Volume 3, Issue 5 e1179
RESEARCH ARTICLE
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Extensions on a local convergence result by Dennis and Schnabel for Newton's method with applications

Ioannis K. Argyros

Ioannis K. Argyros

Department of Mathematical Sciences, Cameron University, Lawton, Oklahoma, USA

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Michael Argyros

Michael Argyros

Department of Computing and Technology, Cameron University, Lawton, Oklahoma, USA

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Johan Ceballos

Johan Ceballos

Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador

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Mariana Ceballos

Mariana Ceballos

Facultad de Estudios Sociales y Empresariales, Universidad Autónoma de Manizales, Manizales, Colombia

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Daniel González

Corresponding Author

Daniel González

Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador

Correspondence Daniel González, Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Av. de los Granados E12-41 y Colimes esq., EC170125 Quito, Ecuador.

Email: [email protected]

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First published: 27 June 2021

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

In this article, we approximate a solution ϕ of
H ( ϕ ) = 0 , ()
using the operator H : D E 2 , which is considered to be continuously differentiable in the Fréchet sense. The set D E 1 is open, convex; and E 1 , E 2 are Banach spaces.
In computational sciences and other disciplines, applications appear like Equation (1) (in general) using mathematical modeling.1-3 It is rarely attainable to find solutions for these equations in closed form. Consequently, most methods to obtain the solution are iterative. There exist two categories of procedures to study for iterative methods: local and semilocal convergence analysis. To guarantee the semilocal convergence, it is necessary to provide conditions on the iterative procedure around an initial point; whereas the local one is, based on the information around a solution, to find estimates of the radii for the convergence balls. The Newton method is connected with the problem of finding such solutions. Linearization is the idea behind Newton's method. We consider a differentiable function H : . If we start from an initial guess: H ( ϕ 0 + h ) H ( ϕ 0 ) + H ( ϕ 0 ) h , we obtain linear approximation in a region around ϕ 0 . Finally, we let H ( ϕ 0 ) + H ( ϕ 0 ) h = 0 , to develop the recurrent method:
ϕ n + 1 = ϕ n [ H ( ϕ n 1 ) ] 1 H ( ϕ n ) , for n = 0 , 1 , 2 , ()

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 ( E 2 , E 1 ) be the space of continuous linear operators from E 2 to E 1 .

The Kantorovich theorem in the non-affine form on Banach spaces states first the semilocal convergence:
  • (A1)

    There exists Λ 0 = [ H ( ϕ 0 ) ] 1 ( E 2 , E 1 ) for some ϕ 0 D , Λ 0 b and Λ 0 H ( ϕ 0 ) d ,

  • (A2)

    H ( ϕ ) T , ϕ D ,

  • (A3)

    T b d 1 2 and B ϕ 0 , 1 1 2 T b d T b D .

Dennis and Schnabel6 presented an important local result applied to the Newton method using the following conditions:
  • (B1)

    For r , β > 0 , the operator [ H ( ϕ ) ] 1 exists, B ( ϕ , r ) D and [ H ( ϕ ) ] 1 β , where ϕ is a solution of (1),

  • (B2)

    For all ϕ D , H ( ϕ ) T .

For ϵ = min { r , R } and R = 1 2 β T , under ( B 1 ) and ( B 2 ) , Dennis and Schnabel showed that, for any initial selection in B ( ϕ , ϵ ) , Newton's method is convergent.

We give a generalization for the local converge requirements utilized by Dennis and Schnabel in ( B 1 ) and ( B 2 ) . Under these generalized conditions, the local convergence of Newton's method is analyzed. Recently, researchers have presented the conditions for the local and semilocal convergence of Newton's method, by weakening the conditions on the operator H (see References 1-13 and the references therein). Taking into account the condition H ( ϕ ) ω ( ϕ ) for ϕ D , we presented a generalization of ( B 2 ) ,10 where we considered the continuous nondecreasing function ω : [ 0 , + ) with ω ( 0 ) 0 . In that article, we generalized the above conditions but in an affine invariant form. More precisely, we suppose that
[ H ( ϕ ) ] 1 H ( m ) ( ϕ ) ω ( ϕ ) , ϕ D , m 3 , ()
and
[ H ( ϕ ) ] 1 ( H ( ϕ ) H ( ϕ ) ) ω 0 ( ϕ ϕ ) , ϕ D , ()
where we have the continuous and nondecreasing functions ω 0 , ω : [ 0 , + ) with ω 0 ( 0 ) , ω ( 0 ) 0 . Benefits of considering the solutions in the affine way instead of the non-affine form are given in Reference 3.

If (1) is a polynomial equation of degree m, we have an interesting case, since the function H ( m ) ( ϕ ) satisfies [ H ( ϕ ) ] 1 H ( m ) ( ϕ ) T for every ϕ D . Hence, conditions (3) and (4) are satisfied by [ H ( ϕ ) ] 1 H ( m ) ( ϕ ) . If we use Taylor's series for more general equations, we can approximate Equation (1) by polynomial equations.

The results presented before can be improved if we suppose that ρ > 0 is the least solution of
ω 0 ( t ) 1 = 0 . ()
Set D 0 = D B ( ϕ , ρ ) . Moreover, suppose that there exists function ω : [ 0 , ρ ) [ 0 , + ) nondecreasing and continuous such that
[ H ( ϕ ) ] 1 H ( m ) ( ϕ ) ω ( ϕ ) , ϕ D 0 , m 3 . ()
Therefore, we have
ω ( t ) ω ( t ) for each t [ 0 , ρ ) , ()
in view of the fact that D 0 D .

Notice that a radius of convergence defines a ball with center ϕ such that if we pick any initial point from this ball the convergence of the method to ϕ 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 ϕ n ϕ 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 ϕ n ϕ 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 D an open convex subset of a Banach space E 1 with correspondence in a Banach space E 2 , the nonlinear operator H is m = 3 , 4 , 5 , times continuously differentiable operator in the sense of Fréchet from D into E 2 . Suppose that ϕ is a solution of H ( ϕ ) = 0 such that B ( ϕ , r ) D and [ H ( ϕ ) ] 1 H ( i ) ( ϕ ) α i (for 2 i m 1 ) with r , α i > 0 and the operator [ H ( ϕ ) ] 1 exists. Consider that conditions (4) and (6), ω 0 ( t ) ω ( t ) for each t [ 0 , r ) , are satisfied and for the equation

m 1 m i = 1 m 2 α i + 1 i ! t i 1 + t m 2 ( m 1 ) ! ω ( ϕ + t ) t + ω 0 ( t ) 1 = 0 ()
there exists a smallest positive solution denoted by R 0 . Then, for every ϕ 0 B ( ϕ , ϵ ) , ϵ > 0 , sequence { ϕ n } generated by Newton's method is well-constructed, lim n + { ϕ n } = ϕ , and
ϕ ϕ n < 1 ϵ ϕ ϕ n 1 2 n . ()

Proof.Let ϵ = min { r , R 0 } . First, we prove, for all ϕ B ( ϕ , ϵ ) , that there exists [ H ( ϕ ) ] 1 and [ H ( ϕ ) ] 1 H ( ϕ ) 1 1 ω 0 ( R 0 ) . For this, we use (4) to obtain

I [ H ( ϕ ) ] 1 H ( ϕ ) ω 0 ( ϕ ϕ ) ω 0 ( R 0 ) < 1 . ()

It follows from (10) and the Banach lemma on invertible operators,15 that [ H ( ϕ ) ] 1 exists and [ H ( ϕ ) ] 1 H ( ϕ ) 1 1 ω 0 ( ϕ ϕ ) 1 1 ω 0 ( R 0 ) .

As ϕ 0 B ( ϕ , ϵ ) , then the operator Λ 0 = [ H ( ϕ 0 ) ] 1 exists,

[ H ( ϕ ) ] 1 Λ 0 1 1 ω 0 ( ϕ 0 ϕ ) 1 1 ω 0 ( R 0 )
and ϕ 1 is well-defined. Moreover,
ϕ 1 ϕ = ϕ 0 Λ 0 H ( ϕ 0 ) ϕ = Λ 0 0 1 ( i = 2 m 1 1 ( i 2 ) ! H ( i ) ( ϕ ) ( t 1 ) i 2 ( ϕ x 0 ) i 2 + 1 ( m 3 ) ! 0 1 H ( m ) ϕ + s ( t 1 ) ( ϕ ϕ 0 ) × ( 1 s ) m 3 ( t 1 ) m 2 ( ϕ ϕ 0 ) m 2 d s ) ( 1 t ) ( ϕ ϕ 0 ) 2 d t ,
since
H ϕ 0 + t ( ϕ ϕ 0 ) = i = 2 m 1 ( t 1 ) i 2 ( i 2 ) ! H ( i ) ( ϕ ) ( ϕ ϕ 0 ) i 2 + 1 ( m 3 ) ! 0 1 H ( m ) ϕ + s ( t 1 ) ( ϕ ϕ 0 ) × ( 1 s ) m 3 ( t 2 ) ϕ 2 ( ϕ ϕ 0 ) m 2 d s .

Thus, we obtain that

ϕ ϕ 1 1 1 ω 0 ( ϕ 0 ϕ ) [ i = 2 m 1 i 1 i ! α i ϕ ϕ 0 i 1 + m 1 m ! ω ( ϕ + ϵ ) ϕ ϕ 0 m 1 ] ϕ ϕ 0 1 1 ω 0 ( ϕ 0 ϕ ) [ i = 1 m 2 i ( i + 1 ) ! α i + 1 R 0 i + m 1 m ! ω ( ϕ + R 0 ) R 0 m 1 ] ϕ ϕ 0 1 1 ω 0 ( R 0 ) [ i = 1 m 2 i ( i + 1 ) ! α i + 1 R 0 i + m 1 m ! ω 0 ( ϕ + R 0 ) R 0 m 1 ] ϕ ϕ 0 < ϕ ϕ 0 R 0 ,
since
i = 2 m 1 i 1 i ! α i ϕ ϕ 0 i 1 i = 1 m 2 i ( i + 1 ) ! α i + 1 R 0 i ,
m 1 m ! ω ( ϕ + ϵ ) ϕ ϕ 0 m 1 m 1 m ! ω ( ϕ + R 0 ) R 0 m 1 ,
i = 1 m 2 i ( i + 1 ) ! α i + 1 R 0 i m 1 m ! i = 1 m 2 1 i ! ·
That is, ϕ 1 B ( ϕ , ϵ ) . Following now an inductive argument, for all n , we have that
ϕ ϕ n 1 1 ω 0 ( ϕ ϕ n ) i = 2 m 1 i 1 i ! α i R 0 i 2 + m 1 m ! ω ( ϕ + R 0 ) R 0 m 1 × ϕ ϕ n 1 2 1 1 ω 0 ( R 0 ) m 1 m [ i = 2 m 1 i 1 i ! α i R 0 i 2 + m 1 m ! ω 0 ( ϕ + R 0 ) R 0 m 1 ] × R 0 ϕ ϕ n 1 < ϕ ϕ n 1 .

Hence, we conclude ϕ n B ( ϕ , ϵ ) and lim n + ϕ n = ϕ . On the other hand, (9) follows from (8) and

1 1 ω 0 ( R 0 ) m 1 m i = 1 m 2 α i + 1 i ! R 0 i 1 + R 0 m 2 ( m 1 ) ! ω ( ϕ + R 0 ) = 1 R 0 < 1 ϵ ·

Remark 1. 

  • (a)

    The results hold with ω 0 replacing ω if ω ( t ) ω 0 ( t ) for each t [ 0 , r ) .

  • (b)

    As we can see in (9) the Q-order of convergence is at least two5 for Newton's method. If ϵ < 1 , then we obtain

    ϕ n ϕ < 1 ϵ ϕ n 1 ϕ 2 1 ϵ 1 + 2 + + 2 n 1 ϕ 0 ϕ 2 n < ϵ 2 n ϵ .
    Hence, the R-order of convergence is at least two for Newton's method.5

  • (c)

    The corresponding equations in Reference 14 are obtained from (8) by setting ω ( t ) = ω ( t ) = ω 0 ( t ) and ω ( t ) = ω ( t ) . Denote by r 0 , R 0 , the corresponding solutions, respectively. Then, we have

    r 0 R 0 R 0 . ()
    Moreover, the corresponding upper error bounds on ϕ n ϕ are
    c n = d n 2 1 ω ( ϕ n ϕ ) ,
    b n = d n 2 1 ω 0 ( ϕ n ϕ ) ,
    a n = d n 1 1 ω 0 ( ϕ n ϕ ) ,
    where
    d n 2 = m 1 m i = 1 m 2 α i + 1 i ! ϕ n ϕ i 1 + ϕ n ϕ m 2 ( m 1 ) ! ω ( ϕ + ϕ n ϕ )
    and
    d n 1 = m 1 m i = 1 m 2 α i + 1 i ! ϕ n ϕ i 1 + ϕ n ϕ m 2 ( m 1 ) ! ω ( ϕ + ϕ n ϕ ) ,
    so
    a n b n c n . ()
    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

    I [ H ( ϕ ) ] 1 H ( ϕ ) [ H ( ϕ ) ] 1 0 1 H ( ϕ + ζ ( ϕ ϕ ) ) d ζ ( ϕ ϕ ) [ H ( ϕ ) ] 1 0 1 i = 2 m 1 ( ζ 1 ) i 2 ( i 2 ) ! H ( i ) ( ϕ ) ( ϕ ϕ ) i 2 d ζ ( ϕ ϕ ) + 1 ( m 3 ) ! 0 1 0 1 [ H ( ϕ ) ] 1 H ( m ) ϕ + s ( ζ 1 ) ( ϕ ϕ ) × ( 1 s ) m 3 ( 1 ζ ) m 2 ϕ ϕ m 1 d s d ζ ] [ i = 2 m 1 1 ( i 1 ) ! α i ϕ ϕ i 1 + 1 ( m 3 ) ! ω ( ϕ + ϵ ) 0 1 ( 1 s ) m 3 d s × 0 1 ( 1 ζ ) m 2 d ζ ϕ ϕ m 1 ] [ i = 1 m 2 1 i ! α i + 1 ϵ i 1 + 1 ( m 1 ) ! ω ( ϕ + ϵ ) ϵ m 2 ] ϵ ,
    since
    H ϕ + ζ ( ϕ ϕ ) = i = 2 m 1 ( ζ 1 ) i 2 ( i 2 ) ! H ( i ) ( ϕ ) ( ϕ ϕ ) i 2 + 1 ( m 3 ) ! 0 1 H ( m ) ϕ + s ( ζ 1 ) ( ϕ ϕ ) ( 1 s ) m 3 ( ζ 1 ) m 2 ( ϕ ϕ ) m 2 d s ,
    0 1 ( i = 2 m 1 ( ζ 1 ) i 2 ( i 2 ) ! H ( i ) ( ϕ ) ( ϕ ϕ ) i 2 d ζ ( ϕ ϕ ) i = 2 m 1 1 ( i 1 ) ! α i ϕ ϕ i 1 , H ( m ) ϕ + s ( ζ 1 ) ( ϕ ϕ ) ω ( ϕ + ϵ ) ,
    so
    I [ H ( ϕ ) ] 1 H ( ϕ ) [ i = 1 m 2 α i + 1 i ! r 0 i 1 + r 0 m 2 ( m 1 ) ! ω ( ϕ + r 0 ) ] r 0 = δ < 1 , ()
    provided that δ 0 , m 2 m 1 . Equation (13) is now presented by
    i = 1 m 2 α i + 1 i ! t i 1 + t m 2 ( m 1 ) ! ω ( ϕ + t ) t δ = 0 , ()
    provided that Equation (14) has a smallest positive solution denoted by r 0 . Then, the error estimate is
    ϕ ϕ n 1 1 δ ( i = 2 m 1 i 1 i ! α i r 0 i 2 + m 1 m ! ω ( ϕ + r 0 ) ) ϕ ϕ n 2 δ ( m 1 ) ( 1 δ ) m ϕ ϕ n 1 : = b n , δ . ()

  • (b)

    The corresponding to (14) equation in Reference 6 is given for δ 0 ( 0 , m 2 m 1 ) by

    i = 1 m 2 α i + 1 i ! t i 1 + t m 2 ( m 1 ) ! ω ( ϕ + t ) t δ 0 = 0 ()
    and the error estimates
    ϕ ϕ n 1 1 δ 0 i = 2 m 1 i 1 i ! α i r 0 i 2 + m 1 m ! ω ( ϕ + r 0 ) ϕ ϕ n 2 δ 0 ( m 1 ) ( 1 δ 0 ) m ϕ ϕ n 1 : = b n , δ 0 , ()
    where r 0 is the least positive solution of (16) and δ 0 is as δ in (13) but r 0 , ω are r 0 and ω , 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 δ = δ 0 = λ 0 . In particular, we have

r 0 r 0 ()
and
b n , δ b n , δ 0 . ()

Remark 3. 

  • (a)

    We can develop another approach as follows: there exist two nondecreasing continuous functions ω 1 : [ 0 , + ) and ω 1 ( t ) : [ 0 , ρ ) [ 0 , + ) with ω 1 ( 0 ) 0 and ω 1 ( 0 ) 0 such that

    [ H ( ϕ ) ] 1 ( H ( ψ ) H ( ϕ ) ) ω 1 ( ψ ϕ ) , for each ϕ and ψ D ()
    and
    [ H ( ϕ ) ] 1 ( H ( ψ ) H ( ϕ ) ) ω 1 ( ψ ϕ ) , for each ϕ and ψ D 0 . ()
    Note that in general
    ω 0 ( t ) ω 1 ( t ) and ω 1 ( t ) ω 1 ( t ) for each t [ 0 , ρ ] ()
    hold and ω 0 ω 1 can be arbitrarily small.3, 8

    We get the estimate using (4) and (22)

    ϕ n + 1 ϕ = ϕ n ϕ H ( ϕ n ) 1 H ( ϕ n ) H ( ϕ n ) 1 H ( ϕ ) 0 1 H ( ϕ ) 1 ( H ( ϕ + θ ( ϕ n ϕ ) ) H ( ϕ n ) ) d θ ( ϕ n ϕ ) 0 1 ω 1 ( ( 1 θ ) ϕ n ϕ ) d θ 1 ω 0 ( ϕ n ϕ ) ϕ n ϕ = e n 2 ϕ n ϕ ,
    where
    e n 2 = 0 1 ω 1 ( ( 1 θ ) ϕ n ϕ ) d θ 1 ω 0 ( ϕ n ϕ ) ()
    and ρ 2 is a radius of convergence provided by the smallest positive solution of equation
    0 1 ω 1 ( ( 1 θ ) t ) d t + ω 0 ( t ) 1 = 0 . ()

    But if we use (4) and (21), we get

    ϕ n + 1 ϕ 0 1 ω 1 ( ( 1 θ ) t ) d θ 1 ω 0 ( t ) ϕ n ϕ = e n 1 ϕ n ϕ ,
    where
    e n 1 = 0 1 ω 1 ( ( 1 θ ) t ) d θ 1 ω 0 ( t ) , ()
    and ρ 1 denotes the least positive solution of
    0 1 ω 1 ( ( 1 θ ) t ) d t + ω 0 ( t ) 1 = 0 . ()

    If we only use (20), we get

    ϕ n + 1 ϕ 0 1 ω 1 ( ( 1 θ ) t ) d θ 1 ω 1 ( t ) ϕ n ϕ = e n 3 ϕ n ϕ ,
    where
    e n 3 = 0 1 ω 1 ( ( 1 θ ) t ) d θ 1 ω 1 ( t ) , ()
    and ρ 3 denotes the least positive solution of
    0 1 ω 1 ( ( 1 θ ) t ) d t + ω 1 ( t ) 1 = 0 . ()

    Then, we have

    ρ 3 ρ 2 ρ 1 ()
    and
    e n 1 e n 2 e n 3 . ()

  • (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 H ( ϕ ) = 0 has a simple solution ϕ D such that (4) holds; and there exists a 0 such that

0 1 ω 0 ( θ a ) d θ < 1 . ()

Then, ϕ is the only solution of equation H ( ϕ ) = 0 in Ω = B ( ϕ , a ) D .

Proof.Set S = 0 1 H ( ϕ + θ ( ϕ ϕ ) ) d θ , where ϕ Ω with H ( ϕ ) = 0 . Then, by (4) and (31), we get in turn that

H ( ϕ ) 1 ( H ( ϕ ) S ) 0 1 ω 0 ( θ ϕ ϕ ) d θ 0 1 ω 0 ( θ a ) d θ < 1 ,
so the inverse of S exists. Hence, ϕ = ϕ follows from
0 = H ( ϕ ) H ( ϕ ) = S ( ϕ ϕ ) .

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 E 1 = E 2 = 3 , D = B ( 0 , 1 ) , and ϕ = ( 0 , 0 , 0 ) T . Consider the operator H from D to E 2 by

H ( x , y , z ) = e x 1 , e 1 2 y + 1 y , z T . ()

We get for u = ( x , y , z ) T that

H ( u ) = diag e x , ( e 1 ) y + 1 , 1 , ()
H ( u ) = e x 0 0 0 0 0 0 0 0 0 0 0 0 e 1 0 0 0 0 0 0 0 0 0 0 0 0 0 , ()
and
H ( u ) = e x 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 . ()

Using (3), (4), and (32)–(35), since H ( ϕ ) = diag { 1 , 1 , 1 } and D 0 = B ( ϕ , 1 / L 0 ) , we can define functions

ω ( t ) = e , ω ( t ) = e 1 / L 0 , ()
ω 0 ( t ) = L 0 t , ()
ω 1 ( t ) = L t , ω 1 ( t ) = e 1 / L 0 t , ()
where L 0 = e 1 and L = e .

We can see that

ω ( t ) < ω ( t ) and ω 1 ( t ) < ω 1 ( t ) for all t 0 , 1 L 0 .
Using 8, Remark 1, and the corresponding equations in Reference 14, we can obtain the results appearing in Table 1 having r 0 < R 0 < R 0 with δ = 0 . 4 and m = 3 , 4 , 5 , 6 . Then, comparing the radii of convergence appearing in References 6, 10, and 14, the new ones are larger.

Besides, we have the relation (18) (see Table 2).

In Table 3, the a priori error bounds are obtained, for m = 3 and ϕ 0 = ( 0 . 7 , 0 . 7 , 0 . 7 ) T as a starting point, to r 0 and r 0 , 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 = 4 , 5 , 6 , .

As we can see in Table 4, inequality (29) is verified.

TABLE 1. Radii used for the domains of convergence
m r 0 R 0 R 0
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…
TABLE 2. Radii used for the domains of convergence
m r 0 r 0
3 0.20087401… 0.20985708…
4 0.21647918… 0.21727336…
5 0.21783205… 0.21787644…
6 0.21790855… 0.21791049…
TABLE 3. A priori error bounds
n b n , δ b n , δ 0
0 1.18879312… 1.44160846…
1 0.09375885… 0.11369813…
2 0.00135374… 0.00164163…
3 0.00000051… 0.00000062…
TABLE 4. Radii of convergence
ρ 3 ρ 2 ρ 1
0.24525296… 0.32494723… 0.38269191…

Example 2.If we adopt the maximum norm with E 1 = E 2 = 𝒞 ( [ a , b ] ) and a = 0 , b = 1 let ϕ ( s ) = 0 . Set D = { x E 1 | x 1 } . Suppose that

H ( x ) ( s ) = x ( s ) 0 1 s t x 3 ( t ) d t , ()
is an operator from H to D. Then, we conclude that H ( ϕ ) ( s ) = 0 . Furthermore, if m = 3 we have: H ( x ) = I , α 2 = 0 , ω ( t ) = ω ( t ) = 2 , ω 0 ( t ) = 3 2 t , and ω 1 ( t ) = 2 t (see Reference 14). Hence, for all t [ 0 , 1 ] we have
ω 0 ( t ) < ω ( t ) , ω 0 ( t ) < ω 1 ( t ) ,
and
ω 1 ( t ) < ω 1 ( t ) .
If δ = 0 . 1 , from Equations (8) and (14), we can see in Table 5 that r 0 < R 0 < R 0 . Hence, the radii of convergence given in References 6, 10, and 14 are smaller than the new proposed. In Table 6, we present priori error bounds for r 0 and r 0 , if x 0 = 0 . 6 and m = 3 . Is important to note that, the new bounds are smaller than the ones in References 6, 10, and 14.

Finally, Equations (19) and (29) are satisfied as we can see in Tables 7 and 8.

TABLE 5. Radii used for the domains of convergence
m r 0 R 0 R 0
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…
TABLE 6. Radii used for the domains of convergence
m r 0 r 0
3 0.25819888… 0.31622776…
4 0.24814046… 0.25127852…
5 0.25748855… 0.25772381…
6 0.25816187… 0.25817420…
TABLE 7. A priori error bounds
n b n , δ b n , δ 0
0 0.26666666… 0.4
1 0.16332703… 0.24499054…
2 0.01772753… 0.02659130…
3 0.00001092… 0.00001638…
TABLE 8. Radius of convergence
ρ 3 ρ 2 ρ 1
0.25 0.33333333… 0.4

In Example 3, we show that results before Remark 3 may not be applicable, since f 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 m = 1 , 2 , 3 , . 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 E 1 = E 2 = , D = [ 1 2 , 3 2 ] . Define f on D by

f ( t ) = t 3 log t 2 + t 5 t 4 , t 0 , 0 , t = 0 ,
so
f ( t ) = 3 t 2 log t 2 + 5 t 4 4 t 3 + 2 t 2 , f ( t ) = 6 t log t 2 + 20 t 3 12 t 2 + 10 t , f ( t ) = 6 log t 2 + 60 t 2 24 t + 22 .
Therefore, f ( t ) is unbounded on D. So, the results before Remark 3 cannot apply. But using Remark 3, we have
ω 0 ( t ) = ω 1 ( t ) = ω 1 ( t ) = 96 . 66290073 t .
Then, Equation (29) is verified as we can see in Table 9.

TABLE 9. Radius of convergence
ρ 3 ρ 2 ρ 1
0.00689682… 0.00689682… 0.01029144…

Example 4.Let E 1 = E 2 = and D = B ( 0 , 1 ) . Define f on D by

f ( t ) = sin t .
Then, we have ϕ = 0 , ω ( t ) = ω ( t ) = 1 , ω 0 ( t ) = ω 1 ( t ) = ω 1 = t , for m = 1 , 2 , . Then, we get ρ 3 = 0 . 66666666 . But the Dennis and Schnabel result (see also Reference 6) gives β = T = 1 , so ε = 1 2 < ρ 3 . Hence, we obtain a larger ball for m = 1 .

Example 5.Consider the system

G ( x ) = x i 2 x i + 1 1 = 0 , 1 i m , x m 2 x 1 1 = 0 .
Choose m = 110 to obtain a 110 × 110 system.

If x 0 = ( 1 . 25 , 1 . 25 , , 1 . 25 ) T , we get ϕ = ( 1 , 1 , , 1 ) T . Moreover, choose D = B ( ϕ , 0 . 26 ) . Then, for ω 0 ( t ) = ω 1 ( t ) = 8 t , Equation (29) is verified as we can see in Table 10.

TABLE 10. Radius of convergence
ρ 3 ρ 2 ρ 1
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.

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