An iterative method for a functional-differential equation of second order with mixed type argument

Abstract

In this paper we shall study a functional differential equation of second order with mixed type argument. For this problem we give an algorithm based on the step method and the successive approximation method.

Authors

D. Otrocol
(Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy)

V.A. Ilea
(Babes Bolyai Univ.)

V. Revnic
(Univ Med & Pharm Iuliu Hatieganu)

Keywords

In this paper we shall study a functional differential equation of second order with mixed type argument. For this problem we give an algorithm based on the step method and the successive approximation method

Cite this paper as:

D. Otrocol, V. Ilea, C. Revnic, An iterative method for a functional-differential equation of second order with mixed type argument, Fixed Point Theory, 14(2013), no. 2, pp. 427-434

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About this paper

Journal

Fixed Point Theory

Publisher Name

Casa Cartii de Stiinta, Cluj-Napoca, Romania

DOI
Print ISSN

1583-5022

Online ISSN

2066-9208

MR

MR3137184

ZBL

Google Scholar

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[11] D. Otrocol, V.A. Ilea, C. Revnic, An iterative method for a functional-differential equations with mixed type argument, Fixed Point Theory, 11(2010), no. 2, 327-336.

[12] R. Precup, Some existence results for differential equations with both retarded and advanced arguments, Mathematica (Cluj), 44(2002), no. 1, 25-31.

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[15] I.A. Rus, Abstract models of step method which imply the convergence of successive approximations, Fixed Point Theory, 9(2008), no. 1, 293-307.

[16] I.A. Rus, M.A. Serban, D. Trif, Step method for some integral equations from biomathematics, Bull. Math. Soc. Sci. Math. Roumanie, 54(102)(2011), no. 2, 167-183.

[17] I.A. Rus, C. Iancu, Wheeler-Feynman problem for mixed order functional differential equations, Tiberiu Popoviciu Itinerant Seminar of Functional Equations, Approximation and Convexity, Cluj-Napoca, May 23-29, 2000, 197-200.

[18] L.S. Schulman, Some differential difference equations containing both advance and retardation, J. Math. Phys., 15(1974), 195-198.

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Fixed Point Theory, Volume …, 200.., …

http://www.math.ubbcluj.ro/nodeacj/sfptcj.html

An iterative method for a functional-differential equation with mixed type argument

Diana Otrocola, Veronica Ileab and Cornelia Revnicc

a Tiberiu Popoviciu Institute of Numerical Analysis
Romanian Academy
Cluj-Napoca, Romania
E-mail: dotrocol@ictp.acad.ro

b Department of Applied Mathematics
Babeş-Bolyai University
Cluj-Napoca, Romania
E-mail: vdarzu@math.ubbcluj.ro

c Department of Mathematics and Computer Science
University of Medicine and Pharmacy “Iuliu Haţieganu”
Cluj-Napoca, Romania
E-mail: cornelia.revnic@umfcluj.ro

Abstract. In this paper we shall study a functional differential equations with mixed type argument. For this problem we give an algorithm based on the step method and the successive approximation method.

Keywords: Functional-differential equations, mixed type argument, step method, successive approximation method, Newton’s method.

AMS Subject Classification: 47H10, 34H10.

1. Introduction

In this paper we study the following problem

(1) x(t)=f(t,x(t),x(th),x(t+h)),t[T,T],x^{\prime}(t)=f(t,x(t),x(t-h),x(t+h)),\ t\in[-T,T],
(2) x(t)=φ(t),t[h,h].x(t)=\varphi(t),\ t\in[-h,h].

The problem (1)-(2) has been studied in the papers R. Driver [4], I.A. Rus and C. Iancu [9] and V.A. Darzu [2]. This problem is known in literature as Wheeler-Feynman problem. For this problem the above authors studied the existence and uniqueness of the solution using the step method.

The purpose of this paper is to elaborate an algorithm based on the step method and the successive approximation method and to apply it on the problem (1)-(2).

Algorithm.

At each step we have a problem like this

(3) {x(t)=f(t,x(t),x(th),x(t+h)),t[a+h,a+2h]x(t)=θ(t),t[a,a+2h],\left\{\begin{array}[c]{l}x^{\prime}(t)=f(t,x(t),x(t-h),x(t+h)),\ t\in[a+h,a+2h]\\ x(t)=\theta(t),\ t\in[a,a+2h],\end{array}\right.

where fC([a+h,a+2h]×3,),θC([a,a+2h],)f\in C([a+h,a+2h]\times\mathbb{R}^{3},\mathbb{R}),\theta\in C([a,a+2h],\mathbb{R}) and x:[a,a+3h].x:[a,a+3h]\rightarrow\mathbb{R}.

Follows that we have

θ(t)=f(t,θ(t),θ(th),x(t+h)),t[a+h,a+2h].\theta^{\prime}(t)=f(t,\theta(t),\theta(t-h),x(t+h)),\ t\in[a+h,a+2h].

We denote ξ:=t+h,ξ[a+2h,a+3h].\xi:=t+h,\ \xi\in[a+2h,a+3h].

Then

θ(ξh)=f(ξh,θ(ξh),θ(ξ2h),x(ξ)),ξ[a+2h,a+3h].\theta^{\prime}(\xi-h)=f(\xi-h,\theta(\xi-h),\theta(\xi-2h),x(\xi)),\ \xi\in[a+2h,a+3h].

We denote F(ξ,x(ξ)):=f(ξh,θ(ξh),θ(ξ2h),x(ξ))θ(ξh)F(\xi,x(\xi)):=f(\xi-h,\theta(\xi-h),\theta(\xi-2h),x(\xi))-\theta^{\prime}(\xi-h). So

(4) F(ξ,x(ξ))=0.F(\xi,x(\xi))=0.

The purpose is to impose conditions on ff such that equation (4) has a unique solution who can be approximated by Newton’s method.

In order to study the problem (1)-(2) we need the following well known results.

Implicit function theorem.

([1]) We suppose that F:[a,b]×F:[a,b]\times\mathbb{R\rightarrow R} satisfy the following conditions

  • (i)

    FC1([a,b]×);F\in C^{1}([a,b]\times\mathbb{R});

  • (ii)

    there exists F(t,u)u\frac{\partial F(t,u)}{\partial u}\in\mathbb{R}^{\ast} and |F(t,u)u|M1,t[a,b],u\left|\frac{\partial F(t,u)}{\partial u}\right|\leq M_{1},\ \forall t\in[a,b],u\in\mathbb{R};

  • (iii)

    for each t0[a,b]t_{0}\in[a,b] there exists u0u_{0}\in\mathbb{R} such that F(t0,u0)=0.F(t_{0},u_{0})=0.

Then, there exists a unique solution xC1[a,b]x\in C^{1}[a,b] such that F(t,x(t))=0,(t,x(t))[a,b]×,F(t,x(t))=0,\linebreak\forall(t,x(t))\in[a,b]\times\mathbb{R}, solution that can be obtained using the successive approximation method.

In terms of ff, for the problem (1)-(2), the conditions from the above theorem are:

  • (C1)

    fC([T,T]×3,),φC([h,h],);f\in C^{\infty}([-T,T]\times\mathbb{R}^{3},\mathbb{R}),\varphi\in C([-h,h],\mathbb{R});

  • (C2)

    f(t,u,v,w)w,\frac{\partial f(t,u,v,w)}{\partial w}\in\mathbb{R}^{\ast}, t[T,T]\forall t\in[-T,T], u,v,w;\forall u,v,w\in\mathbb{R};

  • (C3)

    |f(t,u,v,w)w|M1,t[T,T],u,v,w\left|\frac{\partial f(t,u,v,w)}{\partial w}\right|\leq M_{1},\forall t\in[-T,T],\ \forall u,v,w\in\mathbb{R};

  • (C4)

    t[T,T],u,v,η,\forall t\in[-T,T],u,v,\eta\in\mathbb{R}, the equation f(t,u,v,w)η=0f(t,u,v,w)-\eta=0 has a unique solution.

We shall use the notations, the terminology and some results given by I.A. Rus in the paper [6], and the following result, that is a generalization of the fibre contraction theorem (see [6]).

Fibre contraction theorem.

(Theorem 9.1., [7]) Let (Xi,di),i=0,m¯,m1,(X_{i},d_{i}),\ i=\overline{0,m},\ m\geq 1, be some metric spaces. Let

Ai:X0××XiXi,i=0,m¯A_{i}:X_{0}\times\cdots\times X_{i}\rightarrow X_{i},\ i=\overline{0,m}

be some operators. We suppose that:

  • (i)

    (Xi,di),i=1,m¯,(X_{i},d_{i}),~i=\overline{1,m}, are complete metric spaces;

  • (ii)

    the operator A0A_{0} is WPO;

  • (iii)

    there exists αi(0;1)\alpha_{i}\in(0;1) such that:

    Ai(x0,,xi1,):XiXi,i=1,m¯A_{i}(x_{0},\ldots,x_{i-1},\cdot):X_{i}\rightarrow X_{i},\ i=\overline{1,m}

    are αi\alpha_{i}-contractions;

  • (iv)

    the operators Ai,i=1,m¯A_{i},~i=\overline{1,m}, are continuous.

The operator A:X0××XmX0××Xm,A:X_{0}\times\cdots\times X_{m}\rightarrow X_{0}\times\cdots\times X_{m},

A(x0,,xm)=(A0(x0),A1(x0,x1),,Am(x0,,xm))A(x_{0},\ldots,x_{m})=(A_{0}(x_{0}),A_{1}(x_{0},x_{1}),\ldots,A_{m}(x_{0},\ldots,x_{m}))

is WPO. If A0A_{0} is PO, then AA is PO.

2. Main result

In this section we apply the algorithm from section 1 for the problem (1)-(2).

Let nn\in\mathbb{N}^{\ast} be such that nhT,(n+1)h>Tnh\leq T,\ (n+1)h>T. In the conditions (C1)(C3)(C_{1})-(C_{3}), the step method consists in the following:

For t[h,2h]t\in[h,2h] we have

x(t)\displaystyle x^{\prime}(t) =f(t,x(t),x(th),x(t+h)),\displaystyle=f(t,x(t),x(t-h),x(t+h)),
x(th)\displaystyle x^{\prime}(t-h) =f(th,x(th),x(t2h),x(t)),\displaystyle=f(t-h,x(t-h),x(t-2h),x(t)),
x0(th)\displaystyle x_{0}^{\prime}(t-h) =f(th,x0(th),x1(t2h),x(t)),\displaystyle=f(t-h,x_{0}(t-h),x_{-1}(t-2h),x(t)),

where x(t)=φ(t)={x1(t),t[h,0],x0(t),t[0,h].x(t)=\varphi(t)=\left\{\begin{array}[c]{l}x_{-1}(t),t\in[-h,0],\\ x_{0}(t),t\in[0,h].\end{array}\right.

We denote x(t):=x1(t),t[h,2h].x(t):=x_{1}(t),\ t\in[h,2h].

Let

F(t,x1(t))\displaystyle F(t,x_{1}(t)) :=f(th,x0(th),x1(t2h),x1(t))x0(th)=0,t[h,2h].\displaystyle:=f(t-h,x_{0}(t-h),x_{-1}(t-2h),x_{1}(t))-x_{0}^{\prime}(t-h)=0,\ t\in[h,2h].
F(t,x1(t))\displaystyle F(t,x_{1}(t)) =0,t[h,2h].\displaystyle=0,\ t\in[h,2h].

From the implicit function theorem there exists a solution x1C1[h,2h]x_{1}^{\ast}\in C^{1}[h,2h] such that

F(t,x1(t))=0,t[h,2h].F(t,x_{1}^{\ast}(t))=0,\forall t\in[h,2h].

The key of each step is to approximate the solution x1x_{1}^{\ast}\in [h,2h][h,2h] with the method of Newton:

x1m(t)=x1,m1(t)G(t,x1(t))F(t,x1,m1(t)),x_{1m}(t)=x_{1,m-1}(t)-G(t,x_{1}^{\ast}(t))F(t,x_{1,m-1}(t)),

where G(t,x1(t))0G(t,x_{1}^{\ast}(t))\neq 0 and x1,m1(t)G(t,x1(t))F(t,x1,m1(t))x_{1,m-1}(t)-G(t,x_{1}^{\ast}(t))F(t,x_{1,m-1}(t)) is a contraction.

We choose the function G:[h,2h]×G:[h,2h]\times\mathbb{R}\rightarrow\mathbb{R} with G(t,x1(t)):=M(F(h,x1(h))x1)1G(t,x_{1}^{\ast}(t)):=\!M\left(\frac{\partial F(h,x_{1}(h))}{\partial x_{1}}\right)\!^{-1}, where M(0,1)M\in(0,1) is a constant. It is obvious that G(t,x1(t))0.G(t,x_{1}^{\ast}(t))\neq 0.

Now we consider the operator A1:C[h,2h]C[h,2h]A_{1}:C[h,2h]\rightarrow C[h,2h], defined by

A1(x1,m1)(t):=x1,m1(t)G(t,x1(t))F(t,x1,m1(t)).A_{1}(x_{1,m-1})(t):=x_{1,m-1}(t)-G(t,x_{1}^{\ast}(t))F(t,x_{1,m-1}(t)).

Proving that A1A_{1} is a contraction we have the uniqueness of the solution x1mx_{1m} on [h,2h][h,2h]. For all t[h,2h]t\in[h,2h] we have the inequality

A1(x1,m1)(t)A1(y1,m1)(t)(1M)x1,m1y1,m1.\displaystyle\left\|A_{1}(x_{1,m-1})(t)-A_{1}(y_{1,m-1})(t)\right\|\leq(1-M)\left\|x_{1,m-1}-y_{1,m-1}\right\|.

We have that x1munifx1x_{1m}\overset{unif}{\rightarrow}x_{1}^{\ast} on [h,2h][h,2h], so in the next step we shall use x1mx_{1m} instead of x1.x_{1}^{\ast}.

For t[2h,3h]t\in[2h,3h] we have

x(t)\displaystyle x^{\prime}(t) =f(t,x(t),x(th),x(t+h)),\displaystyle=f(t,x(t),x(t-h),x(t+h)),
x(th)\displaystyle x^{\prime}(t-h) =f(th,x(th),x(t2h),x(t)),\displaystyle=f(t-h,x(t-h),x(t-2h),x(t)),
x1m(th)\displaystyle x_{1m}^{\prime}(t-h) =f(th,x1m(th),x0(t2h),x(t)).\displaystyle=f(t-h,x_{1m}(t-h),x_{0}(t-2h),x(t)).

We denote x(t):=x2(t),t[2h,3h]x(t):=x_{2}(t),\ t\in[2h,3h].

Let

F(t,x2(t))\displaystyle F(t,x_{2}(t)) :=f(th,x1m(th),x0(t2h),x2(t))x1m(th)=0,t[2h,3h].\displaystyle:=f(t-h,x_{1m}(t-h),x_{0}(t-2h),x_{2}(t))-x_{1m}^{\prime}(t-h)=0,\ t\in[2h,3h].
F(t,x2(t))\displaystyle F(t,x_{2}(t)) =0,t[2h,3h]\displaystyle=0,\ t\in[2h,3h]

Applying implicit function theorem we have that there exists the solution x2C1[2h,3h]x_{2}^{\ast}\in C^{1}[2h,3h] such that

F(t,x2(t))=0,t[2h,3h].F(t,x_{2}^{\ast}(t))=0,\forall t\in[2h,3h].

Now we approximate the solution x2x_{2}^{\ast}\in [2h,3h][2h,3h] with the method of Newton:

x2m(t)=x2,m1(t)G(t,x2(t))F(t,x2,m1(t)),x_{2m}(t)=x_{2,m-1}(t)-G(t,x_{2}^{\ast}(t))F(t,x_{2,m-1}(t)),

where G(t,x2(t))0G(t,x_{2}^{\ast}(t))\neq 0 and x2,m1(t)G(t,x2(t))F(t,x2,m1(t))x_{2,m-1}(t)-G(t,x_{2}^{\ast}(t))F(t,x_{2,m-1}(t)) is a contraction.

We choose G:[2h,3h]×G:[2h,3h]\times\mathbb{R}\rightarrow\mathbb{R} with G(t,x2(t)):=M(F(2h,x2(2h))x2)1G(t,x_{2}^{\ast}(t)):=M\left(\frac{\partial F(2h,x_{2}(2h))}{\partial x_{2}}\right)^{-1}, where M(0,1)M\in(0,1) is a constant. Then we have G(t,x2(t))0.G(t,x_{2}^{\ast}(t))\neq 0.

Let us consider the operator A2:C[2h,3h]C[2h,3h]A_{2}:C[2h,3h]\rightarrow C[2h,3h], defined by

A2(x2,m1)(t):=x2,m1(t)G(t,x2(t))F(t,x2,m1(t)).A_{2}(x_{2,m-1})(t):=x_{2,m-1}(t)-G(t,x_{2}^{\ast}(t))F(t,x_{2,m-1}(t)).

In the same way as in the previous step we prove that A2A_{2} is a contraction. Follows that x2munifx2x_{2m}\overset{unif}{\rightarrow}x_{2}^{\ast} on [2h,3h][2h,3h], so in the next step we shall use x2mx_{2m} instead of x2.x_{2}^{\ast}.

By induction, for t[nh,T]t\in[nh,T] we have

x(t)\displaystyle x^{\prime}(t) =f(t,x(t),x(th),x(t+h)),\displaystyle=f(t,x(t),x(t-h),x(t+h)),
x(th)\displaystyle x^{\prime}(t-h) =f(th,x(th),x(t2h),x(t)),\displaystyle=f(t-h,x(t-h),x(t-2h),x(t)),
xn1,m(th)\displaystyle x_{n-1,m}^{\prime}(t-h) =f(th,xn1,m(th),xn2,m(t2h),x(t)).\displaystyle=f(t-h,x_{n-1,m}(t-h),x_{n-2,m}(t-2h),x(t)).

We denote x(t):=xn(t),t[nh,T]x(t):=x_{n}(t),\ t\in[nh,T].

Let

F(t,xn(t))\displaystyle F(t,x_{n}\!(t)) :=f(th,xn1,m(th),xn2,m(t2h),x(t))xn1,m(th)=0,\displaystyle:=\!f(t-h,x_{n-1,m}(t-h),x_{n-2,m}\!(t-2h),x(t))-x_{n-1,m}^{\prime}\!(t-h)=0,
F(t,xn(t))\displaystyle F(t,x_{n}\!(t)) =0,t[nh,T].\displaystyle=0,\ t\in[nh,T].

Applying implicit function theorem, there exists the solution xnC1[nh,T]x_{n}^{\ast}\in C^{1}[nh,T] such that

F(t,xn(t))=0,t[nh,T].F(t,x_{n}^{\ast}(t))=0,\forall t\in[nh,T].

We approximate the solution xnx_{n}^{\ast}\in [nh,T][nh,T] with the method of Newton:

xnm(t)=xn,m1(t)G(t,xn(t))F(t,xn,m1(t)),x_{nm}(t)=x_{n,m-1}(t)-G(t,x_{n}^{\ast}(t))F(t,x_{n,m-1}(t)),

where G(t,xn(t))0G(t,x_{n}^{\ast}(t))\neq 0 and xn,m1(t)G(t,xn(t))F(t,xn,m1(t))x_{n,m-1}(t)-G(t,x_{n}^{\ast}(t))F(t,x_{n,m-1}(t)) is a contraction.

The function chosen here is G:[nh,T]×,G(t,xn(t)):=M(F(nh,xn(nh))xn)1G:[nh\!,T]\times\mathbb{R}\rightarrow\mathbb{R},\ G(t\!,x_{n}^{\ast}\!(t))\!:=\!\!M\left(\!\frac{\partial F(nh,\!x_{n}\!(nh))}{\partial x_{n}}\!\right)^{-1}, where M(0,1)M\in(0,1) is a constant. Then G(t,xn(t))0.G(t,x_{n}^{\ast}(t))\neq 0.

Let the operator An:C[nh,T]C[nh,T]A_{n}:C[nh,T]\rightarrow C[nh,T] defined by

An(xn,m1)(t):=xn,m1(t)G(t,xn(t))F(t,xn,m1(t)).A_{n}(x_{n,m-1})(t):=x_{n,m-1}(t)-G(t,x_{n}^{\ast}(t))F(t,x_{n,m-1}(t)).

It is trivial to prove that AnA_{n} is a contraction. Then we have that xnmunifxnx_{nm}\overset{unif}{\rightarrow}x_{n}^{\ast} on [nh,T].[nh,T].

So, the following convergence takes place

x~={x1,t[h,0]x0,t[0,h]x1m,t[h,2h]xnm,t[nh,T]x={x1,t[h,0]x0,t[0,h]x1,t[h,2h]xn,t[nh,T].\widetilde{x}=\begin{cases}x_{-1},&t\in[-h,0]\\ x_{0},&t\in[0,h]\\ x_{1m},&t\in[h,2h]\\ \vdots&\\ x_{nm},&t\in[nh,T]\end{cases}\rightarrow x^{\ast}=\begin{cases}x_{-1},&t\in[-h,0]\\ x_{0},&t\in[0,h]\\ x_{1}^{\ast},&t\in[h,2h]\\ \vdots&\\ x_{n}^{\ast},&t\in[nh,T].\end{cases}

In what follows we present the step method for the solution determined with the above algorithm.

(p0)x(t)=φ(t)={x1(t),t[h,0],x0(t),t[0,h];\displaystyle(p_{0})\ x(t)=\varphi(t)=\left\{\begin{array}[c]{l}x_{-1}(t),t\in[-h,0],\\ x_{0}(t),t\in[0,h];\end{array}\right.
(p1)x0(th)=x0(0)+htf(sh,x0(sh),x1(s2h),x1(s))𝑑s,t[h,2h];\displaystyle(p_{1})\ x_{0}^{\ast}(t-h)=x_{0}^{\ast}(0)+\int_{h}^{t}f(s-h,x_{0}^{\ast}(s-h),x_{-1}^{\ast}(s-2h),x_{1}(s))ds,t\in[h,2h];
(p2)x1(th)=x1(h)+2htf(sh,x1(sh),x0(s2h),x2(s))𝑑s,t[2h,3h];\displaystyle(p_{2})\ x_{1}^{\ast}(t-h)=x_{1}^{\ast}(h)+\int_{2h}^{t}f(s-h,x_{1}^{\ast}(s-h),x_{0}(s-2h),x_{2}(s))ds,t\in[2h,3h];
(p3)x2(th)=x2(2h)+3htf(sh,x2(sh),x1(s2h),x3(s))𝑑s,t[3h,4h];\displaystyle(p_{3})\ x_{2}^{\ast}(t-h)=x_{2}^{\ast}(2h)+\int_{3h}^{t}f(s-h,x_{2}^{\ast}(s-h),x_{1}^{\ast}(s-2h),x_{3}(s))ds,t\in[3h,4h];
\displaystyle\vdots
(pn)xn1(th)=xn1((n1)h)+nhtf(sh,xn1(sh),xn2(s2h),xn(s))𝑑s,\displaystyle(p_{n})\ x_{n-1}^{\ast}(t-h)=\!x_{n-1}^{\ast}\!((n-1)h)\!+\!\int_{nh}^{t}\!f(s-h,\!x_{n-1}^{\ast}\!(s-h),x_{n-2}^{\ast}\!(s-2h),x_{n}(s))\!ds,
t[nh,T].\displaystyle\quad\quad t\in[nh,T].

Thus we have the following theorem

Theorem 1.

In the conditions (C1)(C3)(C_{1})-(C_{3}) we have

  • a)

    the problem (1)-(2) has in C[T,T]C[-T,T] a unique solution

    x(t)={φ(t),t[h,h]x1(t),t[h,2h]xn(t),t[nh,T]x^{\ast}(t)=\begin{cases}\varphi(t),&t\in[-h,h]\\ x_{1}^{\ast}(t),&t\in[h,2h]\\ \vdots&\\ x_{n}^{\ast}(t),&t\in[nh,T]\end{cases}
  • b)

    the sequence define by

    x0(th)=x0(0)+htf(sh,x0(sh),x1(s2h),x1m(s))𝑑s,t[h,2h];\displaystyle\ x_{0}(t-h)=x_{0}(0)+\int_{h}^{t}f(s-h,x_{0}(s-h),x_{-1}(s-2h),x_{1m}(s))ds,t\in[h,2h];
    x1(th)=x1(h)+2htf(sh,x1(sh),x0(s2h),x2m(s))𝑑s,t[2h,3h];\displaystyle x_{1}^{\ast}(t-h)=x_{1}^{\ast}(h)+\int_{2h}^{t}f(s-h,x_{1}^{\ast}(s-h),x_{0}(s-2h),x_{2m}(s))ds,t\in[2h,3h];
    x2(th)=x2(2h)+3htf(sh,x2(sh),x1(s2h),x3m(s))𝑑s,t[3h,4h];\displaystyle\ x_{2}^{\ast}(t-h)=x_{2}^{\ast}(2h)+\int_{3h}^{t}f(s-h,x_{2}^{\ast}(s-h),x_{1}^{\ast}(s-2h),x_{3m}(s))ds,t\in[3h,4h];
    \displaystyle\vdots
    xn1(th)=xn1((n1)h)+nhtf(sh,xn1(sh),xn2(s2h),xnm(s))𝑑s,\displaystyle\ x_{n-1}^{\ast}(t-h)=x_{n-1}^{\ast}((n\!-\!1)h)\!+\!\int_{nh}^{t}\!f(s-h,x_{n-1}^{\ast}\!(s-h),x_{n-2}^{\ast}\!(s-2h),x_{nm}(s))\!ds,
    t[nh,T];\displaystyle\quad\quad t\in[nh,T];

    is convergent and limmxkm=xk,k=1,n¯.\underset{m\rightarrow\infty}{\lim}x_{km}=x_{k}^{\ast},\ k=\overline{1,n}.

Theorem 1 gives a uniqueness result for the solution of the problem (1)-(2) by successive approximation method and now we want to improve the convergence of this solution. So, here cames the question: can we put xi1,m(t)x_{i-1,m}(t) instead of xi1(t),i=2,n¯x_{i-1}^{\ast}(t),\ i=\overline{2,n} in the conclusion b) of theorem 1? The answer of this question is given by the following theorem.

Theorem 2.

We suppose that the conditions (C1)(C3)(C_{1})-(C_{3}) and

  • (C4)

    there exists Lf>0L_{f}>0 such that

    |f(t,u,v,w1)f(t,u,v,w2)|Lf|w1w2|,t[T,T],u,v,w1,w2;\left|f(t,u,v,w_{1})-f(t,u,v,w_{2})\right|\leq L_{f}\left|w_{1}-w_{2}\right|,\forall t\in[-T,T],u,v,w_{1},w_{2}\in\mathbb{R};

are satisfied. Then the sequence defined by

(6) x0(th)\displaystyle x_{0}(t\!-\!h)\! =φ(0)+htf(sh,x0(sh),x1(s2h),x1m(s))𝑑s,t[h,2h];\displaystyle=\!\varphi(0)\!+\!\int_{h}^{t}\!f(s\!-\!h,x_{0}(s\!-\!h),x_{-1}\!(s\!-\!2h),x_{1m}(s))\!ds,t\in[h,2h];
x1m(th)\displaystyle x_{1m}(t\!-\!h)\! =x1m(h)+2htf(sh,x1m(sh),x0(s2h),x2m(s))𝑑s,t[2h,3h];\displaystyle=\!x_{1m}(h)\!+\!\int_{2h}^{t}\!f(s\!-\!h,x_{1m}\!(s\!-\!h),x_{0}\!(s\!-\!2h),x_{2m}(s))\!ds,t\in[2h,3h];
x2m(th)\displaystyle x_{2m}\!(t\!-\!h)\! =x2m(2h)+3htf(sh,x2m(sh),x1m(s2h),x3m(s))𝑑s,t[3h,4h];\displaystyle=\!x_{2m}(2h)\!+\!\!\int_{3h}^{t}\!f(s\!-\!h,x_{2m}\!(s\!-\!h),x_{1m}(s\!-\!2h),x_{3m}(s))\!ds,t\in[3h,4h];
\displaystyle\cdots
xn1,m(th)\displaystyle x_{n-1,m}\!(t\!-\!h)\! =xn1,m((n1)h)+\displaystyle=\!x_{n-1,m}((n-1)h)+
+nhtf(sh,xn1,m(sh),xn2,m(s2h),xnm(s))𝑑s,t[nh,T];\displaystyle\quad+\int_{nh}^{t}f(\!s-h,x_{n\!-1,m}(s\!-h),x_{n\!-2,m}(s\!-2h),x_{nm}(s)\!)\!ds,t\in[nh,T];

is convergent and limmxkm=xk,k=1,n¯.\underset{m\rightarrow\infty}{\lim}x_{km}=x_{k}^{\ast},\ k=\overline{1,n}.

Proof.

We consider the Banach spaces

X0\displaystyle X_{0} =(C[h,h],0) with 0=maxt[h,h]{x(t)eλ(t+h)},λ>0,\displaystyle=(C[-h,h],\left\|\cdot\right\|_{0})\text{ with }\left\|\cdot\right\|_{0}=\underset{t\in[-h,h]}{\max}\{\left\|x(t)\right\|e^{-\lambda(t+h)}\},\ \lambda>0,
Xi\displaystyle X_{i} =(C[ih,(i+1)h],i) with i=maxt[ih,(i+1)h]{x(t)eλ(tih)},λ>0,\displaystyle=(C[ih,(i+1)h],\left\|\cdot\right\|_{i})\text{ with }\left\|\cdot\right\|_{i}=\underset{t\in[ih,(i+1)h]}{\max}\{\left\|x(t)\right\|e^{-\lambda(t-ih)}\},\ \lambda>0,
Xn\displaystyle X_{n} =(C[nh,T],n) with n=maxt[nh,T]{x(t)eλ(tnh)},λ>0,\displaystyle=(C[nh,T],\left\|\cdot\right\|_{n})\text{ with }\left\|\cdot\right\|_{n}=\underset{t\in[nh,T]}{\max}\{\left\|x(t)\right\|e^{-\lambda(t-nh)}\},\ \lambda>0,

and the operators

A0\displaystyle A_{0} :X0X0,A(x0)(t)=φ(t),t[h,h],\displaystyle:X_{0}\rightarrow X_{0},\ A(x_{0})(t)=\varphi(t),\ t\in[-h,h],
Ai\displaystyle A_{i} :Xi2×Xi1×XiXi,i=1,n1¯\displaystyle:X_{i-2}\times X_{i-1}\times X_{i}\rightarrow X_{i},\ i=\overline{1,n-1}
Ai(xi2,xi1,xi)(t)\displaystyle A_{i}(x_{i-2},x_{i-1},x_{i})(t) =xi1((i1)h)+\displaystyle=x_{i-1}((i-1)h)+
+ihtf(sh,xi1(sh),xi2(s2h),xi(s))𝑑s,t[ih,(i+1)h]\displaystyle\quad+\!\!\int_{ih}^{t}\!\!f(s\!-\!h,\!x_{i-1}\!(s\!-\!h),\!x_{i-2}(s\!-\!2h),\!x_{i}(s))\!ds,t\in[\!ih,\!(i\!+\!1)h]
An\displaystyle A_{n} :Xn2×Xn1×XnXn,\displaystyle:X_{n-2}\times X_{n-1}\times X_{n}\rightarrow X_{n},\
An(xn2,xn1,xn)(t)\displaystyle A_{n}(x_{n-2},x_{n-1},x_{n})(t) =xn1((n1)h+\displaystyle=x_{n-1}((n-1)h+
+nhtf(sh,xn1(sh),xn2(s2h),xn(s))𝑑s,t[nh,T]\displaystyle\quad+\!\!\int_{nh}^{t}\!f(s\!-\!h,\!x_{n-1}\!(s-h),\!x_{n-2}\!(s-2h),\!x_{n}(s))\!ds,t\in[\!nh,T\!]

and

A\displaystyle A :X0××XnX0××Xn\displaystyle:X_{0}\times\cdots\times X_{n}\rightarrow X_{0}\times\cdots\times X_{n}
A(x0,,xn)\displaystyle A(x_{0},\ldots,x_{n}) =(A0(x0),A1(x1,x0,x1),,An(xn2,xn1,xn)).\displaystyle=(A_{0}(x_{0}),A_{1}(x_{-1},x_{0},x_{1}),\ldots,A_{n}(x_{n-2},x_{n-1},x_{n})).

For fixed (x0,,xn)X0××Xn(x_{0},\ldots,x_{n})\in X_{0}\times\cdots\times X_{n}, the sequence (6) means

(x0m,,xnm)=Am(x0,,xn).(x_{0m},\ldots,x_{nm})=A^{m}(x_{0},\ldots,x_{n}).

We need to prove that the operator AA is PO and for this we apply the fibre contraction theorem.

Since A0:X0X0A_{0}:X_{0}\rightarrow X_{0} is a constant operator then A0A_{0} is α0\alpha_{0} contraction with α0=0\alpha_{0}=0, so A0A_{0} is PO and FA0={x0},F_{A_{0}}=\{x_{0}^{\ast}\}, where x0=φx_{0}^{\ast}=\varphi. For i=1,n¯i=\overline{1,n} we have:

Ai(xi2,xi1,xi)Ai(xi2,xi1,yi)iLfλxiyi\left\|A_{i}(x_{i-2},x_{i-1},x_{i})-A_{i}(x_{i-2},x_{i-1},y_{i})\right\|_{i}\leq\frac{L_{f}}{\lambda}\left\|x_{i}-y_{i}\right\|

for all xi2Xi2,xi1Xi1,xiXix_{i-2}\in X_{i-2},x_{i-1}\in X_{i-1},x_{i}\in X_{i}. Choosing λ=Lf+1\lambda=L_{f}+1 we get that Ai(xi2,xi1,):XiXiA_{i}(x_{i-2},x_{i-1},\cdot):X_{i}\rightarrow X_{i} are αi\alpha_{i}-contractions with αi=LfLf+1,\alpha_{i}=\frac{L_{f}}{L_{f}+1}, so we are in the conditions of the fibre contraction theorem, therefore AA is PO and FA={(x0,,xn)}F_{A}=\{(x_{0}^{\ast},\ldots,x_{n}^{\ast})\}. Thus

(x0m,,xnm)=Am(x0,,xn)(x0,,xn)(x_{0m},\ldots,x_{nm})=A^{m}(x_{0},\ldots,x_{n})\rightarrow(x_{0}^{\ast},\ldots,x_{n}^{\ast})

x0m=φx_{0}^{m}=\varphi, for all m,m\in\mathbb{N}, and x1m,,xnmx_{1m},\ldots,x_{nm} are defined by (6). From condition (C3)(C_{3}) and from the definitions of Ai,i=1,n¯A_{i},i=\overline{1,n}, we have

xi1((i1)h)=xi((i1)h),i=1,n¯,x_{i-1}^{\ast}((i-1)h)=x_{i}^{\ast}((i-1)h),i=\overline{1,n},

therefore

x(t)={φ(t),t[h,h]x1(t),t[h,2h]xn(t),t[nh,T]x^{\ast}(t)=\begin{cases}\varphi(t),&t\in[-h,h]\\ x_{1}^{\ast}(t),&t\in[h,2h]\\ \vdots&\\ x_{n}^{\ast}(t),&t\in[nh,T]\end{cases}

is the unique solution in C[T,T]C[-T,T]. ∎

3. Numerical example

In this section we give an example to test the numerical method presented above. We consider the following functional-differential problem with mixed type argument:

(7) x(t)\displaystyle x^{\prime}(t) =4x(t)+x(th)+3x(t+h)+(12h)/12,t[7;7],h=1,\displaystyle=-4x(t)+x(t-h)+3x(t+h)+(1-2h)/12,\ t\in[-7;7],\ h=1,
x(t)\displaystyle x(t) =(t1)/12,t[1;1].\displaystyle=(t-1)/12,\ t\in[-1;1].

We divide the working interval [1;7][-1;7] by the points Pn=nh,P_{n}=nh, n=1,7¯n=\overline{-1,7}. We develop the solution for the step of time s=0.1s=0.1 so we obtain N=10N=10 points on each subinterval In=I_{n}=[Pn1,Pn]P_{n-1},P_{n}]. From implicit function theorem, on each InI_{n}, there exists a solution xn(t)x_{n}(t) and this solution is approximated by Newton’s method. Applying the algorithm explained in the previous section we get:

(8) xnm(t)=xnm1(t)F(t,xnm1(t))/3x_{nm}(t)=x_{nm-1}(t)-F(t,x_{nm-1}(t))/3

with

F(t,xnm1(t))=4x(tnh)+x(t2nh)+3x(t)+12nh12x(tnh).F(t,x_{nm-1}(t))=-4x(t-nh)+x(t-2nh)+3x(t)+\frac{1-2nh}{12}-x^{\prime}(t-nh).

The algorithm from the section 2 is implemented using Matlab in the following way:

Step 0: We construct the vector tt formed by 2N+12N+1 points of the interval [h;h][-h;h] at each step ss. Further, we initialize the known solution for this interval with φ(t)=(t1)/12\varphi(t)=(t-1)/12 and its derivative with φ(t)=1/12.\varphi^{\prime}(t)=1/12.

Step k: We concatenate to the initial vector tt the rest of the points till TT, constructing the interval [nh,T],n=2,7¯[nh,T],\ n=\overline{2,7}. For this interval we get the solution applying Newton’s method. For starting this method, we initialize the value of the first solution with that computed to the last knot at the previous step.

Stoping test: We evaluate the difference in norm between two consecutive computed values xn(k)x_{n}^{(k)} and xn(k+1)x_{n}^{(k+1)} and the iterations stop when it is less than a chosen value (in our case 10610^{-6}). The last values of the solution are retained in the solution vector and are plotted along to the exact solution of the equation (7). These solutions are presented in Fig.1.

We can see from Fig.1 that for this example, the equation (7), our algorithm work perfectly. The exact solution x(t)=(t1)/12x(t)=(t-1)/12 are designed graphically by circles and the numerical solution x=xn(t)x=x_{n}(t) by line. We observe that the numerical solution is overlapping the exact solution.

Refer to caption
Figure 1. Exact and numerical solution for equation (7)

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2013

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