Abstract
The celebrated Traub’s method involving Banach space-defined operators is extended. The main feature in this study involves the determination of a subset of the original domain that also contains the Traub iterates. In the smaller domain, the Lipschitz constants are smaller too. Hence, a finer analysis is developed without the usage of additional conditions. This methodology applies to other methods. The examples justify the theoretical results.
MSC:
49M15; 47H17; 65J15; 65G99; 41A25
1. Introduction
The purpose of this article is to locate a solution of equation
provided that is derivable according to Fréchet. Moreover, stand for Banach spaces, whereas is nonempty and open.
The famous quadratically convergent Newton–Kantorovich method is defined for all as
has been used extensively to produce sequence such that [1,2,3,4,5,6,7,8]. Although there is a plethora on convergence results for (2) there exist some problems. In particular, the convergence ball is in general small [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Hence, it is important to extend this ball but with no additional conditions. Other defects relate to the accuracy of bounds on or as well as the results on the location uniqueness of The same defects appear in the study of high convergence order methods [19,20,21,22]. We have developed a technique that helps determine some where iterates can also be found. This way, using D instead of , a finer analysis is possible with no additional conditions.
We demonstrate our techniques for a certain high convergence order, although it can similarly be used on other methods [12,15,16,17].
We extend the two step Traub method [21] (see also [18,22]) to the following three step fifth order method
Traub’s two-step method requires less computational effort than any third-order method utilizing the second derivative [2,4,5,14].
Let us provide the earlier results.
- (i)
- Convergence has been shown in Potra and Pták in [14] usingandandwhere is a closed ball with radius and center at The center-Lipschitz condition is introduced by us asDefine the setMoreover, we introduced the restricted-Lipschitz conditionHowever, then we notice thatandhold, sinceSupposeIt follows that (9), (13), andcan used for (4), (5), and respectively, given in [14] (Theorem 5.2, p. 79). Hence,andhold. So, the applicability of Traub’s method is extended. The parameters and L are special cases of so no additional effort is used. It is also worth to mention that but The proof in [14] (Theorem 5.2) utilizedleading to (by the Banach lemma [12] on linear invertible operators)However, we getleading to tighter
- (ii)
- In [12], they usedandwith denoting the minimal positive solution of equationIn our case we usewhereorifis used, instead, where is the minimal positive solution of equationThen, conditionis the corresponding and weaker sufficient convergence criterion. Notice again that, and The old estimate in [12] involving the bounds on iswhereas, we usewhich is more precise.
2. Majorizing Sequences
We introduce some auxiliary results on scalar majorizing sequences.
Definition 1.
Let be a Banach space valued sequence. Then, a nondecreasing scalar sequence is majorizing for if
So, the convergence of sequence reduces to studying that of [14]. Set and for some
Let be a parameter and be continuous and non-decreasing function. We shall use scalar sequences and defined for each by
where
Next, we present a convergence result for a sequence under very general conditions
Lemma 1.
Suppose:
- (a)
- for alland
- (b)
- Function is continuous, increasing andfor each Then, sequences converge monotonically to which is their unique upper bound (least).
Remark 1.
We introduce, sequences functions and sequences of functions as follows
and
Next, we present a second convergence result for
Lemma 2.
Suppose:
There exists parameter such that
Then, sequences converge to where Moreover, the following estimates hold
and
Furthermore, we have for each
Proof.
Estimates (24)–(28) hold if
and
hold for each However, they are true for by (21)–(23). Notice that we have by the definition (17) and these conditions that
Suppose, estimates (24)–(28) hold for all integers smaller or equal to Hence, by replacing by and using the induction hypotheses we see that (29)–(33) shall be true if
or
or
for and which holds true by (23). The induction for (24)–(28) is terminated. The remaining of the proof can be found in Lemma 1. □
Remark 2.
(a) The conditions of Lemma 2 imply those of Lemma 1 but not necessarily vice versa.
(b) Consider functions “φ“ to be given by and φ in the interesting case and for and
Then, consider functions on given by
and
By these definitions, we have
It follows from the intermediate value theorem (IVT) that functions have zeros in Denote the minimal such zeros by respectively.
Define parameters
and
Then, we can show a third result on the convergence of sequence
Lemma 3.
Suppose:
There exists satisfying
Then, the conclusions of Lemma 2 for a sequence follow.
Proof.
We must show by Lemma 2
But by the preceding definitions, we can show instead
We must relate to We can write
so
In particular, by (38) and the definition of
Define function by
Then, we have by (40)
So, we can show instead of (37) (for ) that
which is true by (35). Similarly, we get
so
In particular, we have
and again
Therefore, sequence is nondecreasing and bounded from above by so it converges to □
Next, we connect Lemmas 1 and 2 to method (3). We first consider conditions (A):
Suppose
- (A1)
- There exists such that and
- (A2)
- For all
- (A3)
- Function has a smallest positive solution Set
- (A4)
- For each
- (A5)
- Hypotheses of Lemma 1 or Lemma 2 or Lemma 3 hold and
- (A6)
- (or ).
Next, we prove the first semi-local convergence theorem for sequence
Theorem 1.
Suppose hypotheses (A) hold. Then, sequences produced by method (3) is well defined in remains in for each and converges to a solution (or ) of equation
Proof.
Using condition (A1) and the first substep of method (3) for we see that is well defined and
so Iterate is exists by (A1) and (3) for So, by (3) and (A3) one has
We also have so By condition (A1) and (3) for exists, and we can write
The condition (A2) and (17) give in turn that
We also have
so Let Using (A2) one obtains
so the Banach lemma for linear invertible operators [5] assures the existence of and
In particular for exists, so does iterate Then, we can write by the first substep of method (3) for
where we also used by the definition of the method
and
Hence, we showed so far
and
for Consider these estimates are true for all Then, simply replace by to terminate the induction for items (46)–(49). So, is fundamental in a Banach space Hence, Then, by letting in the estimate (see also (45))
and the continuity of we conclude □
A uniqueness of the solution result follows.
Proposition 1.
Suppose:
- (i)
- There exists a simple solution of equation
- (ii)
- There exists such that
Set Then, is unique in
Proof.
Let with Set Then, in view of (A2), we obtain in turn that
so follows from the invertability of M and the estimate □
3. Examples
We present examples to further justify the theoretical results.
Example 1.
Consider
where are parameters. Then, clearly for large and small, can be small (arbitrarily). Notice that as
Example 2.
If and for define polynomial ψ on as
If we consider case 1 of Newton’s method, then, we obtain and But then, for all So, Theorem 5.2 in [14] cannot assure convergence. However, we have for all Hence, our result guarantees convergence to as long as
Example 3.
Let the domain of functions given on which are continuous. We consider the max-norm. Choose Define G on Ω be
is given, δ is a parameter and N is the Green’s kernel given by
In Table 1 that follows we have listed the results on the convergence criteria for various values of the parameter involved.
Table 1.
Comparison table of criteria.
Example 4.
Let , and be as in the Example 3. It is well known that the boundary value problem [16]
can be presented as a Hammerstein-like nonlinear integral equation [12]
for ℓ being a parameter, consider given by
Choose and Then, clearly since Suppose Then, conditions (A) are satisfied for
and Notice that
4. Conclusions
Two different techniques and a new domain D included in the original one are introduced. This change in the analysis gives a finer convergence with no additional conditions.
Author Contributions
Conceptualization, S.R., C.I.A., I.K.A. and S.G.; methodology, S.R., C.I.A., I.K.A. and S.G.; software, S.R., C.I.A., I.K.A. and S.G.; validation, S.R., C.I.A., I.K.A. and S.G.; formal analysis, S.R., C.I.A., I.K.A. and S.G.; investigation, S.R., C.I.A., I.K.A. and S.G.; resources, S.R., C.I.A., I.K.A. and S.G.; data curation, S.R., C.I.A., I.K.A. and S.G.; writing—original draft preparation, S.R., C.I.A., I.K.A. and S.G.; writing—review and editing, S.R., C.I.A., I.K.A. and S.G.; visualization, S.R., C.I.A., I.K.A. and S.G.; supervision, S.R., C.I.A., I.K.A. and S.G. project administration, S.R., C.I.A., I.K.A. and S.G.; funding acquisition, S.R., C.I.A., I.K.A. and S.G. 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.
References
- Argyros, I.K. On the Newton-Kantorovich hypothesis for solving equations. J. Comput. Math. 2004, 169, 315–332. [Google Scholar] [CrossRef] [Green Version]
- Argyros, I.K. Convergence and Applications of Newton-Type Iterations; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
- Argyros, I.K.; Hilout, S. Weaker conditions for the convergence of Newton’s method. J. Complex. 2012, 28, 364–387. [Google Scholar] [CrossRef] [Green Version]
- Argyros, I.K.; Magréñan, A.A. A Contemporary Study of Iterative Methods; Elsevier (Academic Press): New York, NY, USA, 2018. [Google Scholar]
- Kantorovich, L.V.; Akilov, G.P. Functional Analysis; Pergamon Press: Oxford, UK, 1982. [Google Scholar]
- Nashed, M.Z.; Chen, X. Convergence of Newton-like methods for singular operator equations using outer inverses. Numer. Math. 1993, 66, 235–257. [Google Scholar] [CrossRef]
- Ortega, L.M.; Rheinboldt, W.C. Iterative Solution of Nonlinear Equations in Several Variables; Academic Press: New York, NY, USA, 1970. [Google Scholar]
- Zabrejko, P.P.; Nguen, D.F. The majorant method in the theory of Newton-Kantorovich approximations and the Pták error estimates. Numer. Funct. Anal. Optim. 1987, 9, 671–684. [Google Scholar] [CrossRef]
- Argyros, I.K. Computational Theory of Iterative Methods; Chui, C.K., Wuytack, L., Eds.; Series: Studies in Computational Mathematics 15; Elsevier Publishing Co.: New York, NY, USA, 2007. [Google Scholar]
- Argyros, I.K.; Hilout, S. On an improved convergence analysis of Newton’s method. Appl. Math. Comput. 2013, 225, 372–386. [Google Scholar] [CrossRef]
- Behl, R.; Maroju, P.; Martinez, E.; Singh, S. A study of the local convergence of a fifth order iterative method. Indian J. Pure Appl. Math. 2020, 51, 439–455. [Google Scholar]
- Ezquerro, J.A.; Hernandez, M.A. Newton’s Method: An Updated Approach of Kantorovich’s Theory; Birkhäuser: Cham, Switzerland, 2018. [Google Scholar]
- Magréñan, A.A.; Gutiérrez, J.M. Real dynamics for damped Newton’s method applied to cubic polynomials. J. Comput. Appl. Math. 2015, 275, 527–538. [Google Scholar] [CrossRef]
- Potra, F.A.; Pták, V. Nondiscrete Induction and Iterative Processes; Research Notes in Mathematics, 103; Pitman Advanced Publishing Program: Boston, MA, USA, 1984. [Google Scholar]
- Proinov, P.D. New general convergence theory for iterative processes and its applications to Newton-Kantorovich type theorems. J. Complex. 2010, 26, 3–42. [Google Scholar] [CrossRef] [Green Version]
- Shakhno, S.M.; Iakymchuk, R.P.; Yarmola, H.P. Convergence analysis of a two step method for the nonlinear squares problem with decomposition of operator. J. Numer. Appl. Math. 2018, 128, 82–95. [Google Scholar]
- Sharma, J.R.; Guha, R.K.; Sharma, R. An efficient fourth order weighted-Newton method for systems of nonlinear equations. Numer. Algorithms 2013, 62, 307–323. [Google Scholar] [CrossRef]
- Verma, R. New Trends in Fractional Programming; Nova Science Publisher: New York, NY, USA, 2019. [Google Scholar]
- Rheinboldt, W.C. An Adaptive Continuation Process of Solving Systems of Nonlinear Equations; Polish Academy of Science, Banach Center Publications: Greifswald, Germany, 1978; Volume 3, pp. 129–142. [Google Scholar]
- Soleymani, F.; Lotfi, T.; Bakhtiari, P. A multi-step class of iterative methods for nonlinear systems. Optim. Lett. 2014, 8, 1001–1015. [Google Scholar] [CrossRef]
- Traub, J.F. Iterative Methods for the Solution of Equations; Prentice Hall: Upper Saddle River, NJ, USA, 1964. [Google Scholar]
- Traub, J.F.; Werschulz, A.G. Complexity and Information, Lezioni Lince; Lincei Lectures; Cambridge University Press: Cambridge, UK, 1998; p. xii+139. ISBN 0-521-48506-1. [Google Scholar]
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