We use inexact Steffensen-Aitken-type methods to approximate implicit functions in a Banach space. Using a projection operator our equation reduces to solving a linear algebraic system of finite order. Semilocal convergence results as well as an error analysis are also provided.
Authors
Ioannis K. Argyros
(Cameron University)
Emil Cătinaş
(Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy)
Ion Păvăloiu
(Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy)
I. Argyros, E. Cătinaş, I. Păvăloiu On the approximate solutions of implicit functions using the Steffensen method, Proyecciones, 19 (2000) no. 3, pp. 291-303
[1] Argyros, I.K. On the convergence of some projection methods with perturbations, J. Comp. Appl. Math. 36, (1991), 255–258.
[2] Argyros, I.K., On an application of the Zincenko method to the approximation of implicit functions, Z.A.A. 10, 3, (1991), 391– 396.
[3] Argyros, I.K. and Szidarovszky, F., The Theory and Application of Iteration Methods, C.R.C. Press, Inc., Boca Raton, Florida, 1993.
[4] Catinas, E. On some iterative methods for solving nonlinear equations, Revue d’analyse Numerique et de theorie de l’approximation, 23, 1, (1994), 47–53.
[5] Kantorovich, L.V., The method of successive approximation for functional equations, Acta Math. 71 (1939), 63–97.
[6] Pavaloiu, I., Sur une generalisation de la methode de Steffensen, Revue d’analyse Numerique et de theorie de l’approximation, 21, 1, (1992), 59–65.
ON THE APPROXIMATE SOLUTION OF IMPLICIT FUNCTIONS USING THE STEFFENSEN METHOD
IOANNIS K. ARGYROSCameron University, U. S. A.EMIL CATINASandION PAVALOIUIntitut de Calcul, Romania
Abstract
We use inexact Steffensen-Aitken-type methods to approximate implicit functions in a Banach space. Using a projection operator our equation reduces to solving a linear algebraic system of finite order. Semilocal convergence results as well as an error analysis are also provided.
Key Words and Phrases: Steffensen-Aitken method, implicit function, projection operator.
1. Introduction
Let E,LambdaE, \Lambda be Banach spaces and denote by U(x_(0),R)U\left(x_{0}, R\right) the closed ball with center x_(0)in Ex_{0} \in E and of radius R >= 0R \geq 0. We will use the same symbol for the norm ||||\|\| in both spaces. Let PP be a projection operator ( P=P^(2)P=P^{2} ) which projects EE on its subspace E_(P)E_{P} and set Q=I-PQ=I-P. Suppose that the nonlinear operators F(x,lambda)F(x, \lambda) and G(x,lambda)G(x, \lambda) with values in EE are defined for x in Dx \in D, where DD is some open convex subset of EE containing U(x_(0),R)U\left(x_{0}, R\right), and lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) for some lambda_(0)in Lambda,S >= 0\lambda_{0} \in \Lambda, S \geq 0. For each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) the operator PF(w,lambda)P F(w, \lambda) will be assumed to be Fréchet-differentiable for all w in Dw \in D. Then PF^(')(x,lambda)P F^{\prime}(x, \lambda) will denote the Fréchet-derivative of the operator PF(w,lambda)P F(w, \lambda) with respect to the argument ww at w=xw=x. Moreover for each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) the operator PG(w,lambda)P G(w, \lambda) will be assumed to be continuous for all w in Dw \in D.
In this study we are concerned with the problem of approximating a solution x^(**):=x^(**)(lambda)x^{*}:=x^{*}(\lambda) of the equation
where by x_(0)x_{0} we mean x_(0)(lambda)x_{0}(\lambda). That is, x_(0)x_{0} depends on the lambda\lambda used in (2). A(x,lambda)in L(E xx Lambda,E)A(x, \lambda) \in L(E \times \Lambda, E) and is given by
where [x(lambda),y(lambda);F][x(\lambda), y(\lambda) ; F] (or [x(lambda),y(lambda);G][x(\lambda), y(\lambda) ; G] ) denotes divided difference of order one on FF (or GG ) at the points x(lambda),y(lambda)in Dx(\lambda), y(\lambda) \in D, satisfying
if F(x(lambda),lambda)F(x(\lambda), \lambda) is Fréchet-differentiable at x(lambda)x(\lambda) for all lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right). The operator z:D xx U(lambda_(0),S)rarr Ez: D \times U\left(\lambda_{0}, S\right) \rightarrow E is chosen so that iteration {x_(n)(lambda)}(n >=\left\{x_{n}(\lambda)\right\}(n \geq 0 ) generated by (2) converges to x^(**)x^{*}. The operator g^(1),g^(2),g^(3),g^(4):D xx U(lambda_(0),S)rarr Eg^{1}, g^{2}, g^{3}, g^{4}: D \times U\left(\lambda_{0}, S\right) \rightarrow E are continuous.
The importance of studying inexact Steffensen-Aitken methods comes from the fact that many commonly used variants can be considered procedures of this type. Indeed approximation (2) characterizes any iterative process in which corrections are taken as approximate solutions of Steffensen-Aitken equations. Moreover we note that if for example an equation on the real line is solved F(x_(n)(lambda),lambda)+G(x_(n)(lambda),lambda) >= 0F\left(x_{n}(\lambda), \lambda\right)+ G\left(x_{n}(\lambda), \lambda\right) \geq 0 and A(x_(n)(lambda),lambda)A\left(x_{n}(\lambda), \lambda\right) overestimates the derivative x_(n)-A(x_(n)(lambda),lambda)^(-1)(F(x_(n)(lambda),lambda)+G(x_(n)(lambda),lambda))x_{n}- A\left(x_{n}(\lambda), \lambda\right)^{-1}\left(F\left(x_{n}(\lambda), \lambda\right)+G\left(x_{n}(\lambda), \lambda\right)\right) is always "larger" than the corresponding Steffensen-Aitken iterate. In such cases a positive z(x_(n)(lambda),lambda)(n >= 0)z\left(x_{n}(\lambda), \lambda\right)(n \geq 0) correction term is appropriate.
It can easily be shown by induction on nn that under the above hypotheses F(x_(n)(lambda),lambda)+G(x_(n)(lambda),lambda)F\left(x_{n}(\lambda), \lambda\right)+G\left(x_{n}(\lambda), \lambda\right) belong to the domain of A(x_(n)(lambda),lambda)^(-1)A\left(x_{n}(\lambda), \lambda\right)^{-1} for all n >= 0n \geq 0.
Therefore, if the inverses exist (as it will be shown later in the theorem), then the iterates {x_(n)(lambda)}\left\{x_{n}(\lambda)\right\} can be computed for all n >= 0n \geq 0. The iterates generated when P=IP=I (identity operator on EE ) cannot easily be computed in infinite dimensional spaces since the inverses may be too difficult or impossible to find. It is easy to see, however, that the solution of equations (2) reduces to solving certain operator equations in the space EPE P. If, moreover, EPE P is a finite dimensional space of dimension NN, we obtain a system of linear algebraic equations of at most order NN. Special choices of the operators introduced above reduce our iteration (2) to earlier considered methods. Indeed we can have: for g^(1)(x(lambda),lambda)=g^(2)(x(lambda),lambda)=x(lambda),g^(3)(x(lambda),lambda)=g^(4)(x(lambda),lambda)=g^{1}(x(\lambda), \lambda)=g^{2}(x(\lambda), \lambda)=x(\lambda), g^{3}(x(\lambda), \lambda)=g^{4}(x(\lambda), \lambda)= 0, z=0z=0 we obtain Newton methods considered in [3], [4], [5]; for P=IP=I, no lambda,g^(1)(x)=g^(2)(x)=x(x in D),g^(3)(x_(n))=x_(n-1)(n >= 1)\lambda, g^{1}(x)=g^{2}(x)=x(x \in D), g^{3}\left(x_{n}\right)=x_{n-1}(n \geq 1), g^(4)(x_(n))=x_(n)(n >= 0),z_(n)=0(n >= 0)g^{4}\left(x_{n}\right)=x_{n}(n \geq 0), z_{n}=0(n \geq 0) we obtain Catinas method [4]; for P=IP=I, no lambda,G(x)=0(x in D),z_(n)=0(n >= 0),g^(3)(x)=g^(4)(x)=0\lambda, G(x)=0(x \in D), z_{n}=0(n \geq 0), g^{3}(x)=g^{4}(x)=0, g^(2)(x)=g^(1)(F(x))(x in D)g^{2}(x)=g^{1}(F(x))(x \in D), we obtain methods considered by Pǎvǎloiu in [3], [4], [6], [7]. Our choices of the operators since they include all previous methods allow us to consider a wider class of problems.
We provide sufficient conditions for the convergence of iteration (2) to a locally unique solution x^(**)(lambda)x^{*}(\lambda) of equation (1) as well as several
error bounds on the distances ||x_(n+1)(lambda)-x_(n)(lambda)||\left\|x_{n+1}(\lambda)-x_{n}(\lambda)\right\| and ||x_(n)(lambda)-x^(**)(lambda)||\left\|x_{n}(\lambda)-x^{*}(\lambda)\right\| ( n >= 0n \geq 0 ).
II. Convergence Analysis
We can now state and prove the following semilocal convergence result:
Theorem. Let F,G,P,QF, G, P, Q be as in the introduction. Assume:
(a) there exist x_(0)(lambda)in D,lambda_(0)in Lambdax_{0}(\lambda) \in D, \lambda_{0} \in \Lambda such that C:=C(lambda)=A(x_(0)(lambda),lambda_(0))C:=C(\lambda)=A\left(x_{0}(\lambda), \lambda_{0}\right) is invertible. Set B=C^(-1)B=C^{-1};
(b) there exist nonnegative numbers a_(i),R,S,i=1,2,dots,15a_{i}, R, S, i=1,2, \ldots, 15 such that:
(6) quad||BP([x,y;F]-[v,w;F])|| <= a_(1)(||x-v||+||y-w||)\quad\|B P([x, y ; F]-[v, w ; F])\| \leq a_{1}(\|x-v\|+\|y-w\|),
(7) ||x-g^(1)(x,lambda)|| <= a_(2)||A(x,lambda)^(-1)(F(x,lambda)+G(x,lambda))-z(x,lambda)||\left\|x-g^{1}(x, \lambda)\right\| \leq a_{2}\left\|A(x, \lambda)^{-1}(F(x, \lambda)+G(x, \lambda))-z(x, \lambda)\right\|
(8) ||x-g^(2)(x,lambda)|| <= a_(3)||A(x,lambda)^(-1)(F(x,lambda)+G(x,lambda))-z(x,lambda)||\left\|x-g^{2}(x, \lambda)\right\| \leq a_{3}\left\|A(x, \lambda)^{-1}(F(x, \lambda)+G(x, \lambda))-z(x, \lambda)\right\|, ||B(A(x_(n+1),lambda)(z(x_(n+1),lambda))-A(x_(n),lambda)(z(x_(n),lambda))|| <= a_(7)||x_(n+1)-x_(n)||:}\| B\left(A\left(x_{n+1}, \lambda\right)\left(z\left(x_{n+1}, \lambda\right)\right)-A\left(x_{n}, \lambda\right)\left(z\left(x_{n}, \lambda\right)\right)\left\|\leq a_{7}\right\| x_{n+1}-x_{n} \|\right.
for all v,w,x,y in U(x_(0),R),lambda in U(lambda,S);v, w, x, y \in U\left(x_{0}, R\right), \lambda \in U(\lambda, S) ;
(c) the sequence {z(x_(n)(lambda),lambda)}(n >= 0)\left\{z\left(x_{n}(\lambda), \lambda\right)\right\}(n \geq 0) is null for all lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right);
(d) for each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) there exists a minimum nonnegative number r^(**):=r_(lambda)^(**)r^{*}:=r_{\lambda}^{*} satisfying
{:(21)T_(lambda)(r^(**)) <= r^(**)quad" and "quadr^(**) <= R:}\begin{equation*}
T_{\lambda}\left(r^{*}\right) \leq r^{*} \quad \text { and } \quad r^{*} \leq R \tag{21}
\end{equation*}
with r:=r(lambda)r:=r(\lambda),
{:(22)T_(lambda)(r)=n+(b_(1)r+b_(2))/(b(r)-b_(3)r)r:}\begin{equation*}
T_{\lambda}(r)=n+\frac{b_{1} r+b_{2}}{b(r)-b_{3} r} r \tag{22}
\end{equation*}
Then
(i) For each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) the scalar sequence {t_(n)(lambda)}(n >= 0)\left\{t_{n}(\lambda)\right\}(n \geq 0) generated by
is monotonically increasing, bounded above by r^(**)r^{*} and lim_(n rarr oo)t_(n)(lambda)=r^(**)\lim _{n \rightarrow \infty} t_{n}(\lambda)= r^{*};
(ii) the inexact Steffensen-Aitken method generated by (2) is well defined, remains in U(x_(0)(lambda),r^(**))U\left(x_{0}(\lambda), r^{*}\right) for all n >= 0n \geq 0, and converges to a solution x^(**)(lambda)in U(x_(0)(lambda),r^(**))x^{*}(\lambda) \in U\left(x_{0}(\lambda), r^{*}\right) of equation (1). Moreover if z=0z=0 then x^(**)(lambda)x^{*}(\lambda) is unique in U(x_(0)(lambda),R)U\left(x_{0}(\lambda), R\right). Furthermore the following error bounds are true:
Proof. (i) By (21) and (30) we deduce 0 <= t_(0)(lambda) <= t_(1)(lambda) <= r^(**)0 \leq t_{0}(\lambda) \leq t_{1}(\lambda) \leq r^{*}. Let us assume 0 <= t_(k-1)(lambda) <= t_(k)(lambda) <= r^(**)0 \leq t_{k-1}(\lambda) \leq t_{k}(\lambda) \leq r^{*} for k=1,2,dots,nk=1,2, \ldots, n. Then it follows from (30) and (31) that 0 <= t_(k)(lambda) <= t_(k+1)(lambda)0 \leq t_{k}(\lambda) \leq t_{k+1}(\lambda). Hence, the sequence {t_(n)(lambda)}(n >= 0)\left\{t_{n}(\lambda)\right\}(n \geq 0) is monotonically increasing. Moreover by (31) and the induction hypotheses we get in turn
That is the sequence {t_(n)(lambda)}(n >= 0)\left\{t_{n}(\lambda)\right\}(n \geq 0) is also bounded above by r^(**)r^{*}. Since for each fixed lambda in U(lambda_(0),S)r^(**)\lambda \in U\left(\lambda_{0}, S\right) r^{*} is the minimum nonnegative number satisfying (21) it follows that lim_(n rarr oo)t_(n)(lambda)=r^(**)\lim _{n \rightarrow \infty} t_{n}(\lambda)=r^{*}.
(ii) By hypotheses (30), (23) and (22) it follows that x_(1)(lambda)in U(x_(0)(lambda),r^(**))x_{1}(\lambda) \in U\left(x_{0}(\lambda), r^{*}\right). Moreover from (26) we deduce g^(1)(x_(0)(lambda),lambda),g^(2)(x_(0)(lambda),lambda)g^{1}\left(x_{0}(\lambda), \lambda\right), g^{2}\left(x_{0}(\lambda), \lambda\right), g^(3)(x_(0)(lambda),lambda),g^(4)(x_(0)(lambda),lambda)in U(x_(0)(lambda),r^(**))g^{3}\left(x_{0}(\lambda), \lambda\right), g^{4}\left(x_{0}(\lambda), \lambda\right) \in U\left(x_{0}(\lambda), r^{*}\right). Let us assume x_(k+1)(lambda)x_{k+1}(\lambda), g^(1)(x_(k)(lambda),lambda),g^(2)(x_(k)(lambda),lambda),g^(3)(x_(k)(lambda),lambda),g^(4)(x_(k)(lambda),lambda)in U(x_(0)(lambda),r^(**))g^{1}\left(x_{k}(\lambda), \lambda\right), g^{2}\left(x_{k}(\lambda), \lambda\right), g^{3}\left(x_{k}(\lambda), \lambda\right), g^{4}\left(x_{k}(\lambda), \lambda\right) \in U\left(x_{0}(\lambda), r^{*}\right) for k=0,1,2,dots,nk=0,1,2, \ldots, n, and that (36) is true for k=1,2,dots,nk=1,2, \ldots, n (since it is true for k=0k=0 by (23) and (30)). Then from (9) and (26) we get
Finally from (31), (41), (42), (46)-(50) we deduce that estimates (35) and (36) are true. By (36) and part (i) it follows that for each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right) iteration {x_(n)(lambda)}\left\{x_{n}(\lambda)\right\} ( n >= 0n \geq 0 ) is Cauchy in a Banach space EE and as such it converges to some x^(**)(lambda)in U(x_(0)(lambda),r^(**))x^{*}(\lambda) \in U\left(x_{0}(\lambda), r^{*}\right) (since U(x_(0)(lambda),r^(**))U\left(x_{0}(\lambda), r^{*}\right) is a closed set. Using hypothesis (c) and letting n rarr oon \rightarrow \infty in (2) we get F(x^(**)(lambda),lambda)+G(x^(**)(lambda),lambda)=0F\left(x^{*}(\lambda), \lambda\right)+G\left(x^{*}(\lambda), \lambda\right)=0. That is x^(**)(lambda)x^{*}(\lambda) is a solution of equation (1). Estimate (37) follows immediately from (36) by using standard majorization techniques [3], [5].
To show uniqueness when z=0z=0, let us assume y^(**)(lambda)in U(x_(0)(lambda),R)y^{*}(\lambda) \in U\left(x_{0}(\lambda), R\right) is a solution of equation (1). Then from (2) we get x_(n+1)(lambda)-y^(**)(lambda)=x_(n)(lambda)-y^(**)(lambda)-A(x_(n)(lambda),lambda)^(-1)[(F(x_(n)(lambda),lambda):}x_{n+1}(\lambda)-y^{*}(\lambda)=x_{n}(\lambda)-y^{*}(\lambda)-A\left(x_{n}(\lambda), \lambda\right)^{-1}\left[\left(F\left(x_{n}(\lambda), \lambda\right)\right.\right.
Analyzing the right-hand side of (51) as in (42) with y^(**)(lambda)y^{*}(\lambda) "replacing" x_(k)(lambda)x_{k}(\lambda) and x_(n)(lambda)x_{n}(\lambda) "replacing" x_(k-1)(lambda)x_{k-1}(\lambda) we get
By letting n rarr oon \rightarrow \infty in (52) and using (27) we get lim_(n rarr oo)x_(n+1)(lambda)=y^(**)(lambda)\lim _{n \rightarrow \infty} x_{n+1}(\lambda)= y^{*}(\lambda) for each fixed lambda in U(lambda_(0),S)\lambda \in U\left(\lambda_{0}, S\right). By the uniqueness of the limit of the sequence {x_(n)(lambda)}(n >= 0)\left\{x_{n}(\lambda)\right\}(n \geq 0) we deduce x^(**)(lambda)=y^(**)(lambda)x^{*}(\lambda)=y^{*}(\lambda).
That completes the proof of the Theorem.
Remarks. (1) Condition (6) implies that F(x(lambda),lambda)F(x(\lambda), \lambda) is differentiable on DD [2], [3], whereas condition (13) does not necessarily imply the differentiability of G(x(lambda),lambda)G(x(\lambda), \lambda) on DD.
(2) Inequalities (21), (23), (25), (26) and (27) will determine r^(**),Rr^{*}, R and SS.
(3) If a_(2)+a_(4) <= 1,a_(3)+a_(5) <= 1,a_(9)+a_(11) <= 1a_{2}+a_{4} \leq 1, a_{3}+a_{5} \leq 1, a_{9}+a_{11} \leq 1 and a_(10)+a_(12) <= 1a_{10}+a_{12} \leq 1 for r^(**)!=0r^{*} \neq 0, condition (26) is satisfied. Indeed from (7) we have
It suffices to show a_(2)r^(**) <= r^(**)(1-a_(4))a_{2} r^{*} \leq r^{*}\left(1-a_{4}\right) or a_(2)+a_(4) <= 1(r^(**)!=0)a_{2}+a_{4} \leq 1\left(r^{*} \neq 0\right) which is true by hypothesis. Similarly we can argue for the rest.
References
[1] Argyros, I.K. On the convergence of some projection methods with perturbations, J. Comp. Appl. Math. 36, (1991), 255-258.
[2] Argyros, I.K. On an application of the Zincenko method to the approximation of implicit functions, Z.A.A. 10, 3, (1991), 391396.
[3] Argyros, I.K. and Szidarovszky, F. The Theory and Application of Iteration Methods, C.R.C. Press, Inc., Boca Raton, Florida, 1993.
[4] Cătinaş, E. On some iterative methods for solving nonlinear equations, Revue d'analyse Numerique et de theorie de l'approximation, 23, 1, (1994), 47-53.
[5] Kantorovich, L.V. The method of successive approximation for functional equations, Acta Math. 71 (1939), 63-97.
[6] Pǎvǎloiu, I. Sur une generalisation de la methode de Steffensen, Revue d'analyse Numerique et de theorie de l'approximation, 21, 1, (1992), 59-65.
[7] Pǎvăloiu, I. Bilateral approximations for the solutions of scalar equations, Revue d'analyse numerique et de theorie de l'approximation, 23, 1, (1994), 95-100.
Received : July 1999.
Ioannis K. Argyros
Cameron University
Department of Mathematics
Lawton, OK 73505,
U.S.A.
e-mail : ioannisa@cameron.edu