1. Introduction
In a real Hilbert space (), equipped with the inner product , we assume that C is a nonempty closed convex subset and is the metric projection of H onto C. If is a mapping on C, then we denote by the fixed-point set of S. Moreover, we denote by the set of all real numbers. Given a mapping . Consider the classical variational inequality problem (VIP) of finding such that for all . We denote by VI() the solution set of the VIP.
To the best of our knowledge, one of the most efficient methods to deal with the VIP is the extragradient method invented by Korpelevich [
1] in 1976, that is, for any given
,
is the sequence constructed by
with constant
. If
, one knows that this method has only weak convergence, and only requires that
A is monotone and
L-Lipschitzian. The literature on the VIP is vast, and Korpelevich’s extragradient method has received great attention from many authors, who improved it via various approaches so that some new iterative methods happen to solve the VIP and related optimization problems; see, e.g., [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12] and the references therein, to name but a few.
It is worth pointing out that the extragradient method needs to calculate two projections onto the feasible set
C per iteration. Without question, once one is hard to calculate the projection onto
C, the minimum distance problem has to be solved twice per iteration. This perhaps affects the applicability and implementability of the method. To improve Algorithm 1, one has to reduce the number of projections per iteration. In 2011, Censor et al. [
13] first suggested the subgradient extragradient method, in which the second projection onto
C is replaced by a projection onto a half-space:
where
A is a
L-Lipschitzian monotone mapping and
.
Since then, various modified extragradient-like iterative methods have been investigated by many researchers; see, e.g., [
14,
15,
16,
17,
18,
19]. In 2014, combining the subgradient extragradient method and Halpern’s iteration method, Kraikaew and Saejung [
20] proposed the Halpern subgradient extragradient method for solving the VIP, that is, for any given
,
is the sequence constructed by
where
and
. They proved the strong convergence of
to
.
In 2018, Thong and Hieu [
21] first suggested the inertial subgradient extragradient method, that is, for any given
, the sequence
is generated by
with constant
. Under suitable conditions, they proved the weak convergence of
to an element of
. Later, Thong and Hieu [
22] designed two inertial subgradient extragradient algorithms with linesearch process for solving a VIP with monotone and Lipschitz continuous mapping
A and a FPP of quasi-nonexpansive mapping
T with a demiclosedness property in
H. Under appropriate conditions, they established the weak convergence results for the suggested algorithms.
Suppose that the notations VIP and CFPP represent a variational inequality problem with Lipschitzian and pseudomonotone mapping and a common fixed-point problem of finitely many nonexpansive mappings and a quasi-nonexpansive mapping T with a demiclosedness property, respectively. Inspired by the research works above, we design two Mann-type inertial subgradient extragradient iterations for finding a common solution of the VIP and CFPP. Our algorithms require only computing one projection onto the feasible set C per iteration, and the strong convergence theorems are established without the assumption of sequentially weak continuity for A on C. Finally, in order to support the applicability and implementability of our algorithms, we make use of our main results to solve the VIP and CFPP in two illustrating examples.
This paper is organized as follows: In
Section 2, we recall some definitions and preliminaries for the sequel use.
Section 3 deals with the convergence analysis of the proposed algorithms. Finally, in
Section 4, in order to support the applicability and implementability of our algorithms, we make use of our main results to find a common solution of the VIP and CFPP in two illustrating examples.
3. Iterative Algorithms and Convergence Criteria
In this section, let the feasible set C be a nonempty closed convex subset of a real Hilbert space H, and assume always that the following hold:
is nonexpansive for and is a quasi-nonexpansive mapping such that is demiclosed at zero;
is L-Lipschitz continuous, pseudomonotone on H, and satisfies the condition that for , ;
with ;
is a contraction with constant , and is -strongly monotone and -Lipschitzian such that for ; , and are such that
- (i)
and ;
- (ii)
and , i.e., ;
- (iii)
and .
In addition, we write for integer with the mod function taking values in the set , i.e., if for some integers and , then if and if .
Algorithm 1. Initialization: Let and be arbitrary.
Iterative Steps: Calculate as follows:
Step 1. Given the iterates
and
, choose
such that
, where
Step 2. Compute and .
Step 3. Construct the half-space , and compute .
Step 4. Calculate
and
, and update
Let and return to Step 1.
Remark 1. It is easy to see that, from (5) we get . Indeed, we have , which together with implies that as .
Lemma 8. Let be generated by (6). Then is a nonincreasing sequence with , and .
Proof. First, from (6) it is clear that
. Furthermore, observe that
□
Remark 2. In terms of Lemmas 2 and 8, we claim that if or , then is an element of . Indeed, if or , then . Thus, the assertion is valid.
The following lemmas are quite helpful for the convergence analysis of our algorithms.
Lemma 9. Let be the sequences generated by Algorithm 1. Then Proof. First, by the definition of
we claim that
Indeed, if
, then inequality (8) holds. Otherwise, from (6) we get (8). Furthermore, observe that for each
,
which hence yields
From
, we get
. By the pseudomonotonicity of
A on
C we have
. Putting
we get
. Thus,
Substituting (10) for (9), we obtain
Since
, we get
, and hence
which together with (8), implies that
Therefore, substituting the last inequality for (11), we infer that inequality (7) holds. □
Lemma 10. Suppose that , and are bounded sequences generated by Algorithm 1. If , and s.t. , then .
Proof. Utilizing the similar arguments to those in the proof of Lemma 3.3 of [
12], we can derive the desired result. □
Lemma 11. Assume that are the sequences generated by Algorithm 1. Then they all are bounded.
Proof. Since
and
, we may assume, without loss of generality, that
Choose a fixed
arbitrarily. Then we obtain
and
for all
, and (7) holds. Noticing
, we might assume that
for all
. So it follows from (7) that for all
,
In terms of Remark 1, one has
as
. Hence we deduce that
s.t.
Using (13)–(15), we obtain that for all
,
Noticing
, we have
for all
. So, using Lemma 6 and (16) we deduce that
and hence
By induction, we obtain . Thus, is bounded, and so are the sequences . □
Theorem 1. Let the sequence be constructed by Algorithm 1. Then converges strongly to the unique solution of the following VIP: Proof. First, it is not difficult to show that
is a contraction. In fact, by Lemma 6 and the Banach contraction mapping principle, we obtain that
has a unique fixed point. Say
, i.e.,
. Thus, the following VIP has only a solution
:
□
We now claim that
for some
. In fact, observe that
Using Lemma 6 and the convexity of the function
, we have
where
for some
. From (7) and (17), we have
Again from (16), we obtain
where
for some
. Using (19) and (20), we get
where
. Consequently,
Next we claim that
for some
. In fact, it is easy to see that
Using (16), (18), and (22), we get
where
for some
.
Then (23) can be rewritten as the following formula:
We next show the convergence of to zero by the following two cases:
Case 1.Suppose that there exists an integer such that is non-increasing. Then
Since
and
, we have
Using Lemma 1 (v), we deduce from (16) that
which immediately yields
Since
,
and
, we have
Using Lemma 1 (v) again, we have
So it follows from (26) and
that
Therefore, from (25)–(27), we conclude that
and
Next, by the boundedness of
, we know that
s.t.
Further we might assume that
. So, from (31) we have
Noticing
and
, we obtain
. Since
(due to (25) and (28)–(30)) and
, by Lemma 10 we get
. So it follows from (17) and (32) that
which hence yields
Since
, and
by Lemma 4 we conclude from (23) that
.
Case 2.Suppose that s.t. , where is the set of all positive integers. Define the mapping
by
Putting
and using the same inference as in Case 1, we can obtain
and
Because of
and
, we conclude from (23) that
and hence
Taking into account
, we have
It is easy to see from (35) that
as
. This completes the proof.
Next, we introduce another Mann-type inertial subgradient extragradient algorithm.
Algorithm 2. Initialization: Let and be arbitrary.
Iterative Steps: Calculate as follows:
Step 1. Given the iterates
and
, choose
such that
, where
Step 2. Compute and .
Step 3. Construct the half-space , and compute .
Step 4. Calculate
and
, and update
Let and return to Step 1.
It is worth pointing out that Lemmas 8–11 are still valid for Algorithm 2.
Theorem 2. Let the sequence be constructed by Algorithm 2. Then converges strongly to the unique solution of the following VIP: Proof. Utilizing the same arguments as in the proof of Theorem 1, we deduce that there exists a unique solution to the VIP (17). □
We now claim that
for some
. In fact, observe that
where
. Using the similar arguments to those of (19) and (20), we have
and
where
for some
and
for some
. Combining the last inequalities, we obtain
where
. This ensures that (39) holds.
Next we claim that
for some
. In fact, using the similar arguments to those of (22) and (23), we have
and
where
for some
.
Then (41) can be rewritten as the following formula:
We next show the convergence of to zero by the following two cases:
Case 3.Suppose that there exists an integer such that is non-increasing. Then
Using the similar arguments to those of (25), we have
Using Lemma 1 (v), we get
which immediately yields
Since
and
, we have
So, from (43)–(45) we infer that
and
In addition, using the similar arguments to those of (33) and (34), we have
and hence
Consequently, applying Lemma 4 to (41), we have .
Case 4.Suppose that s.t. , where is the set of all positive integers. Define the mapping by In the remainder of the proof, using the same arguments as in Case 2 of the proof of Theorem 1, we obtain the desired assertion. This completes the proof.
It is markable that our results improve and extend the corresponding results of Kraikaew and Saejung [
20] and Ceng et al. [
11], in the following aspects.
(i) Our problem of finding an element of
includes as a special case the problem of finding an element of
in [
20], where
are nonexpansive and
is quasi-nonexpansive. It is worth mentioning that Halpern’s subgradient extragradient method for solving the VIP in [
20] is extended to develop our Mann-type inertial subgradient extragradient rule for solving the VIP and CFPP, in which
A is
L-Lipschitz continuous, pseudomonotone on
H, but it is not required to be sequentially weakly continuous on
C.
(ii) Our problem of finding an element of
includes as a special case the problem of finding an element of
in [
11], where in [
11],
A is required to be
L-Lipschitz continuous, pseudomonotone on
H, and sequentially weakly continuous on
C. The modified inertial subgradient extragradient method for solving the VIP and CFPP in [
11] is extended to develop our Mann-type inertial subgradient extragradient rule for solving the VIP and CFPP, where
is nonexpansive for
and
is quasi-nonexpansive.