Abstract
Fixed point theory provides an important structure for the study of symmetry in mathematics. In this article, a new iterative method (general Picard–Mann) to approximate fixed points of nonexpansive mappings is introduced and studied. We study the stability of this newly established method which we find to be summably almost stable for contractive mappings. A number of weak and strong convergence theorems of such iterative methods are established in the setting of Banach spaces under certain geometrical assumptions. Finally, we present a number of applications to address various important problems (zero of an accretive operator, mixed equilibrium problem, convex optimization problem, split feasibility problem, periodic solution of a nonlinear evolution equation) appearing in the field of nonlinear analysis.
1. Introduction
Let be a nonempty subset of Banach space A mapping is said to be nonexpansive if for all A point is said to be a fixed point of T if To reckon fixed points of nonlinear mappings, various iterative methods have been used by several mathematicians. The simplest and most popular iterative method was developed by Charles Emile Picard (1856–1941) and is defined as:
and is known as Picard iterative method [1]. It is mostly used to obtain fixed points of contractive mappings. In general, the contractive condition is strong enough, not only to guarantee the existence of a unique fixed point, but also to approximate that fixed point by the Picard method. However, for nonexpansive mappings, the Picard iterative method need not converge to a fixed point. This can be seen by considering an anti-clockwise rotation of the unit disc of about the origin through an angle of, say, This is a nonexpansive symmetric mapping which has the origin as the unique fixed point. However, the sequence fails to converge with any initial guess other than the original.
Krasnosel’skiĭ [2] resolved this problem and considered a new method known as the Krasnosel’skiĭ iterative method. Schaefer [3] improved the Krasnosel’skiĭ iterative method by introducing a parameter as a controlling object. Mann [4] proposed a more general iterative method to approximate fixed points of nonexpansive mappings. He considered the sequence of parameters as controlling objects. Many well-known algorithms in signal processing and image recovery are iterative in nature. For particular choices of nonexpasive mappings, a wide variety of iteration algorithms used in signal processing and image recovery among others are special cases of the Krasnosel’skiĭ and Mann methods (cf. [5,6,7,8,9,10,11,12]).
In the last two decades, a number of iterative methods (from one step to four steps) have been considered and studied by researchers in order to improve the speed of convergence; see [4,13,14,15,16,17,18,19,20,21,22]. Motivated by these results, we consider a new iterative method (general Picard–Mann, in short GPM) to approximate fixed points of nonexpansive mappings in the setting of Banach spaces. It turns out that this method is highly efficient and an improvement over many other methods reported in the literature. Some new algorithms are suggested to find zeros of accretive operators, constrained convex optimization problems, generalized mixed equilibrium problems, split feasibility problems and periodic solutions of nonlinear evolution equations.
The rest of the paper is organized as follows: In Section 2, we present some existing results from the literature which are utilized in the rest part of paper. In Section 3, we define a general Picard–Mann iterative method. Moreover, we present stability results for the GPM iterative method and show that this method is summably almost stable for contractive mappings. Section 4 is devoted to weak and strong convergence results. We show that the sequence defined by GPM converges weakly and strongly to fixed points of nonexpansive mappings under different geometric conditions on Banach spaces. In Section 5, we discuss some applications.
2. Preliminaries
→ denotes strong convergence, ⇀ denotes weak convergence and denotes the cluster points (-limit) of a sequence that is, Let be a Banach space with its dual. The value of at is denoted by . The normalized duality mapping is defined as
A Banach space is called smooth if, for every , the limit
exists. The norm of is a Fréchet differentiable norm if, for every , the limit (2) exists and is attained uniformly for . A Banach space has the Kadec-–Klee property (or, KK-property) if, for any sequence , we have the following:
In [23], (Remark 1) it is proved that if a reflexive Banach space has a Fréchet differentiable norm, then the dual space of has the KK-property.
The definition of a uniformly convex Banach space (in short, UCBS) can be found in [24].
A Banach space has the Opial property [25] if, for every weakly convergent sequence, in with a weak limit
for all with All finite dimensional Banach spaces, Hilbert spaces and have the Opial property; see [24].
Lemma 1
([26], (p. 484)). Let be a UCBS and , ∀ Let and be two sequences in such that and hold for some Then
Lemma 2
([27]). For given a Banach space is uniformly convex if and only if there exists a strictly increasing continuous function in such a way that
for all , and
Proposition 1.
Let be a UCBS and a closed convex subset of such that
- (i)
- Ref. [28] (Demiclosedness principle) Let be a nonexpansive mapping. If is a sequence in such that weakly converges to ϱ and then That is, is demiclosed at zero.
- (ii)
- Ref. [29] If is bounded, then there exists a continuous, strictly increasing and convex function (depending only on the diameter of with such that for every nonexpansive mapping for all and the following inequality holds:
Definition 1
([30]). Let be a norm space and a subset of such that A mapping satisfies condition if there exists a nondecreasing function with and for all such that for all where If mapping T is nonexpansive with and demicompact, then T must satisfy condition
Definition 2.
Let be a normed space. A mapping is said to be a contraction if there exists a number such that, for all
3. A General Picard-Mann Iterative Method
In this section, we propose a new iterative method (a general Picard–Mann) which we define as follows:
where k is a fixed natural number and is a sequence in
Remark 1.
- (i)
- For , the iterative method (4) becomes normal S-iterative method [17] (or Picard–Mann hybrid iterative method [31]).
- (ii)
- (iii)
Stability Results
Now, we discuss the stability results for the GPM method (4). A fixed point iteration method is numerically stable if a small perturbation (due to rounding errors, approximation, etc.) during computations, will produce small changes in the approximate value of the fixed point computed by means of this method; see [32]. The stability of an iterative method plays a vital role in fractal geometry, computational analysis, game theory and others.
Let be a Banach space and a convex subset of such that Let be a mapping with For given the fixed point iteration method generates a sequence in as follows:
where f is some function. Harder and Hicks [33] considered the following definition:
Definition 3.
Osilike [34] considered the following concept of almost stability.
Definition 4.
Berinde [35] considered the weaker concept of stability, called summably almost T-stable.
Definition 5.
Any almost stable iteration procedure is also summably almost stable, but the reverse implication does not hold in general.
Now, we show that iterative method (4) is summably almost stable for contractive type mappings.
Theorem 1.
Let be a Banach space and a closed convex subset of such that Let be a contraction mapping with a fixed point Let be a sequence in , for given and for fixed , the sequence defined by (4). Suppose is an arbitrary sequence in and define
Then, we have following:
- (1)
- The sequence strongly converges to the fixed point
- (2)
- implies that so that is summably almost T-stable
- (3)
- implies
Proof.
Since , strongly converges to
Now, we prove (2), for each we have
and, for fixed
By the triangle inequality,
In view of assumption and [35], (Lemma 1) it implies that Finally, we prove (3). Suppose . Now,
This completes the proof. □
4. Convergence Results for Nonexpansive Mappings
In this section, we present some convergence results for the sequence generated by iterative method (4).
Lemma 3.
Let be a Banach space and a closed convex subset of such that Let be a nonexpansive mapping with Let be a sequence defined by (4). The following assertions hold:
- (1)
- If , then exists;
- (2)
- exists, where denotes the distance from ϱto
- (3)
- For all and the limit exists.
- (4)
- In addition, if is uniformly convex and the dual space of has the KK-property, then is a singleton.
Proof.
From (4) and let ,
Therefore, the sequence is a nonincreasing and bounded. Thus, exists for each . Therefore, exists.
From (1), the sequence is bounded. Let and set
Then, and exists. Now, we need to check the case Now, we define a mapping by
Then, is a nonexpansive mapping. Indeed, ∀
Moreover, and Let be the mapping defined as
It can be observed that and Moreover,
Set
Now, from Proposition 1, there exists a strictly increasing continuous convex function with such that
Since exists, the last difference is zero. Therefore, and Now, we have
Hence,
That is, there exists for all From [23], (Lemma 3.2) we conclude that is a singleton. This completes the proof. □
Theorem 2.
Let be a UCBS, and T be same as in Lemma 3. Let be a sequence defined by (4) with
Assume that either of the following assumptions hold:
- (a)
- satisfies the Opial’s property;
- (b)
- has the KK-property.
Then, weakly converges to a fixed point of
Proof.
In view of Lemma 3, both sequences and are bounded, so these are contained in for sufficiently large In view of Lemma 2, there exists a continuous, convex and strictly increasing function with such that (3) holds. Thus, we have
So,
This implies that
In particular, Due to (9), Therefore,
However,
Thus, sequence is nonincreasing. Therefore, exists. From (11), we obtain
Since is uniformly convex, is reflexive. Since is reflexive, there exists a subsequence of and weakly converges to a point From the demiclosedness principle of (Proposition (1)), we notice that
Now, we show that is a singleton; this implies that there is a unique weak limit for each subsequences of , and weakly converges to a fixed point of T.
First, we suppose that (a) is true; that is, has Opial’s property. We assume that does not converge weakly to p, i.e., let and be subsequences of such that and , respectively, then If we reach the following contradiction:
Suppose (b) is true ( has the KK-property); from Lemma 3, it is guaranteed that is a singleton. Therefore, in both cases it is showed that is a singleton. This completes the proof. □
Theorem 3.
Let , T, be same as in Theorem 2 and is UCBS. If the range of under T is contained in a compact subset of . Then, strongly converges to a fixed point of T.
Proof.
Since the range of under T is contained in a compact set, there exists a subsequence of that strongly converges to By the triangle inequality, we obtain
and by (12), the subsequence strongly converges to By the triangle inequality and nonexpansiveness of T
Taking ,
and, we have Lemma 3 ensures that exists. Therefore, is a strong limit of the sequence . □
Theorem 4.
Let , T, be same as in Theorem 2 and be a UCBS. Then, the sequence strongly converges to a fixed point of T if .
Proof.
Let From Lemma 3, exists, so
Let be a subsequence of sequence such that ∀, where is a sequence in From Lemma 3, we have
By the triangle inequality and (13),
Following the standard argument, it can be easily shown that is a Cauchy sequence in Since is closed [24], the sequence converges to a point By the triangle inequality
Letting it follows that strongly converges to z. By Lemma 3, exists; thus, the sequence strongly converges to □
Theorem 5.
Let , , T and be same as in Theorem 4. If T satisfies condition , then strongly converges to a fixed point of
Proof.
From Theorem 2,
Since T satisfies condition (I),
From (14), we obtain
Since the function is a nondecreasing with ∀ and . Thus, all conditions of Theorem 4 are fulfilled and strongly converges to a point in □
5. Applications
In this section, we discuss some useful applications of our results.
5.1. Zero of an Accretive Operator
Let be an operator having domain and range in a Banach space . The operator is called as accretive if there exists such that
where J is the duality mapping from to (dual space of ). An operator is m-accretive if
We denote by the set of zeros of , that is,
For any , denote by the resolvent of and defined as
It is well-known that is a nonexpansive mapping from to For any ,
It is well-known fact that many pivotal problems originating in different fields can be modelled as an initial value problem defined below
where is an accretive operator on . Some important models such as Schrödinger, heat and wave equations are examples of evolution equations (cf. [36]). In [37], Browder showed that (15) is solvable if is locally Lipschitzian and accretive operator on . Many researchers considered the solution of (15) under various conditions on the operator .
It can be seen that in (15), whenever u is not depending on t, then (15) reduces to Therefore, the zero of accretive operators is equivalent to the equilibrium points of the system (15), see [37]. Thus, the equilibrium points of the system described by (15) correspond to approximating zeros of accretive operators; see [37] and references therein.
Now, we consider a problem of finding zeros of an m-accretive operator in
Lemma 4.
[38]. Let . Then, for all
In particular, if for all and is any sequence in then
Theorem 6.
Let be a UCBS and an m-accretive operator in such that For fixed , let be a sequence defined as follows:
where is a sequences in with and is a sequence of positive numbers satisfying the following condition:
Assume that either of following assumptions hold:
- (a)
- has the Opial’s property;
- (b)
- has the KK-property.
Then, the sequence weakly converges to a point of
Proof.
Let By (16), we obtain
Thus, is bounded and exists. Call it r. That is
Using nonexpansiveness of and (17)
Thus,
By (20) and Lemma 4, we obtain
From (21) and demiclosedness principle, it follows that Following the last part of the proof of Theorem 2, we can conclude that the sequence weakly converges to a point in □
5.2. Generalized Mixed Equilibrium Problem
Let be a closed convex subset of and is a bifunction satisfying certain conditions. Consider the following problem which is known as equilibrium problem (or EP), see [39]:
Zhang [40] generalized EP and called it generalized mixed equilibrium problem:
for all where is a nonlinear mapping and is a real valued function. We denote the set of solutions of (23) by that is,
for all
- The problem (23) reduced to mixed equilibrium problem (in short, MEP) [41] if
- The problem (23) is equivalent to mixed variational inequality problem of Browder type [42] if
In order to solve problem (23), we suppose that the bifunction satisfies the following assumptions:
- (X1)
- for all
- (X2)
- is monotone, that is, for all
- (X3)
- for all
- (X4)
- for each is a convex and lower semicontinuous;
- (X5)
- for fixed and there exists a bounded subset of and such that for all
It is shown in [40] that if satisfies (X1)–(X4), then for the function
assumptions (X1)–(X4) still hold and is closed and convex.
Lemma 5.
[40]. Let be a Hilbert space and a nonempty closed convex subset of Let be a continuous and monotone mapping, a bifunction satisfying (X1)–(X4) and a proper lower semicontinuous and convex function. For given and , define a mapping by
for all Then
- (a)
- For each is nonempty;
- (b)
- is a single valued mapping;
- (c)
- is firmly nonexpansive, that is, for allso is a nonexpansive.
- (d)
- ;
- (e)
- is closed and convex.
Theorem 7.
Let , Θ, φ and ϕ be same as in Lemma (5). Suppose Let be a sequence defined by
for every where is a same sequence as in Theorem 2. Then, weakly converges to a point in
Proof.
Taking , in Theorem 2 and in view of Lemma 5, we can easily obtain the desired result. □
5.3. Constrained Convex Optimization Problem
Let be a Hilbert space and a closed convex subset of Let be a differentiable convex function. Consider the following minimization problem:
It can be seen that is a solution to the minimization problem (26) if and only if is a solution to the fixed point equation
where is any fixed number. It is known that if satisfies the Lipschitz condition, that is,
for all , where , then the mapping is is averaged for . Hence, is nonexpansive mapping. Now, we employ the iterative method (4) to solve the minimization problem (26).
Theorem 8.
Again, we discuss a quadratic optimization problem on the trust region (see [43] for more details). Let be a bounded self-adjoint linear operator. Let be a fixed constant and u a given point in Consider the following problem:
Take
Then, is a closed ball having radius with center at origin. Thus, the projection can be defined as
The gradient of is defined as
and is L-Lipschitz with We consider the following theorem.
5.4. Split Feasibility Problem
Censor and Elfving [44] introduced the following problem (or split feasibility problem, in short SFP):
Let and be finite dimensional Hilbert spaces. Let be a bounded linear operator. Let and be nonempty closed convex subsets of and respectively. The split feasibility problem (SFP) is to find an element
Let be set of solutions of (30). The SFP has many important applications which appear in modeling inverse problems, medical image reconstruction and others; for more details, see [45]. A number of authors have extended the SFP from finite dimensional spaces to infinite dimensional Hilbert spaces.
Byrne [5] considered the following algorithm known as CQ to obtain the solution of (30):
where is the adjoint of and
In view of following analysis, we can use fixed point iterative method to solve the SFP (30) (see [46] for more details).
Suppose that and . Hence , which in turn follows the equation which leads to the equation . Therefore, we have the fixed point equation
To ensure that we can consider the following:
Proposition 2
Theorem 10.
Let , Γ, and be as defined above. Assume that the SFP (30) is consistent and . For fixed and let be a sequence such that
for every where satisfying the following condition:
Then, the sequence weakly converges to a point
Proof.
It is shown in [46], (Theorem 3.6) that is -averaged with Thus, take and T is a nonexpansive mapping. Therefore, the required result follows from Theorem 2. □
5.5. Periodic Solution of a Nonlinear Evolution Equation
Browder [47] considered the following time-dependent nonlinear evolution equation,
where is Hilbert space, is a mapping and is a family of closed linear operators in .
Definition 6
([47]). Let be continuous under the strong topology. ϱ is called a mild solution of (32) on with initial value
if and only if
where is the evolution system for the homogeneous linear system
The following theorem proved the existence of periodic solution of (32).
Theorem 11
([47]). Assume that and are periodic in r with a common period ξ and the following conditions hold:
- (1)
- For each r,
- (2)
- For each r,
- (3)
- For each initial value there exists a mild solution ϱ of (32) on .
- (4)
- There exists some such that
Then, there exists an element with such that the mild solution of (32) with initial condition is periodic of period ξ.
We employ the iterative method (4) to obtain a periodic solution of (32). Let be a mapping defined as follows:
where is the solution of (32) satisfying the initial condition That is, let T be the mapping which assigns to each the value of of the mild solution of (32) with T maps the closed ball into itself due to the Assumption (4). It is noted that T is a nonexpansive mapping. Therefore, T has a fixed point, say , and the corresponding solution of (32) with is a desired periodic solution of (32) with period More precisely, finding a periodic solution of (32) is equivalent to finding a fixed point of T.
Now, we consider an iterative method approach to finding a periodic solution of (32).
Theorem 12.
Suppose that Assumptions (1)–(4) in Theorem 11 hold. For a given , define a sequence of functions as follows:
where is a sequence as in Theorem 2, is a solution of (32) with and is a solution of (32) with for each (that is (35) holds for and ). Then, the sequence weakly converges to a point in and the corresponding mild solution of (32) with is a periodic of period
Author Contributions
Conceptualization, R.S.; original draft preparation, R.S.; writing, review and editing, R.P. and W.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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