Abstract
Some new inertial forward-backward projection iterative algorithms are designed in a real Hilbert space. Under mild assumptions, some strong convergence theorems for common zero points of the sum of two kinds of infinitely many accretive mappings are proved. New projection sets are constructed which provide multiple choices of the iterative sequences. Some already existing iterative algorithms are demonstrated to be special cases of ours. Some inequalities of metric projection and real number sequences are widely used in the proof of the main results. The iterative algorithms have also been modified and extended from pure discussion on the sum of accretive mappings or pure study on variational inequalities to that for both, which complements the previous work. Moreover, the applications of the abstract results on nonlinear capillarity systems are exemplified.
Keywords:
m-accretive mapping; strongly positive mapping; μ-inversely strongly accretive mapping; τ-Lipschitz continuous mapping; variational inequalities; capillarity systems MSC:
47H05; 47H09
1. Introduction and Preliminaries
Suppose H is a real Hilbert space with norm and inner product . Let K be the non-empty closed and convex subset of We use → and ⇀ to denote the strong and weak convergence in respectively.
We know that Hilbert space H satisfies Opial’s condition in the sense that for with and (see [1]).
The inclusion problem for finding such that
is studied intensively, where is a mapping and is a multi-valued mapping. This is mainly because many problems appear in convex programming, variational inequalities, split feasibility problems, minimization problem, inverse problem and image processing can be modeled by (1).
A mapping is said to be an accretive mapping (see [2]) if for each there exist and such that An accretive mapping is said to be m-accretive if for
A mapping is said to be -inversely strongly accretive mapping (see [3]) if for each and
For a mapping a point is called a zero point of W if The set of zero points of W is denoted by If satisfies that then x is called a fixed point of W. The set of fixed points of W is denoted by
The study of the special case of inclusion problem (1), where T is accretive and S is -inversely strongly accretive, has been a hot topic during the past few years. In particular, the constructions of the iterative algorithms for approximating the zero point of the sum of T and S are focused, see [3,4,5,6,7,8,9,10,11,12] and the references therein. The inertial forward-backward splitting method is one of the important iterative algorithms studied by some authors, see [7,8,9,13,14].
In 2015, Lorenz and Pock [9] proposed the following inertial forward-backward algorithm for approximating zero points of , where is m-accretive and is -inversely strongly accretive:
In addition, the result that as is proved under some conditions.
To get strong convergence, Dong et al. proposed the following inertial forward-backward projection algorithm in Hilbert spaces in [14]:
where T and S are the same as those in (2) and is the metric projection whose meaning can be seen in Definition 1. The projection sets and play an important role in the iterative construction to ensure the strong convergence. The result that as is proved under some conditions.
In 2018, Khan et al. proposed the following one in which the projection set is deleted (see [7]):
where T and S are the same as those in (3). The strong convergence that as is also obtained under some conditions.
On the other hand, the inclusion problem (1) is extended to the system of inclusion problems:
where is m-accretive and is -inversely strongly accretive for or In addition, some iterative algorithms for approximating common zero points of are constructed in [3,15,16,17]. In particular, Wei et al. proposed the following implicit mid-point forward-backward projection algorithm in [17]:
where is a contraction, is strongly positive linear bounded mapping, is the computational error, is m-accretive and is -inversely strongly accretive for The result that as is proved under some conditions.
Recall that is called a contraction (see [17]) if there exists a constant such that for
A mapping is called strongly positive (see [17]) if there exists such that for In this case,
where I is the identity mapping, and
A mapping is said to be non-expansive (see [17]) if for each
In 2018, Wei et al. proposed some new hybrid iterative algorithms to approximate the common element of the set of zero points of infinitely many m-accretive mappings and the set of fixed points of infinitely many non-expansive mappings . A special case (see Corollary 3.6 in [18]) is presented as follows:
The result that as is proved under some conditions. We may notice that infinite choices of can be made, which is totally different from traditional projection iterative algorithms, e.g., (3).
In 2016, Wei et al. proposed an implicit forward-backward mid-point iterative algorithm for approximating common zero points of where is m-accretive and is -inversely strongly accretive, for A special case in [19] in the frame of Hilbert space is presented as follows:
where f and F are the same as those in (6), , and are the error sequences. Under some conditions, is proved to be convergent strongly to which also satisfies the following variational inequality:
We may notice that the connection between the common element of for and the solution of one kind variational inequality is set up in [19].
In this paper, our main purpose is formulated as follows: (1) obtain strong convergence theorems instead of weak ones; (2) construct new projection sets, which ensure that infinitely many iterative sequences can be generated compared to traditional projection iterative algorithms (3), (4) and (6); (3) inject the idea of inertial forward-backward algorithm into the iterative construction, compared to iterative algorithms (6)–(8); (4) set up the connection between the common zero point of the sum of two kinds of infinitely many accretive mappings and the solution of one kind variational inequality, which complements the corresponding work since rare studies of the projection iterative algorithms (e.g., (3)–(7)) have mentioned that; (5) provide the application of the abstract result to capillarity systems.
To begin our study, we need some preliminaries.
Definition 1.
(see [2]) For the Hilbert space H and its non-empty closed and convex subset K, there exists a unique point such that for each In this case, the metric projection mapping is defined by , for
Definition 2.
(see [20]) Let be a sequence of non-empty closed and convex subsets of H. Then
- (1)
- the strong lower limit of , is defined as the set of all such that there exists for almost all n and it tends to x as in the norm;
- (2)
- the weak upper limit of is defined as the set of all such that there exists a subsequence of and for every and it tends to x as in the weak topology;
- (3)
- the limit of is the common value when .
Lemma 1.
(see [20]) Let be a decreasing sequence of closed and convex subsets of H, i.e., if Then converges in H and
Lemma 2.
(see [21]) Suppose H is a real Hilbert space. If exists and is not empty, then for every as
Lemma 3.
(see [2,19]) If is accretive, then is non-expansive.
Lemma 4.
(see [22]) If H is a real Hilbert space with K its non-empty closed and convex subset, is non-expansive for and for then is non-expansive with under the assumption that
Lemma 5.
(see [19]) If H is a real Hilbert space with K its non-empty closed and convex subset, is a single-valued mapping and is an m-accretive mapping, then
for
Lemma 6.
(see [23]) Let H be a real Hilbert space and . Then there exists a continuous, strictly increasing and convex function with such that for with and .
Lemma 7.
(see [24]) Let K be the non-empty closed and convex subset of Hilbert space H and be the metric projection. Then
- (1)
- for and
- (2)
- if and only if there holds the following inequality for
Lemma 8.
(see [25]) If is a contraction, then there is a unique element such that
2. Some Inertial Forward-Backward Algorithms
In this section, unless otherwise stated, we always assume that:
- (1)
- H is a real Hilbert space;
- (2)
- is -inversely strongly accretive and is m-accretive, for each . In addition,
- (3)
- is the computational error;
- (4)
- , and are three real number sequences in for ;
- (5)
- , and are three real number sequences in with for
- (6)
- is a real number sequence in with for
- (7)
- is a real number sequence in for some
2.1. New Inertial Forward-Backward Projection Algorithms
Theorem 1.
Let be generated by the following iterative algorithm:
Under the assumptions that: for as ; there exists such that for we have: as
Proof.
We split the proof into nine steps.
Step 1. is non-expansive, for
The proof of Step 1 is essentially from that of Step 1 in Theorem 2.1 of [17]. For the sake of completeness, we present it as follows.
Since then for each
Thus, is non-expansive, for It then follows from Lemmas 3 and 4 that is non-expansive, for Combining with Lemma 5,
Step 2. for
If it is obvious that
Now, suppose the result is true for then if it follows from (10) that
Using Step 1, we have:
Combining (11) and (12),
which ensures that Then by induction, for
Step 3. is a closed and convex subset of for each
It is not difficult to see that
Then is a closed and convex subset of for each
Step 4. is non-empty for each which ensures that is well-defined.
From Step 3 and the definition of metric projection, for there exists such that Thus, for And then is well-defined.
Step 5. as .
The proof of Step 5 is similar to Step 2 of Theorem 3.1 in [18]. It follows from Lemma 1 that exists and . Then Lemma 2 implies that as .
Step 6. as .
Since and is a convex subset of H, then for which implies that
Using Lemma 6, we have
Then (13) and (14) ensure that Letting first and then , we know that as From Step 5, as .
Step 7. and as .
Since and then from (10), as . Thus, as . Since , then
as . Thus, as .
Step 8.
In fact, if, otherwise, Then
Since then as
Since then there exists a subsequence of , which is still denoted by such that as
Thus, as
Since H satisfies Opial’s condition, then
which makes a contradiction! Thus,
Step 9.
From Step 8,
On the other hand, since , then Thus,
Using Lemma 7, we have
which implies that □
Remark 1.
Compared to (3) and (4), infinitely many m-accretive mappings and infinitely many -inversely strongly accretive mappings are considered in (10). Compared to (6), the idea of inertial forward-backward algorithm is embodied in (10). Compared to (3), (4) and (6), infinite choices of the iterative sequences are defined.
Remark 2.
The traditional idea for choosing the unique iterative element as the metric projection of the initial element in iterative algorithms (3), (4) and (6) is contained in the ideas of (10) in our paper.
In fact, if take we can easily see that
Thus, which means that this is a kind of choice of (10).
Corollary 1.
If then (10) in Theorem 1 becomes to the traditional forward-backward iterative algorithm:
Corollary 2.
If then (10) in Theorem 1 becomes to the following iterative algorithm:
Remark 3.
Let for After taking in (15), we may see that can be deleted which implies that (15) reduces to (4). However, the strong assumption that in [7] is no longer needed in our paper.
2.2. New Mid-Point Inertial Forward-Backward Projection Algorithms
Theorem 2.
Suppose is a contraction with and is a strongly positive linear bounded operator with coefficient Let be generated by the following iterative algorithm:
Under the assumptions of in Theorem 1 and and as we have: as
Proof.
We split the proof into ten steps.
Step 1. is non-expansive, for
Copy Step 1 in Theorem 1.
Step 2. is well-defined.
Define by
where is non-expansive and
It is easy to check that U is a contraction since
for
In view of Lemma 8, there exists a unique element such that Combining with the result of Step 1, is well-defined.
Step 3. for
If it is obvious that
Now, suppose the result is true for then if in view of Lemma 4, we have:
which ensures that
It follows from (16) that
Combining (17) and (18),
Thus, Then by induction, for
Step 4. is a closed and convex subset of for each It is not difficult to see that
Then is a closed and convex subset of for each
Step 5. is non-empty for each which ensures that is well-defined.
Copy Step 4 in Theorem 1.
Step 6. as .
Copy Step 5 in Theorem 1.
Step 7. as .
Copy Step 6 in Theorem 1.
Step 8. , and as .
Since and then it is easy to see that , as .
Since then as .
Since and then
Thus, as .
Step 9.
In fact, if, otherwise, Then
Since then as
Since then there exists a subsequence of , which is still denoted by such that as
Thus, as
Since H satisfies Opial’s condition, then
which makes a contradiction. Thus,
Step 10.
From Step 9,
On the other hand, since , then Thus,
Using Lemma 7, we have
which implies that □
Remark 4.
Similar to Remark 2, is also a possible choice of in Theorem 2.
Corollary 3.
If , then (16) becomes to the following traditional mid-point inertial forward-backward projection iterative algorithm:
If, moreover, in (19), then it becomes to the following traditional forward-backward mid-point iterative algorithm:
Corollary 4.
If in (15), then it becomes to the following one:
If, moreover, take in (21), then it becomes to (6).
2.3. Relationship with Variational Inequalities
A lot work has been done on designing iterative algorithms to approximate solution of variational inequalities due to their wide applications (e.g., [26,27]). A classical variational inequality is to find such that for
where is a nonlinear mapping. The symbol denotes the solution of the above variational inequality.
2.3.1. The First Kind Iteration Theorems
Definition 3.
Let H be a real Hilbert space with K being its non-empty closed and convex subset. is called a τ-Lipschitz continuous mapping if for
Theorem 3.
Let H be a real Hilbert space with K being its non-empty closed and convex subset. Suppose is -inversely strongly accretive and is m-accretive, for each . Suppose is an accretive and τ-Lipschitz continuous mapping. Let be generated by the following iterative algorithm:
Under the assumptions of (i)–(iv) in Theorem 1 and the additional assumptions we have: as
Proof.
We split the proof into nine steps.
Step 1. is non-expansive, for
Copy the proof of Step 1 in Theorem 1.
Step 2. for
We first show that
In fact, in view of Lemma 7, we have:
which implies that (24) is true.
Next, we can easily check the following by noticing the result of Step 1 and (24):
Thus, by induction as that in Theorem 1, , for .
Step 3. is a closed and convex subset of for each
It is not difficult to see that
Then is a closed and convex subset of for each
Copy the results of Steps 4–6 in Theorem 1, we have:
Step 4. is non-empty for each which ensures that is well-defined.
Step 5. as .
Step 6. as .
Step 7. , and as .
It is easy to see that is a closed subset of K. Then and imply that Therefore,
as . Thus, as .
Since and then as .
Since then from (23), as . Thus, as .
Step 8.
We shall first show that
For this, define
where is the normal cone to K at It is well-known that is m-accretive and if and only if [28].
Let then From the definition of the normal cone, we have
From Lemma 7, we have:
which implies that
In view of (25) and (26), we know that
Taking limit on both sides of the above inequality, we have: which implies from the fact B is m-accretive that and then
Next, we shall show that
In fact, if, otherwise, Then
Since then as
Since then there exists a subsequence of , which is still denoted by such that as
Thus, as From Step 1 and , we can also know that as
Since H satisfies Opial’s condition, and , then
which makes a contradiction! Thus,
Step 9.
From Step 8,
On the other hand, since , then
Thus
Using Lemma 7, we have
which implies that
This completes the proof. □
2.3.2. The Second Kind Iteration Theorems
The following result is a special result of Lemma 10 in [19]:
Theorem 4.
Let H be a real Hilbert space and K be a non-empty closed and convex subset of H. Suppose is a contraction with is a strongly positive linear bounded operator with coefficient ξ and is non-expansive mapping. If , then there exists which satisfies for Moreover, as and satisfies the following variational inequality: for
In Lemma 10 of [19], we can also know that the solution of the variational inequality (27) is unique.
Theorem 5.
Under the assumptions of Theorem 2, generated by (16) converges strongly to . Set If then satisfies the following variational inequality:
Proof.
It follows from Lemma 7 that Since Theorem 4 tells us that (28) has a unique solution, then we know that generated by (16) converges strongly to the unique solution of variational inequality (28). □
Remark 5.
The assumption that is reasonable. For example, we may take and for
Remark 6.
For projection iterative algorithms such as (16), rare work can be found to show that the limit of the iterative sequences is also the solution of a kind of variational inequalities.
3. Applications
3.1. Preparation for Discussion of Capillarity Systems
To present some examples in this section, we need some basic definitions in Banach spaces.
Let E be a real Banach space with being its dual space and let denote the generalized duality pairing between E and .
Definition 4.
(see [29]) Recall that is called the normalized duality mapping if
Definition 5.
(see [29]) Recall that is said to be a monotone mapping if one has
A monotone mapping is said to be maximal monotone if , .
Definition 6.
(see [29]) Recall that a mapping is said to be coercive if with then
Definition 7.
(see [29]) Recall that a mapping is said to be a hemi-continuous mapping if , as for
Definition 8.
(see [29]) is said to be a proper convex functional if there exists such that and for and is said to be lower-semi-continuous: for The subdifferential of ψ, is defined by:
3.2. Applications to Capillarity Elliptic Systems
Example 1.
Suppose Ω is bounded conical domain in with ϑ is the exterior normal derivative of Γ, is a non-negative constant, is a positive number, is a given function, for . In addition, is the subdifferential of , where for each . Suppose . If then and if then for
The following capillarity system is studied in [30]:
where and denote the norm and inner product in respectively.
The study on (29) in [30] is based on the following assumptions.
- (1)
- is a proper convex and lower-semi-continuous mapping with .
- (2)
- , is measurable for .
- (3)
- For each satisfies Caratheodory’s conditions and satisfies thatfor
By using splitting method, the sufficient condition that (29) has non-trivial solution is obtained:
Theorem 6.
(see [30]) If satisfies that
for then is the non-trivial solution of capillarity system (29).
Based on Example 1, we present the following example:
Example 2.
Suppose Ω, Γ and ϑ are the same as those in Example 1. Suppose , . If then suppose and if then suppose for
Now, we will discuss the following capillarity systems.
Please note that (30) is the extension from the finite case of (29) to that for infinite case. However, both the capillarity equations and the boundary conditions are the special case of (29) in the sense that and for and for
Lemma 9.
(see [30]) The mapping defined by
for is everywhere defined, hemi-continuous, monotone and coercive, for each .
Lemma 10.
(see [30]) Define by
For , . Then is m-accretive, for each .
Lemma 11.
(see [30]) The mapping defined by
is -inversely strongly accretive, for , for
Theorem 7.
If then is the solution of capillarity system (30). Moreover,
Proof.
It is easy to see that is the solution of capillarity system (30) and Now, we shall show that
In fact, if and then which implies that
Thus, and then is a singleton. Since then the result follows. □
Theorem 8.
Let for Suppose and are the same as those in Lemmas 10 and 11, respectively. Let be any strongly positive linear bounded operator with coefficient and be a contraction with coefficient Constructing the following iterative algorithm:
Under the assumptions of Theorem 2, using the result of Theorem 5, one has which is the unique solution of capillarity system (30) and satisfies the following variational inequality: For
Remark 7.
From Theorem 8 we can easily see the relationship among the solution of capillarity system, the solution of variational inequality and the zero of sum of infinitely many m-accretive mappings and infinitely many -inversely strongly accretive mappings.
Author Contributions
Funding
Supported by the National Natural Science Foundation of China (11071053), Natural Science Foundation of Hebei Province (A2014207010), Key Project of Science and Research of Hebei Educational Department (ZD2019073), Key Project of Science and Research of Hebei University of Economics and Business (2018ZD06), Youth Project of Science and Research of Hebei University of Economics and Business (2017KYQ09) and Youth Project of Science and Research of Hebei Educational Department (QN2017328).
Acknowledgments
Thanks for the reviewers’ valuable opinions and careful work.
Conflicts of Interest
The authors declare that they have no competing interests.
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