Extensions on a local convergence result by Dennis and Schnabel for Newton's method with applications
Abstract
The aim of this article is to extend the applicability of Newton's method involving k-Fréchet differentiable operators. By using tighter majorizing functions and under the same computational cost as in earlier works, we find at least as large radius of convergence and at least as tighter error bounds on the distances involved. Numerical examples further validate the theoretical results.
1 INTRODUCTION
Iteration (2) is Newton's method, presented in 1669 by Isaac Newton just for polynomials. Later, Joseph Raphson proposed a generalized Newton's method for most global functions H. For this reason, the Newton method is also called: Newton–Raphson method. This method converges quadratically close to the root. This fact was shown in 1818 by Joseph Fourier. On the other hand, a multidimensional extension was presented by Augustin Louis Cauchy (1829, 1847). An extension was given in 1948 (see Reference 4) to functional spaces, commonly known as the Newton–Kantorovich method. Hundreds of articles using the Newton–Kantorovich method in Banach spaces have been written, ever since, including applications using Newton-type methods (see References 3-5 and the references therein).
Let be the space of continuous linear operators from to .
- (A1)
There exists for some , and ,
- (A2)
, ,
- (A3)
and .
- (B1)
For , the operator exists, and , where is a solution of (1),
- (B2)
For all , .
For and , under and , Dennis and Schnabel showed that, for any initial selection in , Newton's method is convergent.
If (1) is a polynomial equation of degree m, we have an interesting case, since the function satisfies for every . Hence, conditions (3) and (4) are satisfied by . If we use Taylor's series for more general equations, we can approximate Equation (1) by polynomial equations.
Notice that a radius of convergence defines a ball with center such that if we pick any initial point from this ball the convergence of the method to is guaranteed.
The rest of the article is structured in the following way: in Section 2, we present a proof for a new local convergence result of the Newton method. In Section 3, five examples are presented where our results compare favorably to previous ones.6, 10 In particular, with the new technique more initial points become available, since the radius of convergence is extended. Moreover, fewer iterates are computed to obtain a certain accuracy, since the new bounds on are more precise. Furthermore, a uniqueness result is presented not given in the earlier articles.
The novelty of our article is that these extensions are realized without additional conditions, since the new conditions specialize to the earlier ones. We also note that our results are important, since more starting points become available, if the radius of convergence is larger. Moreover, fewer iterations are needed, if the upper bounds on are tighter than in earlier articles. The developed technique is so general that it can be used to extend the usage of other methods in an analogous way.
The Dennis and Schnabel result for Newton's method is seminal. But it has stayed unchallenged for a long time. We were able to extend this result by introducing and combining our ideas of the center-Lipschitz condition together with the notion of the restricted convergence domain. Moreover, our technique is so general that we plan to use it in future works to extend the applicability of other iterative methods requiring (or not) m-Fréchet differentiable operators.
2 CONVERGENCE ANALYSIS
In this section, we develop a new result about local convergence of Newton's method using conditions (4) and (6), instead of (3) and (4) as in Reference 14 or only (3) as in Reference 6. The original idea was presented by Dennis and Schnabel in Reference 6 and analyzed by others in Reference 14.
Theorem 1.Let an open convex subset of a Banach space with correspondence in a Banach space , the nonlinear operator H is times continuously differentiable operator in the sense of Fréchet from D into . Suppose that is a solution of such that and (for ) with and the operator exists. Consider that conditions (4) and (6), for each , are satisfied and for the equation
Proof.Let . First, we prove, for all , that there exists and . For this, we use (4) to obtain
It follows from (10) and the Banach lemma on invertible operators,15 that exists and .
As , then the operator exists,
Thus, we obtain that
Hence, we conclude and . On the other hand, (9) follows from (8) and
Remark 1.
- (a)
The results hold with replacing if for each .
- (b)
As we can see in (9) the Q-order of convergence is at least two5 for Newton's method. If , then we obtain
- (c)
The corresponding equations in Reference 14 are obtained from (8) by setting and . Denote by , , the corresponding solutions, respectively. Then, we have
()Moreover, the corresponding upper error bounds on are()Hence, the new radii of convergence are larger and the error bounds are tighter under our new approach (see also the numerical examples).
Next, we obtain estimates under different conditions, so we can compare them to each other.
Remark 2.
- (a)
Using only (6), we have that
()provided that . Equation (13) is now presented by()provided that Equation (14) has a smallest positive solution denoted by . Then, the error estimate is() - (b)
The corresponding to (14) equation in Reference 6 is given for by
()and the error estimates()where is the least positive solution of (16) and is as in (13) but , are and , respectively.
Then, it follows from (3), (4), (6), (7), and the above definitions that the new error bounds are at least as tight whereas the new convergence radii at least as large as the ones in Reference 14 or Reference 6 (see also the numerical examples). We can certainly set . In particular, we have
Remark 3.
- (a)
We can develop another approach as follows: there exist two nondecreasing continuous functions and with and such that
()and()Note that in general()hold and can be arbitrarily small.3, 8We get the estimate using (4) and (22)
()and is a radius of convergence provided by the smallest positive solution of equation()But if we use (4) and (21), we get
()and denotes the least positive solution of()If we only use (20), we get
()and denotes the least positive solution of()Then, we have
()and() - (b)
Uniqueness of the solution results can be found in References 3-5, 7, 8, 11, and 14.
Next, we complete this section by presenting a more general uniqueness of the solution result than the ones given in References 3-5, 7, 8, 11, and 14.
Proposition 1.Suppose: equation has a simple solution such that (4) holds; and there exists such that
Then, is the only solution of equation in .
3 APPLICATIONS
We present five examples in this section, so we can compare the current results to the ones given in the previous articles.
Example 1.Let , , and . Consider the operator H from D to by
We get for that
Using (3), (4), and (32)–(35), since and , we can define functions
We can see that
Besides, we have the relation (18) (see Table 2).
In Table 3, the a priori error bounds are obtained, for and as a starting point, to and , respectively. We can see that the new results improve the ones appearing in References 6, 10, and 14.
We see that the results on the calculations for the a priori error bounds are tighter than before in Reference 14 performing the same calculation for .
m | |||
---|---|---|---|
3 | 0.20087401… | 0.31732628… | 0.32692337… |
4 | 0.21647918… | 0.31648485… | 0.31759589… |
5 | 0.21783205… | 0.30939518… | 0.30947896… |
6 | 0.21790855… | 0.30388105… | 0.30388589… |
m | ||
---|---|---|
3 | 0.20087401… | 0.20985708… |
4 | 0.21647918… | 0.21727336… |
5 | 0.21783205… | 0.21787644… |
6 | 0.21790855… | 0.21791049… |
n | ||
---|---|---|
0 | 1.18879312… | 1.44160846… |
1 | 0.09375885… | 0.11369813… |
2 | 0.00135374… | 0.00164163… |
3 | 0.00000051… | 0.00000062… |
0.24525296… | 0.32494723… | 0.38269191… |
Example 2.If we adopt the maximum norm with and , let . Set . Suppose that
Finally, Equations (19) and (29) are satisfied as we can see in Tables 7 and 8.
m | |||
---|---|---|---|
3 | 0.25819888… | 0.5 | 0.53801683… |
4 | 0.24814046… | 0.47268777… | 0.47750900… |
5 | 0.25748855… | 0.47933648… | 0.47999293… |
6 | 0.25816187… | 0.47684219… | 0.47690570… |
m | ||
---|---|---|
3 | 0.25819888… | 0.31622776… |
4 | 0.24814046… | 0.25127852… |
5 | 0.25748855… | 0.25772381… |
6 | 0.25816187… | 0.25817420… |
n | ||
---|---|---|
0 | 0.26666666… | 0.4 |
1 | 0.16332703… | 0.24499054… |
2 | 0.01772753… | 0.02659130… |
3 | 0.00001092… | 0.00001638… |
0.25 | 0.33333333… | 0.4 |
In Example 3, we show that results before Remark 3 may not be applicable, since does not exist. But we then handle this problem by our results given in Remark 3. In the fourth example, we show how the conditions are satisfied for . In particular, a larger radius of convergence is obtained than the one given by Dennis and Schnabel.6 In the fifth example, a large nonlinear system is solved.
Example 3.Let , . Define f on D by
0.00689682… | 0.00689682… | 0.01029144… |
Example 4.Let and . Define f on D by
Example 5.Consider the system
If , we get . Moreover, choose . Then, for , Equation (29) is verified as we can see in Table 10.
0.08333333… | 0.08333333… | 0.11673277… |
4 CONCLUSIONS
The application of Newton's method has been extended by using a new technique. The radius of convergence is enlarged, whereas the new error bounds are tighter than in earlier works. In particular, we extend the usage of the Dennis and Schnabel local result involving Newton's method.6, 7 This technique can be used to improve the applicability of other methods, since it is so general.1, 3-5, 12, 13
CONFLICT OF INTEREST
The authors declare no potential conflict of interests.
AUTHOR CONTRIBUTIONS
All authors have equally contributed to this work. All authors read and approved the final manuscript.
Biographies
Ioannis K. Argyros obtained his PhD degree from the University of Georgia, Athens. He has published over 1000 papers, 35 books and 21 chapters in book. He is editor in numerous journals in computational mathematics, active reviewer, and grant evaluator. His research is at the border of image processing, numerical solutions of PDE, iterative methods, and matrix equations. His teaching and research has been in the areas of iterative methods, numerical functional analysis, scientific computing, optimization, and banach spaces.
Michael Argyros is a research scientist with interests in algorithmic analysis, scientific computing, artificial intelligence, and computational and applied mathematics. He has published sixteen papers and one book in these areas. He is currently working at the Department of Computer Science, University of Oklahoma, Oklahoma, USA.
Johan Ceballos earned his Ph.D. in Mathematics Engineering, from Universidad Carlos III in Madrid. He is interested in numerical linear algebra, numerical analysis and in the last three years in Clifford analysis.
Mariana Ceballos is currently studying Economics and Political Science at Universidad Autónoma de Manizales, Colombia. She is interested in applied economics and numerical methods.
Daniel González earned a doctorate in Applied Mathematics studying initial value problems for the Newton method in Banach spaces in the Universidad de La Rioja, in Spain. González is a prolific researcher on top of the journals in the field of numerical analysis, focused principally on nonlinear, integral, and differential equations using iterative processes. He is also interested in pedagogical processes to teach mathematics in higher education and currently heads several international projects.