An efficient step method for a system of differential equations with delay

Abstract

Using the step method, we study a system of delay differential equations and we prove the existence and uniqueness of the solution and the convergence of the successive approximation sequence using Perov’s contraction principle and the step method. Also, we propose a new algorithm of successive apprthe oximation sequence generated by the step method and, as an example, we consider some second order delay differential equations with initial conditions.

Authors

D. Otrocol
(Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy,
Technical University of Cluj-Napoca)

M.A. Serban
(Babes-Bolyai University)

Keywords

System of delay differential equations; step method; Picard operators; generalized fibre contraction principle

Cite this paper as:

D. Otrocol; M.A. Serban, An efficient step method for a system of differential equations with delay, J. Appl. Anal. Comput. Vol. 8 (2018) no. 2, pp. 498-508

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About this paper

Journal

Journal of Applied Analysis and Computation

Publisher Name

Wilmington Scientific Publisher, USA

Print ISSN

2156-907X

Online ISSN

2158-5644

MR

MR3760107

ZBL

Google Scholar

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AN EFFICIENT STEP METHOD FOR A SYSTEM OF DIFFERENTIAL EQUATIONS WITH DELAY

Diana Otrocol1,2,โ€  and Marcel-Adrian ลžerban3
Abstract

Using the step method, we study a system of delay differential equations and we prove the existence and uniqueness of the solution and the convergence of the successive approximation sequence using the Perovโ€™s contraction principle and the step method. Also, we propose a new algorithm of successive approximation sequence generated by the step method and, as an example, we consider some second order delay differential equations with initial conditions.

keywords:
System of delay differential equations, Step method, Picard operators, Generalized fibre contraction principle.
{MSC}

47H10, 47N20, 45G10, 45G15.

โ€ โ€ footnotetext: โ€  the corresponding author. Email address: dotrocol@ictp.acad.ro 1 โ€œT. Popoviciuโ€ Institute of Numerical Analysis, Romanian Academy, Fรขntanele 57, 400110 Cluj-Napoca, Romania 2 Department of Mathematics, Technical University of Cluj-Napoca, G. Bariลฃiu 25, 400027 Cluj-Napoca, Romania 3 Department of Mathematics, โ€œBabeลŸ-Bolyaiโ€ University, M. Kogฤƒlniceanu 1, RO-400084 Cluj-Napoca, Romania

1 Introduction

We consider the system of delay differential equations

{x1โ€ฒ(t)=f1(t,x1(t),x2(t),x1(tโˆ’h),x2(tโˆ’h)),tโˆˆ[a,b]x2โ€ฒ(t)=f2(t,x1(t),x2(t),x1(tโˆ’h),x2(tโˆ’h))\left\{\begin{array}[]{l}x_{1}^{\prime}(t)=f_{1}(t,x_{1}(t),x_{2}(t),x_{1}(t-h),x_{2}(t-h)),\ t\in[a,b]\\ x_{2}^{\prime}(t)=f_{2}(t,x_{1}(t),x_{2}(t),x_{1}(t-h),x_{2}(t-h))\end{array}\right. (1)

with initial conditions

{x1(t)=ฯ†1(t),tโˆˆ[aโˆ’h,a]x2(t)=ฯ†2(t),\left\{\begin{array}[]{l}x_{1}(t)=\varphi_{1}(t),\ t\in[a-h,a]\\ x_{2}(t)=\varphi_{2}(t),\end{array}\right. (2)

where f1,f2โˆˆC([a,b]ร—โ„4,โ„),ฯ†1,ฯ†2โˆˆC([aโˆ’h,a],โ„)f_{1},f_{2}\in C([a,b]\times\mathbb{R}^{4},\mathbb{R}),\ \varphi_{1},\varphi_{2}\in C([a-h,a],\mathbb{R}) and h>0h>0 is a parameter. We denote by๐ฑ=(x1,x2),๐Ÿ=(f1,f2)\ \mathbf{x=(}x_{1},x_{2}\mathbf{),\ f}=(f_{1},f_{2}) and๐‹=(ฯ†1,ฯ†2).\ \boldsymbol{\varphi}=(\varphi_{1},\varphi_{2}).\ By a solution of the problem (1)-(2) we mean a function ๐ฑโˆˆC([aโˆ’h,b],โ„2)โˆฉC1([a,b],โ„2)\mathbf{x}\in C([a-h,b],\mathbb{R}^{2})\cap C^{1}([a,b],\mathbb{R}^{2}) which satisfies the system (1) and the conditions (2).

In this paper we study this problem using the ideas of I.A. Rus [14] to obtain existence, uniqueness theorems and the convergence of an iterative algorithm using Perovโ€™s theorem, fibre contraction principle and step method. As an application, we consider a second order functional differential equation with delay and we approximate the solution using the Chebyshev spectral method (see [18, 19, 20, 21]). We compare the obtained results with Matlab dde23 procedure.

Such kind of results have been proved in [15] and [7] in the case of integro-differential equations with lags and in [2] in the case of an integral equation from biomathematics. Other results regarding efficient and rapidly convergent algorithms for solving Volterra differential and integral equations can be found in [4, 5, 8].

Let (X,d)(X,d) be a metric space and A:Xโ†’XA:X\rightarrow X an operator. In this paper we use the terminologies and notations from [13]. For the convenience of the reader we shall recall some of them.

Denote by A0:=1X,A1:=A,An+1:=Aโˆ˜An,nโˆˆโ„•A^{0}:=1_{X},\ A^{1}:=A,\ A^{n+1}:=A\circ A^{n},\ n\in\mathbb{N}, the iterate operators of the operator AA and by FA:={xโˆˆX|A(x)=x}F_{A}:=\left\{x\in X|\ A(x)=x\right\} the fixed point set of A.A.

Definition 1.1.

A:Xโ†’XA:X\rightarrow X is called a Picard operator (briefly PO) if: FA={xโˆ—}F_{A}=\{x^{\ast}\} and An(x)โ†’xโˆ—A^{n}(x)\rightarrow x^{\ast} as nโ†’โˆžn\rightarrow\infty, for all xโˆˆX.x\in X.

Definition 1.2.

A:Xโ†’XA:X\rightarrow X is said to be a weakly Picard operator (briefly WPO) if the sequence (An(x))nโˆˆโ„•(A^{n}(x))_{n\in\mathbb{N}} converges for all xโˆˆXx\in X and the limit (which may depend on xx) is a fixed point of AA.

Definition 1.3.

A matrix Qโˆˆโ„+2ร—2Q\in\mathbb{R}_{+}^{2\times 2} is called a matrix convergent to zero iff Qkโ†’0Q^{k}\rightarrow 0 as kโ†’โˆž.k\rightarrow\infty.

As concerns matrices which are convergent to zero, we mention the following equivalent characterizations:

Theorem 1.4.

(see [10]) Let Qโˆˆโ„+2ร—2Q\in\mathbb{R}_{+}^{2\times 2}. The following statements are equivalent:

  • (i)

    QQ is a matrix convergent to zero;

  • (ii)

    Qkxโ†’0Q^{k}x\rightarrow 0 as kโ†’โˆž,โˆ€xโˆˆโ„2;k\rightarrow\infty,\ \forall x\in\mathbb{R}^{2};

  • (iii)

    I2โˆ’QI_{2}-Q is non-singular and (I2โˆ’Q)โˆ’1=I2+Q+Q2+โ€ฆ;(I_{2}-Q)^{-1}=I_{2}+Q+Q^{2}+\ldots;

  • (iv)

    I2โˆ’QI_{2}-Q is non-singular and (I2โˆ’Q)โˆ’1(I_{2}-Q)^{-1} has nonnegative elements;

  • (v)

    ฮปโˆˆโ„‚,det(Qโˆ’ฮปI2)=0\lambda\in\mathbb{C},\ \det(Q-\lambda I_{2})=0 imply |ฮป|<1;\left|\lambda\right|<1;

  • (v)

    there exits at least one subordinate matrix norm such that โ€–Qโ€–<1\left\|Q\right\|<1.

The matrices convergent to zero were used by Perov [9] to generalize the contraction principle in the case of generalized metric spaces with the metric taking values in the positive cone of โ„2.\mathbb{R}^{2}.

Definition 1.5.

[9] Let (X,d)(X,d) be a complete generalized metric space with d:Xร—Xโ†’โ„+2d:X\times X\rightarrow\mathbb{R}_{+}^{2} and A:Xโ†’XA:X\rightarrow X. The operator AA is called a QQ-contraction if there exists a matrix Qโˆˆโ„+2ร—2Q\in\mathbb{R}_{+}^{2\times 2} such that:

  • (i)

    QQ is a matrix convergent to zero;

  • (ii)

    d(A(x),A(y))โ‰คQd(x,y),โˆ€x,yโˆˆXd(A(x),A(y))\leq Qd(x,y),\ \forall x,y\in X.

Theorem 1.6.

(Perov, [12], [2]) Let (X,d)(X,d) be a complete generalized metric space with d:Xร—Xโ†’โ„+2d:X\times X\rightarrow\mathbb{R}_{+}^{2} and A:Xโ†’XA:X\rightarrow X be a QQ-contraction. Then

  • (i)

    AA is a Picard operator, FA=FAn={xโˆ—},โˆ€nโˆˆโ„•โˆ—F_{A}=F_{A^{n}}=\{x^{\ast}\},\ \forall n\in\mathbb{N}^{\ast};

  • (ii)

    d(An(x),xโˆ—)โ‰ค(I2โˆ’Q)โˆ’1Qnd(x,A(x)),โˆ€xโˆˆX.d(A^{n}(x),x^{\ast})\leq(I_{2}-Q)^{-1}Q^{n}d(x,A(x)),\forall x\in X.

Finally, we recall the following result that is a generalization of the fibre contraction theorem (see I.A. Rus [12], [2]):

Theorem 1.7.

(Theorem 9.1., [11]) Let (Xi,di)(X_{i},d_{i}), i=0,mยฏi=\overline{0,m}, mโ‰ฅ1,m\geq 1, be some generalized metric spaces. Let Ai:X0ร—โ‹ฏร—Xiโ†’XiA_{i}:X_{0}\times\cdots\times X_{i}\rightarrow X_{i}, i=0,mยฏi=\overline{0,m}, be some operators. We suppose that:

  • (i)

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

  • (ii)

    the operator A0A_{0} is a weakly Picard operator;

  • (iii)

    there exist the matrices Qiโˆˆโ„+2ร—2Q_{i}\in\mathbb{R}_{+}^{2\times 2} which converge to zero, such that the operators Ai(x0,โ€ฆ,xiโˆ’1,โ‹…):Xiโ†’Xi,i=1,mยฏA_{i}(x^{0},\ldots,x^{i-1},\cdot):X_{i}\rightarrow X_{i},\ i=\overline{1,m} are QiQ_{i}-generalized contractions, for all xiโˆˆXi,i=1,mยฏx^{i}\in X_{i},i=\overline{1,m};

  • (iv)

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

Then the operator A:X0ร—โ‹ฏร—Xmโ†’X0ร—โ‹ฏร—Xm,A:X_{0}\times\cdots\times X_{m}\rightarrow X_{0}\times\cdots\times X_{m},

A(x0,โ€ฆ,xm)=(A0(x0),A1(x0,x1),โ€ฆ,Am(x0,โ€ฆ,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 a weakly Picard operator. Moreover, if A0A_{0} is a Picard operator, then AA is a Picard operator.

2 Main result

We begin this section with an existence theorem for the solution of the problem (1)-(2). We denote by โˆฅโ‹…โˆฅ:โ„2โ†’โ„+2\left\|\cdot\right\|:\mathbb{R}^{2}\rightarrow\mathbb{R}_{+}^{2} the vectorial norm

โ€–๐ฎโ€–:=(|u1||u2|),๐ฎ=(u1,u2)โˆˆโ„2.\left\|\mathbf{u}\right\|:=\left(\begin{array}[]{c}\left|u_{1}\right|\\ \left|u_{2}\right|\end{array}\right),\ \mathbf{u}=(u_{1},u_{2})\in\mathbb{R}^{2}.

Relative to the problem (1)-(2) we consider the following conditions:

  • (H1)

    ๐ŸโˆˆC([a,b]ร—โ„4,โ„2),๐‹โˆˆC([aโˆ’h,a],โ„2),hโˆˆโ„+โˆ—,a,bโˆˆโ„,a<b;\mathbf{f}\in C([a,b]\times\mathbb{R}^{4},\mathbb{R}^{2}),\ \boldsymbol{\varphi}\in C([a-h,a],\mathbb{R}^{2}),\ h\in\mathbb{R}_{+}^{\ast},\ a,b\in\mathbb{R},\ a<b;

  • (H2)

    there exists Lโˆˆโ„+2ร—2L\in\mathbb{R}_{+}^{2\times 2} such that

    โ€–๐Ÿ(t,๐ฎ1,๐ฏ1)โˆ’๐Ÿ(t,๐ฎ2,๐ฏ2)โ€–โ‰คL(โ€–๐ฎ1โˆ’๐ฎ2โ€–+โ€–๐ฏ1โˆ’๐ฏ2โ€–),\left\|\mathbf{f}(t,\mathbf{u}^{1},\mathbf{v}^{1})-\mathbf{f}(t,\mathbf{u}^{2},\mathbf{v}^{2})\right\|\leq L(\left\|\mathbf{u}^{1}-\mathbf{u}^{2}\right\|+\left\|\mathbf{v}^{1}-\mathbf{v}^{2}\right\|),

    tโˆˆ[a,b],๐ฎ1,๐ฎ2,๐ฏ1,๐ฏ2โˆˆโ„2;t\in[a,b],\mathbf{u}^{1},\mathbf{u}^{2},\mathbf{v}^{1},\mathbf{v}^{2}\in\mathbb{R}^{2};

  • (Hโ€ฒ2{}_{2}^{\prime})

    there exists Lโ€ฒโˆˆโ„+2ร—2L^{\prime}\in\mathbb{R}_{+}^{2\times 2} such that

    โ€–๐Ÿ(t,๐ฎ1,๐ฏ)โˆ’๐Ÿ(t,๐ฎ2,๐ฏ)โ€–โ‰คLโ€ฒ(โ€–๐ฎ1โˆ’๐ฎ2โ€–),\left\|\mathbf{f}(t,\mathbf{u}^{1},\mathbf{v})-\mathbf{f}(t,\mathbf{u}^{2},\mathbf{v})\right\|\leq L^{\prime}(\left\|\mathbf{u}^{1}-\mathbf{u}^{2}\right\|),

    tโˆˆ[a,b],๐ฎ1,๐ฎ2,๐ฏโˆˆโ„2.t\in[a,b],\mathbf{u}^{1},\mathbf{u}^{2},\mathbf{v}\in\mathbb{R}^{2}.

We consider the space X:=C([aโˆ’h,b],โ„2)X:=C([a-h,b],\mathbb{R}^{2}) endowed with the generalized norm โˆฅโ‹…โˆฅB\left\|\cdot\right\|_{B} where โ€–๐ฑโ€–B:=(|x1|B|x2|B),๐ฑ=(x1,x2)\left\|\mathbf{x}\right\|_{B}:=\binom{\left|x_{1}\right|_{B}}{\left|x_{2}\right|_{B}},\ \mathbf{x}=(x_{1},x_{2}) and

|xi|B:=maxaโˆ’hโ‰คtโ‰คb(|xi(t)|eโˆ’ฯ„(tโˆ’a+h)),ฯ„>0,i=1,2.\left|x_{i}\right|_{B}:=\underset{a-h\leq t\leq b}{\max}(\left|x_{i}(t)\right|e^{-\tau(t-a+h)}),\ \tau>0,i=1,2.

It is clear that the space (X,โˆฅโ‹…โˆฅB)(X,\left\|\cdot\right\|_{B}) is a generalized Banach space. Any solution of the problem (1)-(2) is a fixed point of the operator Af:Xโ†’XA_{f}:X\rightarrow X , defined by

Af(๐ฑ)(t)={๐‹(t),tโˆˆ[aโˆ’h,a]๐‹(a)+โˆซt0t๐Ÿ(s,๐ฑ(s),๐ฑ(sโˆ’h))๐‘‘s,tโˆˆ[a,b].A_{f}(\mathbf{x})(t)=\left\{\begin{array}[]{l}\boldsymbol{\varphi}(t),\ t\in[a-h,a]\\ \boldsymbol{\varphi}(a)+\int\limits_{t_{0}}^{t}\mathbf{f}(s,\mathbf{x}(s),\mathbf{x}(s-h))ds,t\in[a,b].\end{array}\right. (3)

Let mโˆˆโ„•โˆ—m\in\mathbb{N}^{\ast} be such that:

a+(mโˆ’1)h<b and a+mhโ‰ฅb.a+(m-1)h<b\text{ and }a+mh\geq b.

We denote by tโˆ’1:=aโˆ’h,t0:=a,ti:=a+ih,i=1,mโˆ’1ยฏ,tm:=b.t_{-1}:=a-h,\ t_{0}:=a,\ t_{i}:=a+ih,\ i=\overline{1,m-1},\ t_{m}:=b.

The following result is well known (see [6]).

Theorem 2.1.

We suppose that the conditions (H1)(H_{1}) and (H2)(H_{2}) hold. Then:

  • (i)

    the problem (1)-(2) has a unique solution ๐ฑโˆ—โˆˆC([tโˆ’1,tm],โ„2)โˆฉC1([t0,tm],โ„2);\mathbf{x}^{\ast}\in C([t_{-1},t_{m}],\mathbb{R}^{2})\cap C^{1}([t_{0},t_{m}],\mathbb{R}^{2});

  • (ii)

    the successive approximations sequence (๐ฑn)nโˆˆโ„•โˆ—,(\mathbf{x}^{n})_{n\in\mathbb{N}^{\ast}}, defined by

    ๐ฑn+1(t):={๐‹(t),tโˆˆ[tโˆ’1,t0]๐‹(t0)+โˆซt0t๐Ÿ(s,๐ฑn(s),๐ฑn(sโˆ’h))๐‘‘s,tโˆˆ[t0,tm]\mathbf{x}^{n+1}(t):=\left\{\begin{array}[]{l}\boldsymbol{\varphi}(t),\ t\in[t_{-1},t_{0}]\\ \boldsymbol{\varphi}(t_{0})+\int\limits_{t_{0}}^{t}\mathbf{f}(s,\mathbf{x}^{n}(s),\mathbf{x}^{n}(s-h))ds,t\in[t_{0},t_{m}]\end{array}\right.

    converges to ๐ฑโˆ—,โˆ€๐ฑ0โˆˆC([tโˆ’1,tm],โ„2);\mathbf{x}^{\ast},\ \forall\mathbf{x}^{0}\in C([t_{-1},t_{m}],\mathbb{R}^{2});

  • (iii)

    the operator AfA_{f} is a Picard operator.

Proof 2.2.

In a standard way we obtain

โ€–Af(๐ฑ)โˆ’Af(๐ฒ)โ€–Bโ‰ค1ฯ„Lโ€–๐ฑโˆ’๐ฒโ€–B,โˆ€๐ฑ,๐ฒโˆˆX.\left\|A_{f}(\mathbf{x})-A_{f}(\mathbf{y})\right\|_{B}\leq\frac{1}{\tau}L\left\|\mathbf{x}-\mathbf{y}\right\|_{B},\forall\mathbf{x},\mathbf{y\in}X.

We can choose ฯ„\tau sufficiently large such that AfA_{f} is QQ-contraction with Q:=1ฯ„LQ:=\frac{1}{\tau}L. So we can apply the Perovโ€™s Theorem (Theorem 1.6) for Af:Xโ†’XA_{f}:X\rightarrow X.

Delay differential equations may be solved as ordinary differential equations over successive intervals [tm,tm+1][t_{m},t_{m+1}] by the step method (see, for example [3] or [1]).

Under the condition (H1), the step method for the problem (1)-(2) consists of the following equations:

  • (p0)(p^{0})

    ๐ฑ0(t)=๐‹(t),tโˆˆ[tโˆ’1,t0];\mathbf{x}^{0}(t)=\boldsymbol{\varphi}(t),t\in[t_{-1},t_{0}];

  • (p1)(p^{1})

    ๐ฑ1(t)=๐‹(t0)+โˆซt0t๐Ÿ(s,๐ฑ1(s),๐‹(sโˆ’h))๐‘‘s,tโˆˆ[t0,t1];\mathbf{x}^{1}(t)=\boldsymbol{\varphi}(t_{0})+\textstyle\int\nolimits_{t_{0}}^{t}\mathbf{f}(s,\mathbf{x}^{1}(s),\boldsymbol{\varphi}(s-h))ds,\ t\in[t_{0},t_{1}];

  • (p2)(p^{2})

    ๐ฑ2(t)=๐ฑ1,โˆ—(t1)+โˆซt1t๐Ÿ(s,๐ฑ2(s),๐ฑ1,โˆ—(sโˆ’h))๐‘‘s,tโˆˆ[t1,t2];\mathbf{x}^{2}(t)=\mathbf{x}^{1,\ast}(t_{1})+\textstyle\int\nolimits_{t_{1}}^{t}\mathbf{f}(s,\mathbf{x}^{2}(s),\mathbf{x}^{1,\ast}(s-h))ds,\ t\in[t_{1},t_{2}];

    โ‹ฏ\cdots

  • (pmโˆ’1)(p^{m-1})

    ๐ฑmโˆ’1(t)=๐ฑmโˆ’2,โˆ—(tmโˆ’2)+โˆซtmโˆ’2t๐Ÿ(s,๐ฑmโˆ’1(s),๐ฑmโˆ’2,โˆ—(sโˆ’h))๐‘‘s,tโˆˆ[tmโˆ’2,tmโˆ’1];\mathbf{x}^{m-1}(t)=\mathbf{x}^{m-2,\ast}(t_{m-2})+\textstyle\int\nolimits_{t_{m-2}}^{t}\mathbf{f}(s,\mathbf{x}^{m-1}(s),\mathbf{x}^{m-2,\ast}(s-h))ds,\ t\in[t_{m-2},t_{m-1}];

  • (pm)(p^{m})

    ๐ฑm(t)=๐ฑmโˆ’1,โˆ—(tmโˆ’1)+โˆซtmโˆ’1t๐Ÿ(s,๐ฑm(s),๐ฑmโˆ’1,โˆ—(sโˆ’h))๐‘‘s,tโˆˆ[tmโˆ’1,tm];\mathbf{x}^{m}(t)=\mathbf{x}^{m-1,\ast}(t_{m-1})+\textstyle\int\nolimits_{t_{m-1}}^{t}\mathbf{f}(s,\mathbf{x}^{m}(s),\mathbf{x}^{m-1,\ast}(s-h))ds,\ t\in[t_{m-1},t_{m}];

where ๐ฑi,โˆ—=(x1i,โˆ—,x2i,โˆ—)โˆˆC([tiโˆ’1,ti],โ„2)\mathbf{x}^{i,\ast}=(x_{1}^{i,\ast},x_{2}^{i,\ast})\in C([t_{i-1},t_{i}],\mathbb{R}^{2}) is the unique solution of the equation (pi),i=1,mยฏ.(p^{i}),\ i=\overline{1,m}.

So, by using the step method and an idea from [14], we obtain:

Theorem 2.3.

We suppose that the conditions (H1)(H_{1}) and (H2โ€ฒ)(H_{2}^{\prime}) hold. Then:

  • (i)

    the problem (1)-(2) has a unique solution ๐ฑโˆ—\mathbf{x}^{\ast} in C([tโˆ’1,tm],โ„2)C([t_{-1},t_{m}],\mathbb{R}^{2}), where

    ๐ฑโˆ—(t)={๐‹(t),tโˆˆ[tโˆ’1,t0]๐ฑ1,โˆ—(t),tโˆˆ[t0,t1]โ‹ฏ๐ฑm,โˆ—(t),tโˆˆ[tmโˆ’1,tm]\mathbf{x}^{\ast}(t)=\left\{\begin{array}[]{l}\boldsymbol{\varphi}(t),t\in[t_{-1},t_{0}]\\ \mathbf{x}^{1,\ast}(t),t\in[t_{0},t_{1}]\\ \cdots\\ \mathbf{x}^{m,\ast}(t),t\in[t_{m-1},t_{m}]\end{array}\right.
  • (ii)

    for each ๐ฑi,0=(x1i,0,x2i,0)โˆˆC([tiโˆ’1,ti],โ„2),i=1,mยฏ\mathbf{x}^{i,0}=(x_{1}^{i,0},x_{2}^{i,0})\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m}, the sequence defined by:

    ๐ฑi,n+1(t)=๐ฑiโˆ’1,โˆ—(tiโˆ’1)+โˆซtiโˆ’1t๐Ÿ(s,๐ฑi,n(s),๐ฑiโˆ’1,โˆ—(sโˆ’h))๐‘‘s,\mathbf{x}^{i,n+1}(t)=\mathbf{x}^{i-1,\ast}(t_{i-1})+\textstyle\int\nolimits_{t_{i-1}}^{t}\mathbf{f}(s,\mathbf{x}^{i,n}(s),\mathbf{x}^{i-1,\ast}(s-h))ds,\

    for tโˆˆ[tiโˆ’1,ti],t\in[t_{i-1},t_{i}], (with ๐ฑ0,โˆ—(t0):=๐‹(t0)\mathbf{x}^{0,\ast}(t_{0}):=\boldsymbol{\varphi}(t_{0})), converges and limnโ†’โˆž๐ฑi,n=๐ฑi,โˆ—,i=1,mยฏ.\underset{n\rightarrow\infty}{\lim}\mathbf{x}^{i,n}=\mathbf{x}^{i,\ast},\ i=\overline{1,m}.

Proof 2.4.

In order to prove this theorem we apply Perovโ€™s theorem for each step [tiโˆ’1,ti],i=1,mยฏ.[t_{i-1},t_{i}],\ i=\overline{1,m}.

For the first step, we consider the Banach space X1:=(C([t0,t1],โ„2),โˆฅโ‹…โˆฅ1B),X_{1}:=(C([t_{0},t_{1}],\mathbb{R}^{2}),\left\|\cdot\right\|_{1B}), where

โˆฅโ‹…โˆฅ1B:=maxt0โ‰คtโ‰คt1(โˆฅ๐ฑ(t)โˆฅeโˆ’ฯ„(tโˆ’t0)),ฯ„>0\left\|\cdot\right\|_{1B}:=\underset{t_{0}\leq t\leq t_{1}}{\max}(\left\|\mathbf{x}(t)\right\|e^{-\tau(t-t_{0})}),\ \tau>0

and the operator A1:X1โ†’X1A_{1}:X_{1}\rightarrow X_{1} defined by

A1(๐ฑ)(t)=๐‹(t0)+โˆซt0t๐Ÿ(s,๐ฑ(s),๐‹(sโˆ’h))๐‘‘s.A_{1}(\mathbf{x})(t)=\boldsymbol{\varphi}(t_{0})+{\textstyle\int\nolimits_{t_{0}}^{t}}\mathbf{f}(s,\mathbf{x}(s),\boldsymbol{\varphi}(s-h))ds.

For ๐ฑ,๐ฒโˆˆX1\mathbf{x},\mathbf{y}\in X_{1}, we obtain

โ€–A1(๐ฑ)โˆ’A1(๐ฒ)โ€–1Bโ‰ค1ฯ„Lโ€ฒโ€–๐ฑโˆ’๐ฒโ€–1B.\left\|A_{1}(\mathbf{x})-A_{1}(\mathbf{y})\right\|_{1B}\leq\frac{1}{\tau}L^{\prime}\left\|\mathbf{x}-\mathbf{y}\right\|_{1B}.

We can choose ฯ„\tau sufficiently large such that A1A_{1} is Q1:=1ฯ„Lโ€ฒQ_{1}:=\frac{1}{\tau}L^{\prime}-contraction, therefore FA1:={๐ฑ1โˆ—}.F_{A_{1}}:=\{\mathbf{x}_{1}^{\ast}\}.

For the next steps, we consider the Banach spaces Xi:=(C([tiโˆ’1,ti],โ„2),โˆฅโ‹…โˆฅiB),i=2,mยฏX_{i}:=(C([t_{i-1},t_{i}],\mathbb{R}^{2}),\left\|\cdot\right\|_{iB}),\ i=\overline{2,m}, where

โ€–๐ฑโ€–iB:=maxtiโˆ’1โ‰คtโ‰คti(โ€–๐ฑ(t)โ€–eโˆ’ฯ„(tโˆ’tiโˆ’1)),ฯ„>0,\left\|\mathbf{x}\right\|_{iB}:=\underset{t_{i-1}\leq t\leq t_{i}}{\max}(\left\|\mathbf{x}(t)\right\|e^{-\tau(t-t_{i-1})}),\ \tau>0,

and the operators Ai:Xiโ†’XiA_{i}:X_{i}\rightarrow X_{i}, defined by

Ai(๐ฑ)(t)=๐ฑiโˆ’1,โˆ—(tiโˆ’1)+โˆซtiโˆ’1t๐Ÿ(s,๐ฑi(s),๐ฑiโˆ’1,โˆ—(sโˆ’h))๐‘‘s.A_{i}(\mathbf{x})(t)=\mathbf{x}^{i-1,\ast}(t_{i-1})+{\textstyle\int\nolimits_{t_{i-1}}^{t}}\mathbf{f}(s,\mathbf{x}^{i}(s),\mathbf{x}^{i-1,\ast}(s-h))ds.

For ๐ฑ,๐ฒโˆˆXi\mathbf{x},\mathbf{y}\in X_{i}, we obtain

โ€–Ai(๐ฑ)โˆ’Ai(๐ฒ)โ€–iBโ‰ค1ฯ„Lโ€ฒโ€–๐ฑโˆ’๐ฒโ€–iB.\left\|A_{i}(\mathbf{x})-A_{i}(\mathbf{y})\right\|_{iB}\leq\frac{1}{\tau}L^{\prime}\left\|\mathbf{x}-\mathbf{y}\right\|_{iB}.

We can choose ฯ„\tau\ sufficiently large such that AiA_{i} is Qi:=1ฯ„Lโ€ฒQ_{i}:=\frac{1}{\tau}L^{\prime}-contraction, therefore FAi:={๐ฑi,โˆ—},i=2,mยฏ.F_{A_{i}}:=\{\mathbf{x}^{i,\ast}\},\ i=\overline{2,m}.

We have that ๐›—(t0)=๐ฑ1,โˆ—(t0)\boldsymbol{\varphi}(t_{0})=\mathbf{x}^{1,\ast}(t_{0}) and from definition of Ai,i=2,mยฏA_{i},i=\overline{2,m}, we obtain

๐ฑiโˆ’1,โˆ—(tiโˆ’1)=๐ฑi,โˆ—(tiโˆ’1),i=2,mยฏ,\mathbf{x}^{i-1,\ast}(t_{i-1})=\mathbf{x}^{i,\ast}(t_{i-1}),\ i=\overline{2,m},

therefore

๐ฑโˆ—(t)={๐‹(t),tโˆˆ[tโˆ’1,t0]๐ฑ1,โˆ—(t),tโˆˆ[t0,t1]โ‹ฏ๐ฑm,โˆ—(t),tโˆˆ[tmโˆ’1,tm]\mathbf{x}^{\ast}(t)=\left\{\begin{array}[c]{l}\boldsymbol{\varphi}(t),t\in[t_{-1},t_{0}]\\ \mathbf{x}^{1,\ast}(t),t\in[t_{0},t_{1}]\\ \cdots\\ \mathbf{x}^{m,\ast}(t),t\in[t_{m-1},t_{m}]\end{array}\right.

is the unique solution in C([tโˆ’1,tm],โ„2).C([t_{-1},t_{m}],\mathbb{R}^{2}).

Next, we will study if it is possible to replace ๐ฑi,โˆ—\mathbf{x}^{i,\ast} by the approximation ๐ฑi,n,i=1,mยฏ\mathbf{x}^{i,n},\ i=\overline{1,m} in the conclusion (ii)(ii) of the Theorem 2.3. Applying the results from [14] we have

Theorem 2.5.

In the condition of Theorem 2.3, for each ๐ฑi,0โˆˆC([tiโˆ’1,ti],โ„2),i=1,mยฏ,\mathbf{x}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m}, the sequences defined by:

๐ฑ1,n+1(t)\displaystyle\mathbf{x}^{1,n+1}(t) =๐‹(t0)+โˆซt0t๐Ÿ(s,๐ฑ1,n(s),๐‹(sโˆ’h))๐‘‘s, for tโˆˆ[t0,t1]\displaystyle=\boldsymbol{\varphi}(t_{0})+\textstyle\int\nolimits_{t_{0}}^{t}\mathbf{f}(s,\mathbf{x}^{1,n}(s),\boldsymbol{\varphi}(s-h))ds,\text{ for }t\in[t_{0},t_{1}] (4)
๐ฑ2,n+1(t)\displaystyle\mathbf{x}^{2,n+1}(t) =๐ฑ1,n(t1)+โˆซt1t๐Ÿ(s,๐ฑ2,n(s),๐ฑ1,n(sโˆ’h))๐‘‘s, for tโˆˆ[t1,t2]\displaystyle=\mathbf{x}^{1,n}(t_{1})+\textstyle\int\nolimits_{t_{1}}^{t}\mathbf{f}(s,\mathbf{x}^{2,n}(s),\mathbf{x}^{1,n}(s-h))ds,\text{ for }t\in[t_{1},t_{2}]
โ‹ฏ\displaystyle\cdots
๐ฑm,n+1(t)\displaystyle\mathbf{x}^{m,n+1}(t) =๐ฑmโˆ’1,n(tmโˆ’1)+โˆซtmโˆ’1t๐Ÿ(s,๐ฑm,n(s),๐ฑmโˆ’1,n(sโˆ’h))๐‘‘s, for tโˆˆ[tmโˆ’1,tm]\displaystyle=\mathbf{x}^{m-1,n}(t_{m-1})+\textstyle\int\nolimits_{t_{m-1}}^{t}\mathbf{f}(s,\mathbf{x}^{m,n}(s),\mathbf{x}^{m-1,n}(s-h))ds,\text{ for }t\in[t_{m-1},t_{m}]

converge and limnโ†’โˆž๐ฑi,n=๐ฑi,โˆ—,i=1,mยฏ.\underset{n\rightarrow\infty}{\lim}\mathbf{x}^{i,n}=\mathbf{x}^{i,\ast},\ i=\overline{1,m}.

Proof 2.6.

We consider the following Banach spaces X0:=(C([tโˆ’1,t0],โ„2),โˆฅโ‹…โˆฅ0B)X_{0}:=(C([t_{-1},t_{0}],\mathbb{R}^{2}),\left\|\cdot\right\|_{0B}), where

โˆฅโ‹…โˆฅ0B:=maxtโˆ’1โ‰คtโ‰คt0(โˆฅ๐ฑ(t)โˆฅeโˆ’ฯ„(tโˆ’tโˆ’1)),ฯ„>0\left\|\cdot\right\|_{0B}:=\underset{t_{-1}\leq t\leq t_{0}}{\max}(\left\|\mathbf{x}(t)\right\|e^{-\tau(t-t_{-1})}),\ \tau>0

and Xi:=(C([tiโˆ’1,ti],โ„2),โˆฅโ‹…โˆฅiB),i=1,mยฏX_{i}:=(C([t_{i-1},t_{i}],\mathbb{R}^{2}),\left\|\cdot\right\|_{iB}),\ i=\overline{1,m} (as in the proof of Theorem 2.3) and the operators

A0:X0โ†’X0,A0(๐ฑ0)(t)=๐‹(t),tโˆˆ[tโˆ’1,t0],\displaystyle A_{0}:X_{0}\rightarrow X_{0},\ A_{0}(\mathbf{x}^{0})(t)=\boldsymbol{\varphi}(t),\ t\in[t_{-1},t_{0}],
Ai:Xiโˆ’1ร—Xiโ†’Xi,i=1,mยฏ\displaystyle A_{i}:X_{i-1}\times X_{i}\rightarrow X_{i},i=\overline{1,m}
Ai(๐ฑiโˆ’1,๐ฑi)(t)=๐ฑiโˆ’1(tiโˆ’1)+โˆซtiโˆ’1t๐Ÿ(s,๐ฑi(s),๐ฑiโˆ’1(sโˆ’h))๐‘‘s,tโˆˆ[tiโˆ’1,ti],\displaystyle A_{i}(\mathbf{x}^{i-1},\mathbf{x}^{i})(t)=\mathbf{x}^{i-1}(t_{i-1})+{\textstyle\int\nolimits_{t_{i-1}}^{t}}\mathbf{f}(s,\mathbf{x}^{i}(s),\mathbf{x}^{i-1}(s-h))ds,t\in[t_{i-1},t_{i}],

and let AA be the operator A:X0ร—X1ร—โ‹ฏร—Xmโ†’X0ร—X1ร—โ‹ฏร—XmA:X_{0}\times X_{1}\times\cdots\times X_{m}\rightarrow X_{0}\times X_{1}\times\cdots\times X_{m} defined by

A(๐ฑ0,๐ฑ1,โ€ฆ,๐ฑm)=(A0(๐ฑ0),A1(๐ฑ0,๐ฑ1),โ€ฆ,Am(๐ฑmโˆ’1,๐ฑm)).A(\mathbf{x}^{0},\mathbf{x}^{1},\ldots,\mathbf{x}^{m})=(A_{0}(\mathbf{x}^{0}),A_{1}(\mathbf{x}^{0},\mathbf{x}^{1}),\ldots,A_{m}(\mathbf{x}^{m-1},\mathbf{x}^{m})).

It is easy to see that for fixed (๐ฑ0,๐ฑ1,โ€ฆ,๐ฑm)โˆˆX0ร—X1ร—โ‹ฏร—Xm(\mathbf{x}^{0},\mathbf{x}^{1},\ldots,\mathbf{x}^{m})\in X_{0}\times X_{1}\times\cdots\times X_{m} the sequence defined by (4) means (๐ฑ0,n,๐ฑ1,n,โ€ฆ,๐ฑm,n)=An(๐ฑ0,๐ฑ1,โ€ฆ,๐ฑm).(\mathbf{x}^{0,n},\mathbf{x}^{1,n},\ldots,\mathbf{x}^{m,n})=A^{n}(\mathbf{x}^{0},\mathbf{x}^{1},\ldots,\mathbf{x}^{m}). To prove the conclusion we need to prove that the operator AA is a Picard operator and for this we apply Theorem 1.7.

Since A0:X0โ†’X0A_{0}:X_{0}\rightarrow X_{0} is a constant operator then A0A_{0} is Q0Q_{0}-contraction where Q0Q_{0} is the null matrix, so A0A_{0} is a Picard operator and ๐ฑ0,โˆ—=๐›—.\mathbf{x}^{0,\ast}=\boldsymbol{\varphi}. For i=1,mยฏi=\overline{1,m}, we have the inequalities:

โ€–Ai(๐ฑiโˆ’1,๐ฑi)โˆ’Ai(๐ฑiโˆ’1,๐ฒi)โ€–iBโ‰ค1ฯ„Lโ€ฒโ€–๐ฑiโˆ’๐ฒiโ€–iB\left\|A_{i}(\mathbf{x}^{i-1},\mathbf{x}^{i})-A_{i}(\mathbf{x}^{i-1},\mathbf{y}^{i})\right\|_{iB}\leq\tfrac{1}{\tau}L^{\prime}\left\|\mathbf{x}^{i}-\mathbf{y}^{i}\right\|_{iB}

for all ๐ฑiโˆ’1โˆˆXiโˆ’1\mathbf{x}^{i-1}\in X_{i-1} and ๐ฑi,๐ฒiโˆˆXi.\mathbf{x}^{i},\mathbf{y}^{i}\in X_{i}. For ฯ„\tau sufficiently large we get that Ai(๐ฑiโˆ’1,โ‹…):Xiโ†’XiA_{i}(\mathbf{x}^{i-1},\cdot):X_{i}\rightarrow X_{i} are QiQ_{i}-contractions with Qi=1ฯ„Lโ€ฒQ_{i}=\frac{1}{\tau}L^{\prime}, so we are in the conditions of Theorem 1.7, therefore AA is a Picard operator and FA={(๐ฑ0,โˆ—,โ€ฆ,๐ฑm,โˆ—)}F_{A}=\{(\mathbf{x}^{0,\ast},\ldots,\mathbf{x}^{m,\ast})\}, thus

(๐ฑ0,n,๐ฑ1,n,โ€ฆ,๐ฑm,n)=An(๐ฑ0,๐ฑ1,โ€ฆ,๐ฑm)โ†’(๐ฑ0,โˆ—,โ€ฆ,๐ฑm,โˆ—),(\mathbf{x}^{0,n},\mathbf{x}^{1,n},\ldots,\mathbf{x}^{m,n})=A^{n}(\mathbf{x}^{0},\mathbf{x}^{1},\ldots,\mathbf{x}^{m})\rightarrow(\mathbf{x}^{0,\ast},\ldots,\mathbf{x}^{m,\ast}),

with ๐ฑ0,n=๐›—\mathbf{x}^{0,n}=\boldsymbol{\varphi} and ๐ฑ1,n,โ€ฆ,๐ฑm,n\mathbf{x}^{1,n},\ldots,\mathbf{x}^{m,n} are defined by (4), for all nโˆˆโ„•n\in\mathbb{N}. From the definitions of Ai,i=1,mยฏA_{i},i=\overline{1,m}, we have

๐ฑiโˆ’1,โˆ—(tiโˆ’1)=๐ฑi,โˆ—(tiโˆ’1),i=1,mยฏ\mathbf{x}^{i-1,\ast}(t_{i-1})=\mathbf{x}^{i,\ast}(t_{i-1}),\ i=\overline{1,m}

and therefore

๐ฑโˆ—(t)={๐‹(t),tโˆˆ[tโˆ’1,t0]๐ฑ1,โˆ—(t),tโˆˆ[t0,t1]โ‹ฏ๐ฑm,โˆ—(t),tโˆˆ[tmโˆ’1,tm]\mathbf{x}^{\ast}(t)=\left\{\begin{array}[c]{l}\boldsymbol{\varphi}(t),t\in[t_{-1},t_{0}]\\ \mathbf{x}^{1,\ast}(t),t\in[t_{0},t_{1}]\\ \cdots\\ \mathbf{x}^{m,\ast}(t),t\in[t_{m-1},t_{m}]\end{array}\right.

is the unique solution in C([tโˆ’1,tm],โ„2)C([t_{-1},t_{m}],\mathbb{R}^{2}).

3 Application

We consider the following second order delay differential equation

โˆ’xโ€ฒโ€ฒ(t)=f(t,x(t),x(tโˆ’h)),tโˆˆ[a,b]-x^{\prime\prime}(t)=f(t,x(t),x(t-h)),\ t\in[a,b] (5)

with initial conditions

{x(t)=ฯ†(t),tโˆˆ[aโˆ’h,a]xโ€ฒ(t)=ฯ†โ€ฒ(t),tโˆˆ[aโˆ’h,a],\left\{\begin{array}[]{l}x(t)=\varphi(t),\ t\in[a-h,a]\\ x^{\prime}(t)=\varphi^{\prime}(t),\ t\in[a-h,a],\end{array}\right. (6)

where f:[a,b]ร—โ„ร—โ„โ†’โ„,hโˆˆโ„+โˆ—,a,bโˆˆโ„,a<b,f:[a,b]\times\mathbb{R}\times\mathbb{R}\rightarrow\mathbb{R},\ h\in\mathbb{R}_{+}^{\ast},\ a,b\in\mathbb{R},\ a<b, ฯ†,ฯ†โ€ฒ:[aโˆ’h,a]โ†’โ„.\varphi,\varphi^{\prime}:[a-h,a]\rightarrow\mathbb{R}.

The problem (5)-(6) can be written in the following form

(y1โ€ฒ(t)y2โ€ฒ(t))=(y2(t)โˆ’f(t,y1(t),y1(tโˆ’h))),tโˆˆ[a,b]\left(\begin{array}[]{c}y_{1}^{\prime}(t)\\ y_{2}^{\prime}(t)\end{array}\right)=\left(\begin{array}[]{c}y_{2}(t)\\ -f(t,y_{1}(t),y_{1}(t-h))\end{array}\right),\ t\in[a,b] (7)

with initial conditions

(y1(t)y2(t))=(ฯ†(t)ฯ†โ€ฒ(t)),tโˆˆ[aโˆ’h,a]\left(\begin{array}[]{c}y_{1}(t)\\ y_{2}(t)\end{array}\right)=\left(\begin{array}[]{c}\varphi(t)\\ \varphi^{\prime}(t)\end{array}\right),\ t\in[a-h,a] (8)

where ๐ฒ:=(y1y2)=(xxโ€ฒ)\mathbf{y:}=\left(\begin{array}[]{c}y_{1}\\ y_{2}\end{array}\right)=\left(\begin{array}[]{c}x\\ x^{\prime}\end{array}\right), ๐…โˆˆC([a,b]ร—โ„2,โ„2)\mathbf{F}\in C([a,b]\times\mathbb{R}^{2},\mathbb{R}^{2}), ๐…:=(f1f2)=(y2โˆ’f)\mathbf{F:=}\left(\begin{array}[]{c}f_{1}\\ f_{2}\end{array}\right)=\left(\begin{array}[]{c}y_{2}\\ -f\end{array}\right), ๐‹โˆˆC([aโˆ’h,a],โ„2)\boldsymbol{\varphi}\in C([a-h,a],\mathbb{R}^{2}), ๐‹:=(ฯ†1ฯ†2)=(ฯ†ฯ†โ€ฒ)\boldsymbol{\varphi}:=\left(\begin{array}[]{c}\varphi_{1}\\ \varphi_{2}\end{array}\right)=\left(\begin{array}[]{c}\varphi\\ \varphi^{\prime}\end{array}\right) and h>0h>0 is a parameter.

Relative to the problem (7)-(8) we consider the following conditions:

  • (C1)

    fโˆˆC([a,b]ร—โ„2,โ„),ฯ†โˆˆC1([aโˆ’h,a],โ„);f\in C([a,b]\times\mathbb{R}^{2},\mathbb{R}),\ \varphi\in C^{1}([a-h,a],\mathbb{R});

  • (C2)

    there exists lfโˆˆโ„+l_{f}\in\mathbb{R}_{+} such that

    |f(t,u1,v)โˆ’f(t,u2,v)|โ‰คlf|u1โˆ’u2|,\left|f(t,u_{1},v)-f(t,u_{2},v)\right|\leq l_{f}\left|u_{1}-u_{2}\right|,

    tโˆˆ[a,b],u1,u2,vโˆˆโ„.t\in[a,b],u_{1},u_{2},v\in\mathbb{R}.

Let mโˆˆโ„•โˆ—m\in\mathbb{N}^{\ast} be such that:

a+(mโˆ’1)h<b and a+mhโ‰ฅb.a+(m-1)h<b\text{ and }a+mh\geq b.

We denote by tโˆ’1:=aโˆ’ht_{-1}:=a-h, t0:=a\ t_{0}:=a, ti:=a+ih\ t_{i}:=a+ih, i=1,mโˆ’1ยฏ\ i=\overline{1,m-1}, tm:=bt_{m}:=b.

Applying the results from Section 2 we have the following theorems.

Theorem 3.1.

We suppose that the conditions (C1)(C_{1}) and (C2)(C_{2}) hold. Then:

  • (i)

    the problem (7)-(8) has a unique solution ๐ฒโˆ—\mathbf{y}^{\ast} in C([tโˆ’1,tm],โ„2)C([t_{-1},t_{m}],\mathbb{R}^{2}) where

    ๐ฒโˆ—(t)={๐‹(t0),tโˆˆ[tโˆ’1,t0]๐ฒ1,โˆ—(t),tโˆˆ[t0,t1]โ‹ฏ๐ฒm,โˆ—(t),tโˆˆ[tmโˆ’1,tm]\mathbf{y}^{\ast}(t)=\left\{\begin{array}[]{l}\boldsymbol{\varphi}(t_{0}),t\in[t_{-1},t_{0}]\\ \mathbf{y}^{1,\ast}(t),t\in[t_{0},t_{1}]\\ \cdots\\ \mathbf{y}^{m,\ast}(t),t\in[t_{m-1},t_{m}]\end{array}\right.
  • (ii)

    for each ๐ฒi,0โˆˆC([tiโˆ’1,ti],โ„2),i=1,mโˆ’1ยฏ\mathbf{y}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m-1}, ๐ฒm,0โˆˆC([tmโˆ’1,tm],โ„2)\ \mathbf{y}^{m,0}\in C([t_{m-1},t_{m}],\mathbb{R}^{2}), the sequence defined by:

    ๐ฒi,n+1(t)=๐ฒiโˆ’1,โˆ—(tiโˆ’1)+โˆซtiโˆ’1t๐…(s,๐ฒi,n(s),๐ฒiโˆ’1,โˆ—(sโˆ’h))๐‘‘s,\mathbf{y}^{i,n+1}(t)=\mathbf{y}^{i-1,\ast}(t_{i-1})+\textstyle\int\nolimits_{t_{i-1}}^{t}\mathbf{F}(s,\mathbf{y}^{i,n}(s),\mathbf{y}^{i-1,\ast}(s-h))ds,\

    for tโˆˆ[tiโˆ’1,ti],t\in[t_{i-1},t_{i}], converges and limnโ†’โˆž๐ฒi,n=๐ฒi,โˆ—,i=1,mยฏ.\underset{n\rightarrow\infty}{\lim}\mathbf{y}^{i,n}=\mathbf{y}^{i,\ast},\ i=\overline{1,m}.

Proof 3.2.

From condition (C1) we have that ๐…โˆˆC([a,b]ร—โ„2,โ„2),๐›—โˆˆC([aโˆ’h,a],โ„2).\mathbf{F}\in C([a,b]\times\mathbb{R}^{2},\mathbb{R}^{2}),\ \boldsymbol{\varphi}\in C([a-h,a],\mathbb{R}^{2}).

From (C2) we have

โ€–๐…(t,๐ฎ1,๐ฏ)โˆ’๐…(t,๐ฎ2,๐ฏ)โ€–\displaystyle\left\|\mathbf{F}(t,\mathbf{u}^{1},\mathbf{v})-\mathbf{F}(t,\mathbf{u}^{2},\mathbf{v})\right\| =โ€–(u21โˆ’f(t,u11,v1))โˆ’(u22โˆ’f(t,u12,v1))โ€–\displaystyle=\left\|\left(\begin{array}[c]{c}u_{2}^{1}\\ -f(t,u_{1}^{1},v_{1})\end{array}\right)-\left(\begin{array}[c]{c}u_{2}^{2}\\ -f(t,u_{1}^{2},v_{1})\end{array}\right)\right\|
โ‰ค(01lf0)(|u11โˆ’u12||u21โˆ’u22|),\displaystyle\leq\left(\begin{array}[c]{cc}0&1\\ l_{f}&0\end{array}\right)\left(\begin{array}[c]{c}\left|u_{1}^{1}-u_{1}^{2}\right|\\ \left|u_{2}^{1}-u_{2}^{2}\right|\end{array}\right),

for all ๐ฎ1=(u11,u21),๐ฎ2=(u12,u22),๐ฏ=(v1,v2)โˆˆโ„2\mathbf{u}^{1}=(u_{1}^{1},u_{2}^{1}),\ \mathbf{u}^{2}=(u_{1}^{2},u_{2}^{2}),\ \mathbf{v}=(v_{1},v_{2})\in\mathbb{R}^{2}. So the problem (7)-(8) verifies the conditions of the Theorem 2.3.

Theorem 3.3.

In the condition of Theorem 3.1, for each ๐ฒi,0โˆˆC([tiโˆ’1,ti],โ„2),i=1,mยฏ,\mathbf{y}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m},\ the sequences defined by:

๐ฒ1,n+1(t)\displaystyle\mathbf{y}^{1,n+1}(t) =๐‹(t0)+โˆซt0t๐…(s,๐ฒ1,n(s),๐‹(sโˆ’h))๐‘‘s, for tโˆˆ[t0,t1]\displaystyle=\boldsymbol{\varphi}(t_{0})+\textstyle\int\nolimits_{t_{0}}^{t}\mathbf{F}(s,\mathbf{y}^{1,n}(s),\boldsymbol{\varphi}(s-h))ds,\text{ for }t\in[t_{0},t_{1}] (9)
๐ฒ2,n+1(t)\displaystyle\mathbf{y}^{2,n+1}(t) =๐ฒ1,n(t1)+โˆซt1t๐…(s,๐ฒ2,n(s),๐ฒ1,n(sโˆ’h))๐‘‘s, for tโˆˆ[t1,t2]\displaystyle=\mathbf{y}^{1,n}(t_{1})+\textstyle\int\nolimits_{t_{1}}^{t}\mathbf{F}(s,\mathbf{y}^{2,n}(s),\mathbf{y}^{1,n}(s-h))ds,\text{ for }t\in[t_{1},t_{2}]
โ‹ฏ\displaystyle\cdots
๐ฒm,n+1(t)\displaystyle\mathbf{y}^{m,n+1}(t) =๐ฒmโˆ’1,n(tmโˆ’1)+โˆซtmโˆ’1t๐…(s,๐ฒm,n(s),๐ฒmโˆ’1,n(sโˆ’h))๐‘‘s, for tโˆˆ[tmโˆ’1,tm]\displaystyle=\mathbf{y}^{m-1,n}(t_{m-1})+\textstyle\int\nolimits_{t_{m-1}}^{t}\mathbf{F}(s,\mathbf{y}^{m,n}(s),\mathbf{y}^{m-1,n}(s-h))ds,\text{ for }t\in[t_{m-1},t_{m}]

converge and limnโ†’โˆž๐ฒi,n=๐ฒi,โˆ—,i=1,mยฏ.\underset{n\rightarrow\infty}{\lim}\mathbf{y}^{i,n}=\mathbf{y}^{i,\ast},\ i=\overline{1,m}.

4 Numerical method

In this section we test some second order initial value problems to show the efficiency and accuracy of the proposed method. We follow the technique from D. Trif [20] where the approximating solution is given by a finite sum of the Chebyshev series. The same technique was used in [2], [7] and [15] for integro-differential equations with delays.

We divide the working interval by the points Pk=k,k=0,1,โ€ฆ,MP_{k}=k,\ k=0,1,\ldots,M, where M=8M=8 and represents the number of subintervals. On each subinterval Ik=[Pkโˆ’1,Pk],k=1,โ€ฆ,MI_{k}=[P_{k-1},P_{k}],\ k=1,\ldots,M, we find the numerical solution by the following form

y1,k\displaystyle y_{1,k} =c0,k1T02+c1,k1T1(ฮพ)+c2,k1T2(ฮพ)+โ€ฆ+cnโˆ’1,k1Tnโˆ’1(ฮพ)\displaystyle=c_{0,k}^{1}\frac{T_{0}}{2}+c_{1,k}^{1}T_{1}(\xi)+c_{2,k}^{1}T_{2}(\xi)+\ldots+c_{n-1,k}^{1}T_{n-1}(\xi)
y2,k\displaystyle y_{2,k} =c0,k2T02+c1,k2T1(ฮพ)+c2,k2T2(ฮพ)+โ€ฆ+cnโˆ’1,k2Tnโˆ’1(ฮพ)\displaystyle=c_{0,k}^{2}\frac{T_{0}}{2}+c_{1,k}^{2}T_{1}(\xi)+c_{2,k}^{2}T_{2}(\xi)+\ldots+c_{n-1,k}^{2}T_{n-1}(\xi)

where Ti(ฮพ)=cosโก(iarccosโก(ฮพ))T_{i}(\xi)=\cos(i\arccos(\xi)) are Chebyshev polynomials of ii degree, i=0,โ€ฆ,nโˆ’1i=0,\ldots,n-1 (n=25n=25), and t=ฮฑฮพ+ฮฒt=\alpha\xi+\beta where ฮฑ=(Pkโˆ’Pkโˆ’1)/2\alpha=(P_{k}-P_{k-1})/2 and ฮฒ=(Pk+Pkโˆ’1)/2\beta=(P_{k}+P_{k-1})/2 (see [17], [18]).

For the efficiency estimation of this algorithm, the integral equation system is written in the form of delay differential system and we use the Matlab command dde23 (Matlab procedure which solves numerically delay differential equations, for details see Shampine [16]) to solve it and we compare the running times. We impose the relative error to 10โˆ’810^{-8} and the absolute error to 10โˆ’1210^{-12} to obtain a accuracy comparable with the step method. We display the graph of solutions.

Example 4.1.

Consider the following:

{xโ€ฒโ€ฒ(t)=eโˆ’2tx2(tโˆ’ฯ„)x(t),tโˆˆ[0,8],ฯ„=1x(t)=eโˆ’t,xโ€ฒ(t)=โˆ’eโˆ’t,tโˆˆ[โˆ’1,0].\left\{\begin{array}[]{l}x^{\prime\prime}(t)=e^{-2t}\frac{x^{2}(t-\tau)}{x(t)},\ t\in[0,8],\tau=1\\ x(t)=e^{-t},\ x^{\prime}(t)=-e^{-t},\ t\in[-1,0].\end{array}\right.

Exact solution: x(t)=eโˆ’tx(t)=e^{-t}.

For this example, the step method obtains the solution in 13771377 iterations with an error of 10โˆ’910^{-9} in 0.0618300.061830 CPU seconds. The Matlab program dde23 needs 0.7374480.737448 CPU seconds for a similar precision.

Refer to caption
Figure 1: The graphs of the exact and numerical solution for Example 4.1.
Example 4.2.

Consider the following:

{xโ€ฒโ€ฒ(t)=x2(tโˆ’ฯ„)โˆ’14(1+t)3โˆ’(1+t)+ฯ„,tโˆˆ[0,8],ฯ„=1x(t)=1+t,xโ€ฒ(t)=121+t,tโˆˆ[โˆ’1,0].\left\{\begin{array}[]{l}x^{\prime\prime}(t)=x^{2}(t-\tau)-\frac{1}{4\sqrt{(1+t)^{3}}}-(1+t)+\tau,\ t\in[0,8],\tau=1\\ x(t)=\sqrt{1+t},\ x^{\prime}(t)=\frac{1}{2\sqrt{1+t}},\ t\in[-1,0].\end{array}\right.

Exact solution: x(t)=1+tx(t)=\sqrt{1+t}.

In this case, the step method obtains the solution in 686686 iterations with an error of 10โˆ’810^{-8} in 0.0174370.017437 CPU seconds. The Matlab program dde23 needs 0.4906280.490628 CPU seconds for a similar precision.

Refer to caption
Figure 2: The graphs of the exact and numerical solution and absolute error evolution for Example 4.2.

5 Conclusions

In this paper we introduce a combination of a step method and a Chebyshev spectral method.

For the first example, the running time of the step method is 1111 times faster than Matlab dde23 procedure and for the second example is 2828 times faster than Matlab dde23 procedure for the similar precision. The above comparisons validate the step method from the accuracy and efficiency point of view.

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2018

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