Abstract
In a real uniformly convex and p-uniformly smooth Banach space, a modified forward-backward splitting iterative algorithm is presented, where the computational errors and the superposition of perturbed operators are considered. The iterative sequence is proved to be convergent strongly to zero point of the sum of infinite m-accretive mappings and infinite -inversely strongly accretive mappings, which is also the unique solution of one kind variational inequalities. Some new proof techniques can be found, especially, a new inequality is employed compared to some of the recent work. Moreover, the applications of the newly obtained iterative algorithm to integro-differential systems and convex minimization problems are exemplified.
Keywords: p-uniformly smooth Banach space, -inversely strongly accretive mapping, -strongly accretive mapping, -strictly pseudo-contractive mapping, perturbed operator
Introduction and preliminaries
Let X be a real Banach space with norm and be its dual space. ‘→’ denotes strong convergence and is the value of at .
The function is called the modulus of smoothness of X if it is defined as follows:
A Banach space X is said to be uniformly smooth if , as . Let be a real number, a Banach space X is said to be p-uniformly smooth with constant if such that for . It is well known that every p-uniformly smooth Banach space is uniformly smooth. For , the generalized duality mapping is defined by
In particular, is called the normalized duality mapping.
For a mapping , we use and to denote its fixed point set and zero point set, respectively; that is, and . The mapping is said to be
- non-expansive if
- contraction with coefficient if
-
accretive [1, 2] if for all , , where ;
m-accretive if T is accretive and for ;
- θ-inversely strongly accretive [3] if for , , there exists such that
- μ-strictly pseudo-contractive [4] if for each , there exists such that
for some .
If T is accretive, then for each , the non-expansive single-valued mapping defined by is called the resolvent of T [1]. Moreover, .
Let D be a nonempty closed convex subset of X and Q be a mapping of X onto D. Then Q is said to be sunny [5] if for all and . A mapping Q of X into X is said to be a retraction [5] if . If a mapping Q is a retraction, then for every , where is the range of Q. A subset D of X is said to be a sunny non-expansive retract of X [5] if there exists a sunny non-expansive retraction of X onto D and it is called a non-expansive retract of X if there exists a non-expansive retraction of X onto D.
It is a hot topic in applied mathematics to find zero points of the sum of two accretive mappings, namely, a solution of the following inclusion problem:
| 1.1 |
For example, a stationary solution to the initial value problem of the evolution equation
| 1.2 |
can be recast as (1.1). A forward-backward splitting iterative method for (1.1) means each iteration involves only A as the forward step and B as the backward step, not the sum . The classical forward-backward splitting algorithm is given in the following way:
| 1.3 |
Some of the related work can be seen in [6–8] and the references therein.
In 2015, Wei et al. [9] extended the related work of (1.1) from a Hilbert space to the real smooth and uniformly convex Banach space and from two accretive mappings to two finite families of accretive mappings:
| 1.4 |
where D is a nonempty, closed and convex sunny non-expansive retract of X, is the sunny non-expansive retraction of E onto D, is the error, and are m-accretive mappings and θ-inversely strongly accretive mappings, respectively, where . is a strongly positive linear bounded operator with coefficient γ̅ and is a contraction. , . The iterative sequence is proved to converge strongly to , which solves the variational inequality
| 1.5 |
for under some conditions.
The implicit midpoint rule is one of the powerful numerical methods for solving ordinary differential equations, and it has been extensively studied by Alghamdi et al. They presented the following implicit midpoint rule for approximating the fixed point of a non-expansive mapping in a Hilbert space H in [10]:
| 1.6 |
where T is non-expansive from H to H. If , then they proved that converges weakly to under some conditions.
Combining the ideas of forward-backward method and midpoint method, Wei et al. extended the study of two finite families of accretive mappings to two infinite families of accretive mappings [3] in a real q-uniformly smooth and uniformly convex Banach space:
| 1.7 |
where , and are three error sequences, and are m-accretive mappings and -inversely strongly accretive mappings, respectively, where . is a strongly positive linear bounded operator with coefficient γ̅, is a contraction, , , for . The iterative sequence is proved to converge strongly to , which solves the following variational inequality:
| 1.8 |
In 2012, Ceng et al. [11] presented the following iterative algorithm to approximate zero point of an m-accretive mapping:
| 1.9 |
where is a γ-strongly accretive and μ-strictly pseudo-contractive mapping, with , is a contraction and is m-accretive. Under some assumptions, is proved to be convergent strongly to the unique element , which solves the following variational inequality:
| 1.10 |
The mapping F in (1.9) is called a perturbed operator which only plays a role in the construction of the iterative algorithm for selecting a particular zero of A, and it is not involved in the variational inequality (1.10).
Inspired by the work mentioned above, in Section 2, we shall construct a new modified forward-backward splitting midpoint iterative algorithm to approximate the zero points of the sum of infinite m-accretive mappings and infinite -inversely strongly accretive mappings. New proof techniques can be found, the superposition of perturbed operators is considered and some restrictions on the parameters are mild compared to the existing similar works. In Section 3, we shall discuss the applications of the newly obtained iterative algorithms to integro-differential systems and the convex minimization problems.
We need the following preliminaries in our paper.
Lemma 1.1
[12]
Let X be a real uniformly convex and p-uniformly smooth Banach space with constant for some . Let D be a nonempty closed convex subset of X. Let be an m-accretive mapping and be a θ-inversely strongly accretive mapping. Then, given , there exists a continuous, strictly increasing and convex function with such that for all with and ,
In particular, if , then is non-expansive.
Lemma 1.2
[13]
Let X be a real smooth Banach space and be a μ-strictly pseudo-contractive mapping and also be a γ-strongly accretive mapping with . Then, for any fixed number , is a contraction with coefficient .
Lemma 1.3
[2]
Let X be a real Banach space and D be a nonempty closed and convex subset of X. Let be a contraction. Then f has a unique fixed point.
Lemma 1.4
[14]
Let X be a real strictly convex Banach space, and let D be a nonempty closed and convex subset of X. Let be a non-expansive mapping for each . Let be a real number sequence in (0,1) such that . Suppose that . Then the mapping is non-expansive and .
Lemma 1.5
[12]
In a real Banach space X, for , the following inequality holds:
Lemma 1.6
[15]
Let X be a real Banach space, and let D be a nonempty closed and convex subset of X. Suppose is a single-valued mapping and is m-accretive. Then
Lemma 1.7
[16]
Let be a real sequence that does not decrease at infinity, in the sense that there exists a subsequence so that for all . For every , define an integer sequence as
Then as and for all , .
Lemma 1.8
[17]
For , the following inequality holds:
for any positive real numbers a and b.
Lemma 1.9
[18]
The Banach space X is uniformly smooth if and only if the duality mapping is single-valued and norm-to-norm uniformly continuous on bounded subsets of X.
Strong convergence theorems
Theorem 2.1
Let X be a real uniformly convex and p-uniformly smooth Banach space with constant where and D be a nonempty closed and convex sunny non-expansive retract of X. Let be the sunny non-expansive retraction of X onto D. Let be a contraction with coefficient , be m-accretive mappings, be -inversely strongly accretive mappings, be -strictly pseudo-contractive mappings and -strongly accretive mappings with for . Suppose and are real number sequences in for . Suppose for and , for , , and . If, for each , we define by
then has a fixed point . Moreover, if , then converges strongly to the unique solution of the following variational inequality, as :
| 2.1 |
Proof
We split the proof into five steps.
Step 1. is a contraction for , and .
In fact, for , using Lemmas 1.1 and 1.2, we have
which implies that is a contraction. By Lemma 1.3, there exists such that . That is, .
Step 2. If , then is bounded for , , where a̅ is a sufficiently small positive number and is the same as that in Step 1.
For , using Lemmas 1.1, 1.2 and 1.6, we know that
Then
Since , then there exists a sufficiently small positive number a̅ such that for . Thus is bounded for and .
Step 3. If , then , as , for .
Noticing Step 2, we have
as .
Step 4. If the variational inequality (2.1) has solutions, the solution must be unique.
Suppose and are two solutions of (2.1), then
| 2.2 |
and
| 2.3 |
Adding up (2.2) and (2.3), we get
| 2.4 |
Since
then (2.4) implies that .
Step 5. If , then , as , which solves the variational inequality (2.1).
Assume . Set and define by
where LIM is the Banach limit on . Let
It is easily seen that K is a nonempty closed convex bounded subset of X. Since from Step 3, then for ,
it follows that ; that is, K is invariant under . Since a uniformly smooth Banach space has the fixed point property for non-expansive mappings, has a fixed point, say , in K. That is, , which ensures from Lemmas 1.4 and 1.6 that . Since is also a minimizer of μ over X, it follows that, for ,
Since X is uniformly smooth, then by letting , we find the two limits above can be interchanged and obtain
| 2.5 |
Since , then
Therefore,
| 2.6 |
Since , then from (2.5), (2.6) and the result of Step 2, we have , which implies that , and then there exists a subsequence which is still denoted by such that .
Next, we shall show that solves the variational inequality (2.1).
Note that , then for ,
as . Since and J is uniformly continuous on each bounded subset of X, then taking the limits on both sides of the above inequality, , which implies that satisfies the variational inequality (2.1).
Next, to prove the net converges strongly to , as , suppose that there is another subsequence of satisfying as . Denote by . Then the result of Step 3 implies that , which ensures that in view of Lemmas 1.4 and 1.6. Repeating the above proof, we can also know that solves the variational inequality (2.1). Thus by using the result of Step 4.
Hence , as , which is the unique solution of the variational inequality (2.1).
This completes the proof. □
Theorem 2.2
Let X be a real uniformly convex and p-uniformly smooth Banach space with constant where and D be a nonempty closed and convex sunny non-expansive retract of X. Let be the sunny non-expansive retraction of X onto D. Let be a contraction with coefficient , be m-accretive mappings, be -inversely strongly accretive mappings, and be -strictly pseudo-contractive mappings and -strongly accretive mappings with for . Suppose , , , , , , , and are real number sequences in , , and are error sequences, where and . Suppose . Let be generated by the following iterative algorithm:
| 2.7 |
Under the following assumptions:
-
(i)
, for ;
-
(ii)
;
-
(iii)
, , , , ;
-
(iv)
, ;
-
(v)
, , , , as ;
-
(vi)
, for , ,
the iterative sequence , which is the unique solution of the variational inequality (2.1).
Proof
We split the proof into four steps.
Step 1. is well defined and so is .
For , define by , where is non-expansive for and . Then, for ,
Thus is a contraction, which ensures from Lemma 1.3 that there exists such that . That is, .
Since and is non-expansive for and , then is well defined, which implies that is well defined.
Step 2. is bounded.
For , we can easily know that
And
Thus
| 2.8 |
Using Lemma 1.2 and (2.8), we have, for ,
| 2.9 |
By using the inductive method, we can easily get the following result from (2.9):
Therefore, from assumptions (iii) and (vi), we know that is bounded.
Step 3. There exists , which solves the variational inequality (2.1).
Using Theorem 2.1, we know that there exists such that for . Moreover, under the assumption that , , as , which is the unique solution of the variational inequality (2.1).
Step 4. , as , where is the same as that in Step 3.
Set , then from Step 2 and assumption (iii), is a positive constant. Using Lemma 1.5, we have
| 2.10 |
Using Lemma 1.1, we know that
Therefore,
| 2.11 |
Now, from (2.10)–(2.11) and Lemmas 1.4 and 1.5, we know that for ,
which implies that
From Step 2, if we set , then is a positive constant.
Let , and .
Then
| 2.12 |
Our next discussion will be divided into two cases.
Case 1. is decreasing.
If is decreasing, we know from (2.12) and assumptions (iv) and (v) that
which ensures that , as . Then, from the property of , we know that , as .
Note that , then
as .
Now, our purpose is to show that , which reduces to showing that .
Let be the same as that in Step 3. Since , then is bounded, as . Using Lemma 1.5 again, we have
which implies that
So, .
Since , then , as .
Noticing that
we have .
From assumptions (iv) and (v) and Step 2, we know that and then . Thus .
Employing (2.12) again, we have
Assumption (iv) implies that . Then
Then the result that follows.
Case 2. If is not eventually decreasing, then we can find a subsequence so that for all . From Lemma 1.7, we can define a subsequence so that for all . This enables us to deduce that (similar to Case 1)
and then copying Case 1, we have . Thus , as .
This completes the proof. □
Remark 2.3
Theorem 2.2 is reasonable if we suppose , take , , , , , , , , , , , for and .
Remark 2.4
Our differences from the main references are:
-
(i)
the normalized duality mapping is no longer required to be weakly sequentially continuous at zero as that in [9];
-
(ii)
the parameter in the resolvent does not need satisfying the condition ‘ and for and some ’ as that in [3] or [9];
-
(iii)
Lemma 1.7 plays an important role in the proof of strong convergence of the iterative sequence, which leads to different restrictions on the parameters and different proof techniques compared to the already existing similar works.
Applications
Integro-differential systems
In Section 3.1, we shall investigate the following nonlinear integro-differential systems involving the generalized -Laplacian, which have been studied in [3]:
| 3.1 |
where Ω is a bounded conical domain of a Euclidean space (), Γ is the boundary of Ω with and ϑ denotes the exterior normal derivative to Γ. and denote the Euclidean inner-product and the Euclidean norm in , respectively. T is a positive constant. and . is the subdifferential of , where for . a and ε are non-expansive constants, , and are given functions.
Just like [3], we need the following assumptions to discuss (3.1).
Assumption 1
is a real number sequence with , is any real number sequence in and is a real number sequence satisfying . and for .
Assumption 2
Green’s formula is available.
Assumption 3
For each , is a proper, convex and lower-semicontinuous function and .
Assumption 4
and for each , the function is measurable for .
Assumption 5
Suppose that satisfies the following conditions:
Carathéodory’s conditions;
- Growth condition.
where , and is a positive constant for ; - Monotone condition. g is monotone in the following sense:
for all and .
Assumption 6
For , let denote the dual space of . The norm in , , is defined by
Definition 3.1
[3]
For , define the operator by
for .
Definition 3.2
[3]
For , define the function by
for .
Definition 3.3
[3]
For , define by
Lemma 3.4
[3]
For , define a mapping as follows:
where is the subdifferential of . For , we set . Then is m-accretive, where .
Lemma 3.5
[3]
Define by
for and is the same as that in (3.1), where . Then is continuous and strongly accretive. If we further assume that , then is -inversely strongly accretive, where .
Lemma 3.6
[3]
For , integro-differential systems (3.1) have a unique solution for .
Lemma 3.7
[3]
If , and , where k is a constant, then is the unique solution of integro-differential systems (3.1). Moreover, .
Remark 3.8
[3]
Set and .
Let , where .
Let , where .
Then , and , .
Theorem 3.9
Let D and X be the same as those in Remark 3.8. Suppose and are the same as those in Lemmas 3.4 and 3.5, respectively. Let be a fixed contractive mapping with coefficient and be -strictly pseudo-contractive mappings and -strongly accretive mappings with for . Suppose that , , , , , , , , , , and satisfy the same conditions as those in Theorem 2.2, where and . Let be generated by the following iterative algorithm:
| 3.2 |
If, in integro-differential systems (3.1), , and , then under the following assumptions in Theorem 2.2, the iterative sequence , which is the unique solution of integro-differential systems (3.1) and which satisfies the following variational inequality: for ,
Convex minimization problems
Let H be a real Hilbert space. Suppose are proper convex, lower-semicontinuous and nonsmooth functions [2], suppose are convex and smooth functions for . We use to denote the gradient of and the subdifferential of for .
The convex minimization problems are to find such that
| 3.3 |
for .
By Fermats’ rule, (3.3) is equivalent to finding such that
| 3.4 |
Theorem 3.10
Let H be a real Hilbert space and D be the nonempty closed convex sunny non-expansive retract of H. Let be the sunny non-expansive retraction of H onto D. Let be a contraction with coefficient . Let be proper convex, lower-semicontinuous and nonsmooth functions and be convex and smooth functions for . Let be -strictly pseudo-contractive mappings and -strongly accretive mappings with for . Suppose , , , , , , , , , , and satisfy the same conditions as those in Theorem 2.2, where and . Let be generated by the following iterative algorithm:
| 3.5 |
If, further, suppose is -Lipschitz continuous and attains a minimizer, then converges strongly to the minimizer of for .
Proof
It follows from [2] that is m-accretive. From [19], since is -Lipschitz continuous, then is -inversely strongly accretive. Thus Theorem 2.2 ensures the result.
This completes the proof. □
Acknowledgements
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 (ZD2016024), Key Project of Science and Research of Hebei University of Economics and Business (2016KYZ07), Youth Project of Science and Research of Hebei Educational Department (QN2017328) and Science and Technology Foundation of Agricultural University of Hebei (LG201612).
Authors’ contributions
All authors contributed equally to the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Li Wei, Liling Duan, Ravi P Agarwal, Rui Chen and Yaqin Zheng contributed equally to this work.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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