and closed in E. Suppose that , where is real-valued. If and h is Lipschitz continuous on C with modulus , then , where stands for the distance of y to K.
Lemma 7
([35]). Let be a sequence of real numbers that does not decrease at infinity in the sense that s.t. . Let the sequence of integers be defined as , with integer satisfying . Then, the following holds:
- (i)
- and ;
- (ii)
- and .
Lemma 8
([36]). Let be a sequence in satisfying , where and both are real sequences such that (i) and , and (ii) or . Then, .
Lemma 9
([37]). Let and be sequences of nonnegative real numbers satisfying the inequality . If and , and then exists.
3. Main Results
In this section, let E be a p-uniformly convex and uniformly smooth Banach space with . Let with C be closed and convex in E. We are now in a position to state and analyze our iterative algorithms for settling a pair of VIPs with CFPP constraint, where the pair of VIPs implicates two mappings of both uniform continuity and pseudomonotonicity and the CFPP involves finite mappings of Bregman’s relative nonexpansivity and a mapping of Bregman’s relative demicontractivity in E. Assume always that the conditions hold below:
- (C1)
- For , the self-mapping on C is of both uniform continuity and Bregman’s relative nonexpansivity and self-mapping on C is of both uniform continuity and Bregman’s relative -demicontractivity.
- (C2)
- is defined as for integer with the mod function taking values in the set ; i.e., if for some integers and , then if and if .
- (C3)
- For , is pseudomonotone and uniformly continuous on C, s.t. with .
- (C4)
- .
We will make use of Lemmas 10–13 below to derive our major outcomes in this paper.
Lemma 10.
Let be the constructed sequence in Algorithm 3. Then, the relations hold: and .
Proof.
Observe that the last two relations are similar. Then, it suffices to show that the latter relation holds. In fact, using the definition of and properties of , one has
Setting in the last inequality, from (5), we obtain
This completes the proof. □
Lemma 11.
Proof.
Observe that the rules (1) and (3) are similar. Then, it suffices to show that the latter rule (3) is valid. Using the uniform continuity of on C, from , one obtains . In case , it is evident that . In case , we know that s.t. (3) holds.
It is evident to see that, for each and are convex and closed. In what follows, we assert that lies in . Let . Using Lemma 2 and the Bregman relative -demicontractivity of , from , we obtain
which, hence, leads to . Meanwhile, by Lemma 4, one obtains . Therefore,
So, it follows from (3) that
Using Lemma 10, we have
This together with (11) arrives at
Consequently, lies in . So, is well defined. □
Lemma 12.
Suppose that and are the sequences generated by Algorithm 3. If and , then and .
Proof.
Observe that the last two inclusions are similar. Then, it suffices to show that the latter inclusion is valid. In fact, let . Whereby, we know that there exists a subsequence of , satisfying both and . Thus, one obtains . Noticing the convexity and closedness of C, from and , we obtain . In what follows, one discusses two aspects. In case , one has because . In case , by the condition on , one has . So, we could assume that . From (6), we obtain
and hence
Note that is uniformly continuous. Then, we know that is of boundedness by Lemma 3. Since is of boundedness as well, using the uniform continuity of on any bounded subset of E, from (12), we deduce that, for all x in C,
To show , one picks a positive s.t. . For every k, is denoted as the smallest satisfying
Since is decreasing, it is explicit that is increasing. For simplicity, is still written as . Noticing (due to ), we put and hence obtain . Indeed, it is evident that . So, by (14), one has . Again, from the pseudomonotonicity of , one has that, for all ,
Let us show . Indeed, since and , we obtain that
Hence, one obtains as . Thus, taking the limit as in (15), by condition (C2), one has . This implies that . □
Lemma 13.
Suppose that and are the sequences generated by Algorithm 3. Then, the following hold:
- (i)
- ;
- (ii)
- .
Proof.
Observe that the assertions (i) and (ii) are similar. Then, it suffices to show that assertion (ii) is valid. To verify assertion (ii), we consider two cases. In case , we might presume that s.t. , which, hence, arrives at
Combining (16) and attains .
In case , we presume . Whereby, one knows that s.t.
One puts . From (5) and , we have and hence
Because is of uniform continuity on any bounded subset of C, one obtains
From the step size rule (3) and the definition of , it follows that
Now, taking the limit as , from (18), we have . This, however, reaches a contradiction. So, it follows that is zero. □
In what follows, we intend to demonstrate the first convergence result in this paper.
Theorem 1.
Suppose that the Banach space E is of both p-uniform convexity and uniform smoothness s.t. is of sequentially weak continuity. If is the constructed sequence in Algorithm 3, then .
Proof.
It is clear that the necessity of Theorem 1 is valid. Next, it suffices to show that the sufficiency is valid. Assume that . Let . It is clear that . Using the definition of , we obtain . From (5) and (10) and the three point identity of , we obtain
where for some . Using Lemma 2, we obtain
Since , by Lemma 9, we deduce that exists. In addition, by the boundedness of , we conclude that , and are also bounded. Using (20), we obtain
which immediately yields
Since , and exist, it follows that , and , which, hence, yields . From , it can be readily seen that . Noticing , we obtain from and the definition of that
Since is bounded and E is reflexive, then we know that . In what follows, we claim that lies in . Let . Whereby, one knows that there exists a subsequence of such that converges weakly to z. From (21), one obtains . Since is of boundedness, one infers that such that . So, it follows that, for each ,
which implies that is -Lipschitzian. Using Lemma 6, one obtains
Noticing , from the definition of and (20), we have
Since and exists, we have , which immediately yields . Hence, from (21), we have
Furthermore, by Lemma 2, we obtain that
Therefore,
Taking the limit in the last inequality as , and using uniform continuity of on bounded subsets of E, (23) and , we obtain and hence . Since is uniformly continuous on any bounded subset of , we have
Now, let us show . By the boundedness of , one knows that there exists , satisfying . So, it follows that, for each ,
which guarantees that is -Lipschitzian. By Lemma 6, one obtains
Thus,
By Lemma 13, we obtain
Moreover, noticing and (21), we obtain that and . By Lemma 12, we deduce that and . Consequently,
Next, we claim that . Indeed, by (21), we immediately obtain
We first claim that . Indeed, by the formulation of , one obtains , which, hence, leads to . Note that
On the other hand, let us show the strongly convergent result for a pair of VIPs with CFPP constraint, where the two VIPs implicate two mappings of both uniform continuity and pseudomonotonicity and the CFPP involves finite mappings of Bregman’s relative nonexpansivity and a mapping of Bregman’s relative demicontractivity.
Theorem 2.
Suppose that the conditions (C1)–(C3) hold. If is the constructed sequence in Algorithm 4, then .
Proof.
It is clear that the necessity of the theorem is true. Next, it suffices to show that the sufficiency is valid. Assume that . In what follows, we divide our proof into four claims.
Using the same inferences as in the proof of Theorem 1, we know that
where for some . This ensures that is bounded.
| Algorithm 4: The 2nd modified inertial-type subgradient extragradient method |
| The 2nd modified inertial-type subgradient extragradient method. Initialization: Given arbitrarily and let and for . Choose and s.t. |
| , , and . Moreover, given the iterates and , choose s.t. , where and
|
| Iterations: Compute below: |
| Step 1. Put , and calculate , , and , with and being the smallest s.t.
|
| Step 2. Calculate , with and
|
| Step 3. Calculate , and , with and being the smallest s.t.
|
| Step 4. Set , and calculate and , with and
|
| Again set and return to Step 1. |
Using (10) and the last two inequalities, from and , we obtain
which immediately arrives at the desired claim. In addition, it is easily known that , and are also bounded.
Claim 2. We show that
Indeed, take . Using Lemma 2, one obtains
and
Set . From (9), we have
Furthermore, from (29), one has
This, along with (30), leads to
Consequently,
Claim 3. Let us show
Indeed, by the analogous reasonings to these of (26), one obtains
Claim 4. We show that as . Indeed, since E is reflexive and is bounded, we know that is nonempty. Let . Whereby, there exists a subsequence of such that converges weakly to z. One defines . In what follows, let us demonstrate in both possible aspects.
Aspect 1. Presume that there exists s.t. is non-increasing. Whereby, and, hence, . From (28) and (31), we obtain
which, hence, yields
Because and the sequence is of boundedness, one deduces that , and , which, hence, yields . From , it is easily known that . Noticing , we deduce from and the definition of that
According to the analogous reasonings, one obtains , which, hence, leads to (due to ). Using uniform continuity of on any bounded subset of , we obtain
This together with (34) implies that
It is clear that
Let us show that . Indeed, since , it can be readily seen that
So, it follows that and thus . This along with (37) arrives at
Observe that ,
Exploiting the uniform continuity of each , one deduces from (34) and (38) that , and . Hence, one has that . So, it follows that . This along with , attains . Consequently, z lies in . Additionally, from (36) and , one has that . Thus, using (35), we obtain . Consequently, ,
In what follows, we show that . From (33), we have
So, it follows that , and, hence,
By Lemma 13, one has that
Applying (40) and Lemma 12, one obtains that z lies in . Thus, we obtain . Consequently, . Lastly, we show that . We can pick a subsequence of such that
Because E is reflexive and is bounded, we might assume that converges weakly to . This, along with and (6), arrives at
In terms of (37), we have
Using uniform continuity of each on C and uniform continuity of on bounded subsets of E, from (38) and the boundedness of , we obtain
Noticing and , we infer that
Because lies in and diverges, by the application of Lemma 8 to (42), one obtains , and, hence, .
Aspect 2. Presume that s.t. for all . Let the indicate the natural-number set and be formulated below
In terms of Lemma 7, one obtains
Noticing and , we obtain that and
Noticing and using the analogous reasonings to those in Aspect 1, one obtains
This together with (44) implies that
Noticing , from (45), we obtain
Using the analogous reasonings to those in Aspect 1, one obtains ,
and
As a result, from (48), we deduce that
Again, using (43), one obtains . Thus, . This completes the proof. □
Remark 1.
It can be easily seen from the proof of Theorem 2 that, if the assumption that is used in place of the one that and , then Theorem 2 is still valid. It can be easily seen that the existing method in [25] is most closely related to our proposed method; that is, the hybrid projection method for resolving a single VIP with FPP constraint in [25] is extended to develop our modified inertial-type subgradient extragradient method for resolving a pair of VIPs with CFPP constraint. Compared with the corresponding results in [25], our results exhibit the novelty below:
First, the problem of finding a solution of a single VIP with FPP constraint (involving a Bregman relatively nonexpansive mapping) in [25] is extended to develop our problem of finding a solution of a pair of VIPs with CFPP constraint (involving finite Bregman relatively nonexpansive mappings and a Bregman relatively demicontractive mapping).
Second, the hybrid projection method with line-search process in [25] is extended to develop our modified inertial-type subgradient extragradient method with line-search process.
If we set , then Algorithm 3 reduces to the iterative algorithm for finding an element of .
Corollary 1.
Assume the conditions (C1)–(C3) with , hold, and . If is the fabricated sequence in Algorithm 5, and then .
| Algorithm 5: The 3rd modified inertial-type subgradient extragradient method |
The 3rd modified inertial-type subgradient extragradient method. Initialization: Given arbitrarily and let . Choose and s.t. , and . Moreover, given the iterates and , choose s.t. , where
Iterative steps: Calculate as follows: Step 1. Set , and calculate , , and , with and being the smallest s.t.
Step 2. Calculate , with and
Step 3. Calculate and , with . Again put and return to Step 1. |
Next, let be a Bregman relatively nonexpansive mapping and the identity mapping of E for . Then, we obtain . In this case, Algorithm 4 reduces to the following iterative scheme for solving a pair of VIPs and the FPP of . By Theorem 2, we obtain the following strong convergence result.
Corollary 2.
Suppose that the condition (C3) holds, and let . For initial , choose s.t. , where
Suppose that is the sequence constructed by
where and are the smallest nonnegative integers k and j satisfying, respectively,
and the sets are constructed below:
- (i)
- and ;
- (ii)
- and .
Then, .
4. Examples
In what follows, we furnish an illustrative instance to back up the practicability and implementability of the suggested approaches. Put , and for . We first provide an example of uniformly continuous and pseudomonotone mappings , Bregman relatively nonexpansive mapping and Bregman relatively demicontractive mapping with . Let and with the inner product and induced norm . The initial points are randomly chosen in C. For , let be defined as and for all . Now, we first show that is Lipschitz continuous and pseudomonotone. In fact, for each , one has
This ensures that is of Lipschitz continuity. Also, let us show that is of pseudomonotonicity. For each , it can be easily seen that
It is readily known that is Lipschitz continuous and monotone. Indeed, we deduce that and
Now, let and be defined as and . It is easy to verify that and is Bregman relatively nonexpansive. Also, is Bregman relatively -demicontractive with . Indeed, note that
Consequently,
In addition, putting , we obtain
In this case, the conditions (C1)–(C3) are satisfied.
Example 1.
Let and . Given the iterates and , choose s.t. , where
Algorithm 3 is rewritten as follows:
with the sets and the step-sizes being picked as in Algorithm 3 for each n. Then, by Theorem 1, we deduce that converges to .
Example 2.
Let and . Given the iterates and , choose s.t. , where
Algorithm 4 is rewritten as follows:
with the sets and the step-sizes are picked as in Algorithm 4 for each n. Then, by Theorem 2, we deduce that converges to .
It is worthy to emphasize that the above numerical example shows the competitive advantage of our suggested algorithms over the existing algorithms, e.g., the ones in [25]. In fact, we have provided the illustrative example of a pair of VIPs with CFPP constraint as above. Note that the existing method in [25] is only utilized for solving a single VIP with an FPP constraint. Hence, there is no way for this method to handle the above numerical example; that is, it is invalid for a pair of VIPs with a CFPP constraint. However, our suggested method can settle the above illustrative example. This reveals the competitive advantage of our proposed algorithms over the existing algorithms in the literature.
5. Conclusions
Let with and let E be a p-uniformly convex and uniformly smooth Banach space. Then, its dual space is q-uniformly smooth Banach space with . Utilizing the geometric properties of E and , we design two inertial-type subgradient extragradient algorithms with the line-search process for solving the pseudomonotone variational inequality problems (VIPs) and common fixed-point problem (CFPP), where the geometric properties involve the properties of the generalized duality mappings and Bregman projection operator . Here, the CFPP indicates the common fixed-point problem of finite Bregman’s relative nonexpansivity mappings and a Bregman’s relative demicontractivity mapping in E. Under the properties of the generalized duality mappings and Bregman projection operator , we have proved that the sequences generated by the suggested algorithms converge weakly and strongly to a solution of a pair of VIPs with a CFPP constraint, respectively. Additionally, an illustrated example is furnished to demonstrate the feasibility and implementability of our proposed approaches. Compared with the corresponding results in [25], our results reveal the novelty as follows: (a) the problem of finding a solution of a single VIP with an FPP constraint (involving Bregman relatively nonexpansive mapping) in [25] is extended to develop our problem of finding a solution of a pair of VIPs with a CFPP constraint (involving finite Bregman relatively nonexpansive mappings and a Bregman relatively demicontractive mapping); (b) the hybrid projection method with a line-search process in [25] is extended to develop our modified inertial-type subgradient extragradient method with the line-search process. In the end, it is worthy to point out that part of our future research is aimed at attaining the weak and strong convergence results for the modifications of our proposed approaches with Nesterov double inertial-type extrapolation steps (see [26]) and adaptive stepsizes.
Author Contributions
Conceptualization, B.L., H.-Y.H. and Y.-S.L.; Data curation, B.L.; Formal analysis, C.-S.W., B.L., S.-L.C. and Y.-S.L.; Funding acquisition, L.-C.C.; Investigation, C.-S.W., L.-C.C., B.L., S.-L.C., H.-Y.H. and Y.-S.L.; Methodology, L.-C.C., H.-Y.H. and Y.-S.L.; Project administration, L.-C.C.; Resources, C.-S.W.; Software, B.L. and S.-L.C.; Supervision, L.-C.C.; Validation, Y.-S.L.; Writing—original draft, C.-S.W., L.-C.C. and B.L.; Writing—review & editing, L.-C.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the 2020 Shanghai Leading Talents Program of the Shanghai Municipal Human Resources and Social Security Bureau (20LJ2006100), the Innovation Program of Shanghai Municipal Education Commission (15ZZ068) and the Program for Outstanding Academic Leaders in Shanghai City (15XD1503100).
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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