Next Article in Journal
The Methods of Three-Dimensional Modeling of the Hydrogenerator Thrust Bearing
Previous Article in Journal
Signal Processing Algorithm for Monopulse Noise Noncoherent Wideband Helicopter Altitude Radar
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Approximating Fixed Points of Nonexpansive Type Mappings via General Picard–Mann Algorithm

Department of Mathematical Sciences and Computing, Walter Sisulu University, Mthatha 5117, South Africa
*
Author to whom correspondence should be addressed.
Computation 2022, 10(9), 151; https://doi.org/10.3390/computation10090151
Submission received: 18 August 2022 / Revised: 25 August 2022 / Accepted: 27 August 2022 / Published: 31 August 2022

Abstract

:
The aim of this paper is to approximate fixed points of nonexpansive type mappings in Banach spaces when the set of fixed points is nonempty. We study the general Picard–Mann (GPM) algorithm, obtaining the weak and strong convergence theorems. We provide an example to illustrate the convergence behaviour of the GPM algorithm. We compare the GPM algorithm with other existing (well known) algorithms numerically (under different parameters and initial guesses).

1. Introduction and Preliminaries

Let ( Z , . ) be a Banach space. The mapping Φ : Z Z is nonexpansive if
Φ ( ϑ ) Φ ( ν ) ϑ ν ϑ , ν Z .
A point ϑ Z is a fixed point of Φ if Φ ( ϑ ) = ϑ . Let F ( Φ ) denote the set of fixed points of Φ . Finding a fixed point of nonlinear mappings is an important problem and various algorithms have been used by many researchers. The Picard algorithm [1] is mostly used (simplest and popular) to find the fixed points of contractive mappings. However, for nonexpansive mappings, the Picard algorithm need not converge to a fixed point. Krasnosel’skiĭ [2], Schaefer [3] and Mann [4] proposed more general algorithms to find fixed points of nonexpansive mappings.
Many mathematicians extended and generalized the class of nonexpansive mappings in different directions, see [5]. In 2011, García-Falset et al. [6] considered the following class of mappings:
Definition 1
([6]). Let Y be a subset of a Banach space Z such that Y . A mapping Φ : Y Y is said to satisfy condition ( E μ ) on Y if there exists μ 1 such that
ϑ Φ ( ν ) μ ϑ Φ ( ϑ ) + ϑ ν , ϑ , ν Y .
A mapping Φ satisfies condition (E) on Y whenever Φ satisfies ( E μ ) for some μ 1 .
A number of papers have been appeared in literature dealing with condition (E), see [5,7,8,9] and references therein. In the last two decades, a number of algorithms (from one step to four steps) were studied by mathematicians to improve the fastness of the algorithm, see [4,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34].
Motivated by the above results, we approximate fixed points of the class of mappings satisfying condition (E). We employ general Picard–Mann (in short GPM) and obtain a number of weak and strong convergence results. We supply a numerical example and compare the GPM algorithm with various algorithms presented in Section 2.
We denote → for strong convergence, ⇀ for weak convergence, and ω w ( ϑ n ) denotes a cluster points ( ω -limit) set of a sequence { ϑ n } , that is, ω w ( ϑ n ) : = { ϑ : ϑ n k ϑ } .
Lemma 1
([35] p. 484). Let Z be a uniformly convex Banach space and 0 < a p n b < 1 for all n N . Let { ϑ n } and { ν n } be two sequences such that lim sup n ϑ n r , lim sup n ν n r and lim n p n ϑ n + ( 1 p n ) ν n = r hold for some r 0 . Then, lim n ϑ n ν n = 0 .
Lemma 2
((Demiclosedness principle). [6]). Let Y be a nonempty subset of a Banach space Z which has the Opial property. Let Φ : Y Y be a mapping satisfying condition (E). Suppose { ϑ n } is a sequence in Y such that { ϑ n } converges weakly to ϑ and lim n ϑ n Φ ( ϑ n ) = 0 . Then, Φ ( ϑ ) = ϑ . That is, I Φ is demiclosed at zero.
Lemma 3
([6]). Let Y be a nonempty subset of a Banach space Z and Φ : Y Y satisfies condition (E) with F ( Φ ) . Then, Φ is quasi-nonexpansive.

2. Various Iterative Methods (or Algorithms)

In this section, we present a number of iterative methods considered in the literature: for a given ϑ 1 Y and { α n } , { β n } , { γ n } [ 0 , 1 ] .
  • Mann [4]
    ϑ n + 1 = ( 1 α n ) ϑ n + α n Φ ( ϑ n ) .
  • Ishikawa [10]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = ( 1 α n ) ϑ n + α n Φ ( ν n ) .
  • Noor [11]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) ϑ n + β n Φ ( z n ) ϑ n + 1 = ( 1 α n ) ϑ n + α n Φ ( ν n ) .
  • Agarwal et al. [12]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = ( 1 α n ) Φ ( ϑ n ) + α n Φ ( ν n ) .
  • Phuengrattana and Suantai [13]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) z n + β n Φ ( z n ) ϑ n + 1 = ( 1 α n ) ν n + α n Φ ( ν n ) .
  • Sahu [14]
    ϑ n + 1 = Φ { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } .
    Remark1.
    In 2013, S. H. Khan [36] introduced the same iterative method like (7) and called it the Picard–Mann hybrid iterative method.
  • Chugh et al. [15]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) Φ ( ϑ n ) + β n Φ ( z n ) ϑ n + 1 = ( 1 α n ) ν n + α n Φ ( ν n ) .
  • Karaca and Yildirim [16]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ { ( 1 α n ) Φ ( ϑ n ) + α n Φ ( ν n ) } .
  • Abbas and Nazir [17]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) Φ ( ϑ n ) + β n Φ ( z n ) ϑ n + 1 = ( 1 α n ) Φ ( ν n ) + α n Φ ( z n ) .
  • Thakur et al. [18]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) z n + β n Φ ( z n ) ϑ n + 1 = ( 1 α n ) Φ ( ϑ n ) + α n Φ ( ν n ) .
  • Sintunavarat and Pitea [19]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) ν n = ( 1 β n ) ϑ n + β n z n ϑ n + 1 = ( 1 α n ) Φ ( ν n ) + α n Φ ( z n ) .
  • Thakur et al. [20]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ { ( 1 α n ) Φ ( ϑ n ) + α n ν n } .
  • Ullah and Arshad et al. [22]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ 2 { ( 1 α n ) ϑ n + α n Φ ( ν n ) } .
  • Ullah and Arshad [23]
    ϑ n + 1 = Φ 2 { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } .
    Remark2.
    In 2020, F. Ali and J. Ali [37] introduced the same iterative method like (15) and called it the F * iterative method.
  • Hussain et al. [24]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ 2 { ( 1 α n ) Φ ( ϑ n ) + α n Φ ( ν n ) } .
  • Ullah and Arshad [25]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ 2 { ( 1 α n ) ν n + α n Φ ( ν n ) } .
  • Piri et al. [26]
    ν n = Φ { ( 1 β n ) ϑ n + β n Φ ( ϑ n ) } ϑ n + 1 = ( 1 α n ) Φ ( ν n ) + α n Φ 2 ( ν n ) .
  • Bhutia and Tiwary [27]
    ν n = Φ { ( 1 β n ) ϑ n + β n Φ ( ϑ n ) } ϑ n + 1 = Φ 2 { ( 1 α n ) ν n + α n Φ ( ν n ) } .
  • Garodia and Uddin [28]
    ϑ n + 1 = Φ 2 { ( 1 α n ) Φ ( ϑ n ) + α n Φ 2 ( ϑ n ) } .
  • Garodia and Uddin [29], and Hussain et al. [38] (D-iterative algorithm, see also [39])
    ν n = Φ { ( 1 β n ) ϑ n + β n Φ ( ϑ n ) } ϑ n + 1 = Φ 2 { ( 1 α n ) Φ ( ϑ n ) + α n Φ ( ν n ) } .
    Remark3.
    If we look at the submission dates, it can be noticed that the paper by Hussain et al. [38] has been received on 3 May 2020, while Garodia and Uddin’s paper [29] has no submission information. Thus, we cannot say which iterative method appeared first.
  • Ali et al. [30]
    ν n = Φ { ( 1 β n ) ϑ n + β n Φ ( ϑ n ) } ϑ n + 1 = Φ { ( 1 α n ) Φ ( ν n ) + α n Φ 2 ( ν n ) } .
  • Ali and Ali [31]
    ϑ n + 1 = Φ 3 { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } .
  • Hassan et al. [32]
    w n = Φ { ( 1 δ n ) ϑ n + δ n Φ ( ϑ n ) } z n = Φ { ( 1 γ n ) w n + γ n Φ ( w n ) } ν n = Φ { ( 1 β n ) z n + β n Φ ( z n ) } ϑ n + 1 = Φ { ( 1 α n ) ν n + α n Φ ( ν n ) } .
  • Rani and Arti [33]
    z n = ( 1 γ n ) ϑ n + γ n Φ ( ϑ n ) } ν n = Φ { ( 1 β n ) Φ ( ϑ n ) + β n Φ ( z n ) } ϑ n + 1 = Φ { ( 1 α n ) ν n + α n Φ ( ν n ) } .
  • Ahmad et al. [34]
    ν n = ( 1 β n ) ϑ n + β n Φ ( ϑ n ) ϑ n + 1 = Φ { ( 1 α n ) Φ ( ν n ) + α n Φ 2 ( ν n ) } .

3. A General Picard–Mann Iterative Method

In [40], Shukla et al. proposed the following algorithm (known as GPM):
ϑ 1 = ϑ Y ϑ n + 1 = Φ k { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } , n N ,
where { α n } is a sequence in [ 0 , 1 ] , and k is a fixed natural number.
Remark 4.
It is easy to see that none of the iterative methods (from (2) to (26)) reduces to iterative method (27).

4. Convergence Theorems

In this section, we present some convergence results for the sequence generated by iterative method (27).
Lemma 4.
Let Y be a nonempty closed convex subset of a Banach space Z and Φ : Y Y a mapping satisfying condition (E) with F ( Φ ) . Let { ϑ n } be a sequence defined by (27). Then, the following assertions hold:
(1)
If p F ( Φ ) , then lim n ϑ n p exists;
(2)
lim n d ( ϑ n , F ( Φ ) ) exists, where d ( ϑ , F ( Φ ) ) denotes the distance from ϑto F ( Φ ) .
Proof. 
Let p F ( Φ ) . From (27), we have
ϑ n + 1 p = Φ k { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } p ( 1 α n ) ϑ n + α n Φ ( ϑ n ) p ( 1 α n ) ϑ n p + α n Φ ( ϑ n ) p ϑ n p .
Therefore, the sequence { ϑ n p } is nonincreasing and bounded. Hence, lim n ϑ n p exists for each p F ( Φ ) . Therefore, lim n d ( ϑ n , F ( Φ ) ) exists. □
Lemma 5.
Let Z be a uniformly convex Banach space, Y and Φ be the same as in Lemma 4 with F ( Φ ) . Let { ϑ n } be a sequence defined by (27) with α n ( a , b ) ( 0 , 1 ) , for all n N , where a , b ( 0 , 1 ) . Then, lim n ϑ n Φ ( ϑ n ) = 0 .
Proof. 
By Lemma 4, the sequence { ϑ n } is bounded and lim n ϑ n p exists. Call it r. That is,
lim n ϑ n p = r .
Using the condition on mapping Φ , we have
p Φ ( ϑ n ) p Φ ( p ) + ϑ n p
and using (29)
lim sup n Φ ( ϑ n ) p r .
Now, by (27) and (29), we have
r = lim n ϑ n + 1 p = lim sup n Φ k { ( 1 α n ) ϑ n + α n Φ ( ϑ n ) } p lim sup n ( 1 α n ) ϑ n + α n Φ ( ϑ n ) p lim n ϑ n p = r .
Thus,
lim n ( 1 α n ) ( ϑ n p ) + α n ( Φ ( ϑ n ) p ) = r .
From (29)–(31) and Lemma 1, it follows that
lim n ϑ n Φ ( ϑ n ) = 0 .
Theorem 1.
Let Z be a uniformly convex Banach space, Y and Φ be the same as in Lemma 4 with F ( Φ ) . Let { ϑ n } be a sequence defined by (27) with α n ( a , b ) ( 0 , 1 ) , for all n N , where a , b ( 0 , 1 ) . If Z satisfies the Opial property, then { ϑ n } weakly converges to a point in F ( Φ ) .
Proof. 
By Lemma 4, the sequence { ϑ n } is bounded and by Lemma 5, lim n ϑ n Φ ( ϑ n ) = 0 . Since Z is uniformly convex, there exists a subsequence { ϑ n j } of { ϑ n } that weakly converges to a point p Y . From the demiclosedness principle of I Φ (Proposition (2)), p ω w ( ϑ n ) F ( Φ ) . Now, we claim that ω w ( ϑ n ) is a singleton, and there is a unique weak limit for each subsequence of { ϑ n } . This implies that { ϑ n } weakly converges to a fixed point of Φ . In view of the Opial property, it can be seen that ω w ( ϑ n ) is a singleton. This completes the proof. □
Theorem 2.
Let Y , Φ and { ϑ n } be the same as in Theorem 1 with F ( Φ ) and Z a uniformly convex Banach space. If the range of Y under Φ is contained in a compact subset of Z , then { ϑ n } strongly converges to a fixed point of Φ .
Proof. 
Since the range of Y under Φ is contained in a compact set, there exists a subsequence { Φ ( ϑ n j ) } of { Φ ( ϑ n ) } that strongly converges to p Y . By the triangle inequality, we obtain
ϑ n j p ϑ n j Φ ( ϑ n j ) + Φ ( ϑ n j ) p
and, by Lemma 5, the subsequence { ϑ n j } strongly converges to ϑ . By the condition on mapping Φ ,
ϑ n j Φ ( p ) μ ϑ n j Φ ( ϑ n j ) + ϑ n j p .
Taking j implies
lim sup j ϑ n j Φ ( p ) μ lim j ϑ n j Φ ( ϑ n j ) + lim sup j ϑ n j p ,
and we have Φ ( p ) = p . In view of Lemma 4, it follows that lim n ϑ n p exists. Therefore, { ϑ n } strongly converges to p . □
Theorem 3.
Let Y , Φ and { ϑ n } be the same as in Theorem 1 with F ( Φ ) and Z a uniformly convex Banach space with F ( Φ ) . Then, the sequence { ϑ n } strongly converges to a fixed point of Φ if lim inf n d ( ϑ n , F ( Φ ) ) = 0 .
Proof. 
This can be completed following Theorem 4.12 [7]. □
Theorem 4.
Let Y , Z , Φ and { ϑ n } be the same as in Theorem 3 with F ( Φ ) . If Φ satisfies condition ( I ) , then { ϑ n } strongly converges to a point in F ( Φ ) .
Proof. 
This can be completed following Theorem 4.13 [7]. □

5. Numerical Results

In this section, we present an example and employ it to compare various iterative methods for different initial guess and parameters.
Example 1.
Let R 2 be a Banach space equipped with the norm
( ϑ ( 1 ) , ϑ ( 2 ) ) = | ϑ ( 1 ) | + | ϑ ( 2 ) |
and Y = [ 0 , 1 ] × [ 0 , 1 ] a subset of R 2 . Let Φ : Y Y be a mapping defined by
Φ ( ϑ ( 1 ) , ϑ ( 2 ) ) = 1 3 ( ϑ ( 1 ) + 1 4 ) 2 , 1 3 4 ϑ ( 2 ) , i f ϑ ( 1 ) [ 0 , 3 4 ) , ϑ ( 1 ) 7 + 1 3 , 1 3 4 ϑ ( 2 ) , i f ϑ ( 1 ) [ 3 4 , 1 ] .
Now, we show that Φ satisfies the condition (E) for μ = 6 , and, for this, we consider the following cases:
Case (i)
Let ϑ ( 1 ) 0 , 3 4 . If ν ( 1 ) 0 , 3 4 ; then,
Φ ( ϑ ) Φ ( ν ) = 1 3 ϑ ( 1 ) + 1 4 2 ν ( 1 ) + 1 4 2 + 3 4 | ϑ ( 2 ) ν ( 2 ) | 1 3 | ( ϑ ( 1 ) + ν ( 1 ) ) ( ϑ ( 1 ) ν ( 1 ) ) | + 1 6 | ϑ ( 1 ) ν ( 1 ) | + 3 4 | ϑ ( 2 ) ν ( 2 ) | 2 3 | ϑ ( 1 ) ν ( 1 ) | + 3 4 | ϑ ( 2 ) ν ( 2 ) | ϑ ν .
Let ν ( 1 ) 3 4 , 1 . Now, we show that
1 3 ϑ ( 1 ) + 1 4 2 ν ( 1 ) 7 1 3 + 3 4 ϑ ( 2 ) ν ( 2 ) 5 ϑ ( 1 ) 1 3 ϑ ( 1 ) + 1 4 2 + ϑ ( 2 ) 1 + 3 4 ϑ ( 2 ) + | ϑ ( 1 ) ν ( 1 ) | + | ϑ ( 2 ) ν ( 2 ) | .
Now, we can break the above inequality into two parts. First, we show the following inequality:
1 3 ϑ ( 1 ) + 1 4 2 ν ( 1 ) 7 1 3 5 ϑ ( 1 ) 1 3 ϑ ( 1 ) + 1 4 2 + | ϑ ( 1 ) ν ( 1 ) | .
From the triangle inequality, we have
1 3 ϑ ( 1 ) + 1 4 2 ν ( 1 ) 7 1 3 1 3 ϑ ( 1 ) + 1 4 2 ϑ ( 1 ) + ϑ ( 1 ) ν ( 1 ) 7 1 3 .
From the considered range of ϑ ( 1 ) and ν ( 1 ) , we estimate that
ϑ ( 1 ) ν ( 1 ) 7 1 3 10 21 .
For ϑ ( 1 ) 0 , 167 756 , it can be seen that | ν ( 1 ) ϑ ( 1 ) | 10 21 . In view of (34), we can see that (33) is true for this case. Again, for ϑ ( 1 ) 167 756 , 3 4 , the function ϑ ( 1 ) 1 3 ϑ ( 1 ) + 1 4 2 is increasing and ϑ ( 1 ) 1 3 ϑ ( 1 ) + 1 4 2 63005 428652 . Thus,
4 ϑ ( 1 ) 1 3 ϑ ( 1 ) + 1 4 2 10 21 .
In light of (34), it follows that (33) is true for this case too. However,
3 4 ϑ ( 2 ) ν ( 2 ) | ϑ ( 2 ) ν ( 2 ) | .
Combining (33) and (35), we can see that (32) is true. By the triangle inequality,
ϑ Φ ( ν ) Φ ( ϑ ) Φ ( ν ) + ϑ Φ ( ϑ )
mapping Φ satisfies condition (E).
Case (ii)
Let ϑ ( 1 ) 3 4 , 1 . If ν ( 1 ) 3 4 , 1 , then Φ is a contractive mapping and satisfies condition (E). Let ν ( 1 ) 0 , 3 4 . We prove the following conditions:
ϑ ( 1 ) 7 + 1 3 1 3 ν ( 1 ) + 1 4 2 + 3 4 ϑ ( 2 ) ν ( 2 ) 5 6 7 ϑ ( 1 ) 1 3 + 7 4 ϑ ( 2 ) 1 + | ϑ ( 1 ) ν ( 1 ) | + | ϑ ( 2 ) ν ( 2 ) | .
We shall break the above inequality into two parts. First, we shall prove the following inequality:
ϑ ( 1 ) 7 + 1 3 1 3 ν ( 1 ) + 1 4 2 5 6 7 ϑ ( 1 ) 1 3 + | ϑ ( 1 ) ν ( 1 ) | .
By the triangle inequality, we have
ϑ ( 1 ) 7 + 1 3 1 3 ν ( 1 ) + 1 4 2 6 7 ϑ ( 1 ) 1 3 + ϑ ( 1 ) 1 3 ν ( 1 ) + 1 4 2 .
From the considered range of ϑ ( 1 ) and ν ( 1 ) , we can estimate
ϑ ( 1 ) 1 3 ν ( 1 ) + 1 4 2 47 48
and 6 7 ϑ ( 1 ) 1 3 13 42 . Therefore, 4 6 7 ϑ ( 1 ) 1 3 47 48 . From the above estimate and (39), it implies that (38) is true. However,
3 4 ϑ ( 2 ) ν ( 2 ) | ϑ ( 2 ) ν ( 2 ) | .
Combining (38) and (40), it can be seen that (37) is true. In view of (36) and (37), mapping Φ satisfies condition (E). Since Φ is not continuous, Φ is not nonexpansive. It can be seen that 5 2 6 4 , 4 7 is a fixed point of Φ .
Now, we compare convergence behavior of various algorithms in view of Example (32). We make different choices of initial guesses and parameters ( α n , β n , γ n ) and set ϑ n p < 10 15 as our stopping criterion (p is a fixed point of Φ ).
Observations: In view of Table 1 and Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6, we note that, for different choices of initial guesses and parameters, the general Picard–Mann algorithm (GPM) (27) (with k = 4 ) converges faster to a fixed point of mapping satisfying condition (E) than other algorithms considered in Section 2. We also conclude that (GPM) algorithm is consistent.

Author Contributions

Conceptualization, software, writing—original draft preparation, R.S., writing—review and editing, R.P. 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.

Acknowledgments

We are very thankful to the reviewers for their constructive comments and suggestions, which have been useful for the improvement of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Picard, E. Mémoire sur la théorie des équations aux dérivées partielles et la méthodedes approximations successives. Mathématiques Pures Appliquées 1890, 6, 145–210. [Google Scholar]
  2. Krasnosel’skiĭ, M.A. Two remarks on the method of successive approximations. Uspehi Mat. Nauk 1955, 10, 123–127. [Google Scholar]
  3. Schaefer, H. Über die Methode sukzessiver Approximationen. Jber. Deutsch. Math.-Verein. 1957, 59, 131–140. [Google Scholar]
  4. Mann, W.R. Mean value methods in iteration. Proc. Amer. Math. Soc. 1953, 4, 506–510. [Google Scholar] [CrossRef]
  5. Pant, R.; Shukla, R.; Patel, P. Nonexpansive mappings, their extensions, and generalizations in Banach spaces. In Metric Fixed Point Theory—Applications in Science, Engineering and Behavioural Sciences; Forum for Interdisciplinary Mathematics; Springer: Singapore, 2021; pp. 309–343. [Google Scholar] [CrossRef]
  6. García-Falset, J.; Llorens-Fuster, E.; Suzuki, T. Fixed point theory for a class of generalized nonexpansive mappings. J. Math. Anal. Appl. 2011, 375, 185–195. [Google Scholar] [CrossRef]
  7. Pandey, R.; Pant, R.; Rakočević, V.; Shukla, R. Approximating fixed points of a general class of nonexpansive mappings in Banach spaces with applications. Results Math. 2019, 74, 7. [Google Scholar] [CrossRef]
  8. Pant, R.; Patel, P.; Shukla, R. Fixed point results for a class of nonexpansive type mappings in Banach spaces. Adv. Theory Nonlinear Anal. Appl. 2021, 5, 368–381. [Google Scholar] [CrossRef]
  9. Pant, R.; Patel, P.; Shukla, R.; De la Sen, M. Fixed point theorems for nonexpansive type mappings in Banach spaces. Symmetry 2021, 13, 585. [Google Scholar] [CrossRef]
  10. Ishikawa, S. Fixed points by a new iteration method. Proc. Amer. Math. Soc. 1974, 44, 147–150. [Google Scholar] [CrossRef]
  11. Noor, M.A. New approximation schemes for general variational inequalities. J. Math. Anal. Appl. 2000, 251, 217–229. [Google Scholar] [CrossRef]
  12. Agarwal, R.P.; O’Regan, D.; Sahu, D.R. Iterative construction of fixed points of nearly asymptotically nonexpansive mappings. J. Nonlinear Convex Anal. 2007, 8, 61–79. [Google Scholar]
  13. Phuengrattana, W.; Suantai, S. On the rate of convergence of Mann, Ishikawa, Noor and SP-iterations for continuous functions on an arbitrary interval. J. Comput. Appl. Math. 2011, 235, 3006–3014. [Google Scholar] [CrossRef]
  14. Sahu, D.R. Applications of the S-iteration process to constrained minimization problems and split feasibility problems. Fixed Point Theory 2011, 12, 187–204. [Google Scholar]
  15. Chugh, R.; Kumar, V.; Kumar, S. Strong convergence of a new three step iterative scheme in Banach spaces. Am. J. Comput. Math. 2012, 2, 345. [Google Scholar] [CrossRef] [Green Version]
  16. Karaca, N.; Yildirim, I. Approximating fixed points of nonexpansive mappings by a faster iteration process. J. Adv. Math. Stud. 2015, 8, 257–264. [Google Scholar]
  17. Abbas, M.; Nazir, T. A new faster iteration process applied to constrained minimization and feasibility problems. Mat. Vesnik 2014, 66, 223–234. [Google Scholar]
  18. Thakur, D.; Thakur, B.S.; Postolache, M. New iteration scheme for numerical reckoning fixed points of nonexpansive mappings. J. Inequal. Appl. 2014, 2014, 328. [Google Scholar] [CrossRef]
  19. Sintunavarat, W.; Pitea, A. On a new iteration scheme for numerical reckoning fixed points of Berinde mappings with convergence analysis. J. Nonlinear Sci. Appl. 2016, 9, 2553–2562. [Google Scholar] [CrossRef]
  20. Thakur, B.S.; Thakur, D.; Postolache, M. A new iterative scheme for numerical reckoning fixed points of Suzuki’s generalized nonexpansive mappings. Appl. Math. Comput. 2016, 275, 147–155. [Google Scholar] [CrossRef]
  21. Thakur, B.S.; Thakur, D.; Agarwal, R.P. Convergence theorems for total asymptotically nonexpansive mappings in CAT(0) spaces. J. Nonlinear Convex Anal. 2017, 18, 2059–2074. [Google Scholar]
  22. Ullah, K.; Arshad, M. New iteration process and numerical reckoning fixed points in Banach spaces. Politehn. Univ. Bucharest Sci. Bull. Ser. A Appl. Math. Phys. 2017, 79, 113–122. [Google Scholar]
  23. Ullah, K.; Arshad, M. Numerical reckoning fixed points for Suzukis generalized nonexpansive mappings via new iteration process. Filomat 2018, 32, 187–196. [Google Scholar] [CrossRef]
  24. Hussain, N.; Ullah, K.; Arshad, M. Fixed point approximation of Suzuki generalized nonexpansive mappings via new faster iteration process. J. Nonlinear Convex Anal. 2018, 19, 1383–1393. [Google Scholar]
  25. Ullah, K.; Arshad, M. New three-step iteration process and fixed point approximation in Banach spaces. J. Linear. Topological. Algebra. 2018, 7, 87–100. [Google Scholar]
  26. Piri, H.; Daraby, B.; Rahrovi, S.; Ghasemi, M. Approximating fixed points of generalized α-nonexpansive mappings in Banach spaces by new faster iteration process. Numer. Algorithms 2019, 81, 1129–1148. [Google Scholar] [CrossRef]
  27. Bhutia, J.; Tiwary, K. New iteration process for approximating fixed points in Banach spaces. J. Linear. Topological. Algebra. 2019, 8, 237–250. [Google Scholar]
  28. Garodia, C.; Uddin, I. A new fixed point algorithm for finding the solution of a delay differential equation. AIMS Math. 2020, 5, 3182–3200. [Google Scholar] [CrossRef]
  29. Garodia, C.; Uddin, I. A new iterative method for solving split feasibility problem. J. Appl. Anal. Comput. 2020, 10, 986–1004. [Google Scholar] [CrossRef]
  30. Ali, F.; Ali, J.; Nieto, J.J. Some observations on generalized non-expansive mappings with an application. Comput. Appl. Math. 2020, 39, 74. [Google Scholar] [CrossRef]
  31. Ali, J.; Ali, F. A new iterative scheme to approximating fixed points and the solution of a delay differential equation. J. Nonlinear Convex Anal. 2020, 21, 2151–2163. [Google Scholar]
  32. Hassan, S.; De la Sen, M.; Agarwal, P.; Ali, Q.; Hussain, A. A new faster iterative scheme for numerical fixed points estimation of Suzuki’s generalized nonexpansive mappings. Math. Probl. Eng. 2020, 2020, 3863819. [Google Scholar] [CrossRef]
  33. Rani, A.; Arti. A new iteration process for approximation of fixed points for Suzuki’s generalized non-expansive mappings in uniformly convex Banach spaces. J. Math. Comput. Sci. 2020, 10, 2110–2125. [Google Scholar]
  34. Ahmad, J.; Ullah, K.; Arshad, M.; Ma, Z. A New Iterative Method for Suzuki Mappings in Banach Spaces. J. Math. 2021, 2021. [Google Scholar] [CrossRef]
  35. Zeidler, E. Nonlinear Functional Analysis and Its Applications. I; Wadsack, P.E., Translator; Fixed-Point Theorems; Springer: New York, NY, USA, 1986; pp. xxi+897. [Google Scholar] [CrossRef]
  36. Khan, S.H. A Picard-Mann hybrid iterative process. Fixed Point Theory Appl. 2013, 2013, 69. [Google Scholar] [CrossRef]
  37. Ali, F.; Ali, J. Convergence, stability, and data dependence of a new iterative algorithm with an application. Comput. Appl. Math. 2020, 39, 267. [Google Scholar] [CrossRef]
  38. Hussain, A.; Ali, D.; Karapinar, E. Stability data dependency and errors estimation for a general iteration method. Alex. Eng. J. 2021, 60, 703–710. [Google Scholar] [CrossRef]
  39. Hussain, A.; Hussain, N.; Ali, D. Estimation of Newly Established Iterative Scheme for Generalized Nonexpansive Mappings. J. Funct. Spaces 2021, 2021, 6675979. [Google Scholar] [CrossRef]
  40. Shukla, R.; Pant, R.; Sinkala, W. A General Picard–Mann iterative method for approximating fixed points of nonexpansive mappings with applications. Symmetry 2022, 14, 1741. [Google Scholar] [CrossRef]
Figure 1. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Figure 1. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Computation 10 00151 g001
Figure 2. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Figure 2. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Computation 10 00151 g002
Figure 3. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Figure 3. Convergence behavior with parameters α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2 and initial guess ( 0.3 , 0.3 ) .
Computation 10 00151 g003
Figure 4. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Figure 4. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Computation 10 00151 g004
Figure 5. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Figure 5. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Computation 10 00151 g005
Figure 6. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Figure 6. Convergence behavior with parameters α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 ) and initial guess ( 0.9 , 0.9 ) .
Computation 10 00151 g006
Table 1. Influence of initial guesses and parameters: comparison of various iterative methods.
Table 1. Influence of initial guesses and parameters: comparison of various iterative methods.
IterationsInitial Points
(0.3, 0.3)(0.6, 0.6)(0.9, 0.9)
Case (i): α n = 1 ( 3 n + 1 ) , β n = 1 ( n + 2 ) 2 , γ n = n ( n 3 + 10 )
GPM with k = 4 (27)262527
Bhutia and Tiwary (19)383538
Garodia and Uddin (20)353336
Garodia and Uddin, and Hussain et al. (D-iterative method) (21)353336
Hussain et al. (16)383638
Ullah and Arshad (17)534953
Piri et al. (18)524953
Ali et al. (23)353336
Rani and Arti (25)353336
Ahmad et al. (26)534953
Case (ii): α n = 1 ( 10 n + 100 ) 1 / 2 , β n = 1 ( n + 5 ) 3 , γ n = 1 ( 9 n + 10 ) 2
GPM with k = 4 (27)252426
Bhutia and Tiwary (19)383338
Garodia and Uddin (20)333134
Garodia and Uddin, and Hussain et al. (D-iterative method) (21)333134
Hussain et al. (16)383638
Ullah and Arshad (17)484548
Piri et al. (18)484549
Ali et al. (23)333134
Rani and Arti (25)333134
Ahmad et al. (26)484549
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Shukla, R.; Panicker, R. Approximating Fixed Points of Nonexpansive Type Mappings via General Picard–Mann Algorithm. Computation 2022, 10, 151. https://doi.org/10.3390/computation10090151

AMA Style

Shukla R, Panicker R. Approximating Fixed Points of Nonexpansive Type Mappings via General Picard–Mann Algorithm. Computation. 2022; 10(9):151. https://doi.org/10.3390/computation10090151

Chicago/Turabian Style

Shukla, Rahul, and Rekha Panicker. 2022. "Approximating Fixed Points of Nonexpansive Type Mappings via General Picard–Mann Algorithm" Computation 10, no. 9: 151. https://doi.org/10.3390/computation10090151

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop