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,† and Marcel-Adrian Şerban3
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 Perov’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.

footnotetext: the corresponding author. Email address: dotrocol@ictp.acad.ro 1 “T. Popoviciu” Institute of Numerical Analysis, Romanian Academy, Fântanele 57, 400110 Cluj-Napoca, Romania 2 Department of Mathematics, Technical University of Cluj-Napoca, G. Bariţiu 25, 400027 Cluj-Napoca, Romania 3 Department of Mathematics, “Babeş-Bolyai” University, M. Kogălniceanu 1, RO-400084 Cluj-Napoca, Romania

1 Introduction

We consider the system of delay differential equations

{x1(t)=f1(t,x1(t),x2(t),x1(th),x2(th)),t[a,b]x2(t)=f2(t,x1(t),x2(t),x1(th),x2(th))\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)=φ1(t),t[ah,a]x2(t)=φ2(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,f2C([a,b]×4,),φ1,φ2C([ah,a],)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 by𝐱=(x1,x2),𝐟=(f1,f2)\ \mathbf{x=(}x_{1},x_{2}\mathbf{),\ f}=(f_{1},f_{2}) and𝝋=(φ1,φ2).\ \boldsymbol{\varphi}=(\varphi_{1},\varphi_{2}).\ By a solution of the problem (1)-(2) we mean a function 𝐱C([ah,b],2)C1([a,b],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 Perov’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:XXA: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:=AAn,nA^{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:={xX|A(x)=x}F_{A}:=\left\{x\in X|\ A(x)=x\right\} the fixed point set of A.A.

Definition 1.1.

A:XXA:X\rightarrow X is called a Picard operator (briefly PO) if: FA={x}F_{A}=\{x^{\ast}\} and An(x)xA^{n}(x)\rightarrow x^{\ast} as nn\rightarrow\infty, for all xX.x\in X.

Definition 1.2.

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

Definition 1.3.

A matrix Q+2×2Q\in\mathbb{R}_{+}^{2\times 2} is called a matrix convergent to zero iff Qk0Q^{k}\rightarrow 0 as k.k\rightarrow\infty.

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

Theorem 1.4.

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

  • (i)

    QQ is a matrix convergent to zero;

  • (ii)

    Qkx0Q^{k}x\rightarrow 0 as k,x2;k\rightarrow\infty,\ \forall x\in\mathbb{R}^{2};

  • (iii)

    I2QI_{2}-Q is non-singular and (I2Q)1=I2+Q+Q2+;(I_{2}-Q)^{-1}=I_{2}+Q+Q^{2}+\ldots;

  • (iv)

    I2QI_{2}-Q is non-singular and (I2Q)1(I_{2}-Q)^{-1} has nonnegative elements;

  • (v)

    λ,det(QλI2)=0\lambda\in\mathbb{C},\ \det(Q-\lambda I_{2})=0 imply |λ|<1;\left|\lambda\right|<1;

  • (v)

    there exits at least one subordinate matrix norm such that Q<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 2.\mathbb{R}^{2}.

Definition 1.5.

[9] Let (X,d)(X,d) be a complete generalized metric space with d:X×X+2d:X\times X\rightarrow\mathbb{R}_{+}^{2} and A:XXA:X\rightarrow X. The operator AA is called a QQ-contraction if there exists a matrix Q+2×2Q\in\mathbb{R}_{+}^{2\times 2} such that:

  • (i)

    QQ is a matrix convergent to zero;

  • (ii)

    d(A(x),A(y))Qd(x,y),x,yXd(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:X×X+2d:X\times X\rightarrow\mathbb{R}_{+}^{2} and A:XXA:X\rightarrow X be a QQ-contraction. Then

  • (i)

    AA is a Picard operator, FA=FAn={x},nF_{A}=F_{A^{n}}=\{x^{\ast}\},\ \forall n\in\mathbb{N}^{\ast};

  • (ii)

    d(An(x),x)(I2Q)1Qnd(x,A(x)),xX.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,m¯i=\overline{0,m}, m1,m\geq 1, be some generalized metric spaces. Let Ai:X0××XiXiA_{i}:X_{0}\times\cdots\times X_{i}\rightarrow X_{i}, i=0,m¯i=\overline{0,m}, be some operators. We suppose that:

  • (i)

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

  • (ii)

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

  • (iii)

    there exist the matrices Qi+2×2Q_{i}\in\mathbb{R}_{+}^{2\times 2} which converge to zero, such that the operators 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 QiQ_{i}-generalized contractions, for all xiXi,i=1,m¯x^{i}\in X_{i},i=\overline{1,m};

  • (iv)

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

Then 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 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 :2+2\left\|\cdot\right\|:\mathbb{R}^{2}\rightarrow\mathbb{R}_{+}^{2} the vectorial norm

𝐮:=(|u1||u2|),𝐮=(u1,u2)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)

    𝐟C([a,b]×4,2),𝝋C([ah,a],2),h+,a,b,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 L+2×2L\in\mathbb{R}_{+}^{2\times 2} such that

    𝐟(t,𝐮1,𝐯1)𝐟(t,𝐮2,𝐯2)L(𝐮1𝐮2+𝐯1𝐯2),\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\|),

    t[a,b],𝐮1,𝐮2,𝐯1,𝐯22;t\in[a,b],\mathbf{u}^{1},\mathbf{u}^{2},\mathbf{v}^{1},\mathbf{v}^{2}\in\mathbb{R}^{2};

  • (H2{}_{2}^{\prime})

    there exists L+2×2L^{\prime}\in\mathbb{R}_{+}^{2\times 2} such that

    𝐟(t,𝐮1,𝐯)𝐟(t,𝐮2,𝐯)L(𝐮1𝐮2),\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\|),

    t[a,b],𝐮1,𝐮2,𝐯2.t\in[a,b],\mathbf{u}^{1},\mathbf{u}^{2},\mathbf{v}\in\mathbb{R}^{2}.

We consider the space X:=C([ah,b],2)X:=C([a-h,b],\mathbb{R}^{2}) endowed with the generalized norm B\left\|\cdot\right\|_{B} where 𝐱B:=(|x1|B|x2|B),𝐱=(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:=maxahtb(|xi(t)|eτ(ta+h)),τ>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,B)(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:XXA_{f}:X\rightarrow X , defined by

Af(𝐱)(t)={𝝋(t),t[ah,a]𝝋(a)+t0t𝐟(s,𝐱(s),𝐱(sh))𝑑s,t[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 mm\in\mathbb{N}^{\ast} be such that:

a+(m1)h<b and a+mhb.a+(m-1)h<b\text{ and }a+mh\geq b.

We denote by t1:=ah,t0:=a,ti:=a+ih,i=1,m1¯,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 𝐱C([t1,tm],2)C1([t0,tm],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 (𝐱n)n,(\mathbf{x}^{n})_{n\in\mathbb{N}^{\ast}}, defined by

    𝐱n+1(t):={𝝋(t),t[t1,t0]𝝋(t0)+t0t𝐟(s,𝐱n(s),𝐱n(sh))𝑑s,t[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 𝐱,𝐱0C([t1,tm],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

Af(𝐱)Af(𝐲)B1τL𝐱𝐲B,𝐱,𝐲X.\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 τ\tau sufficiently large such that AfA_{f} is QQ-contraction with Q:=1τLQ:=\frac{1}{\tau}L. So we can apply the Perov’s Theorem (Theorem 1.6) for Af:XXA_{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})

    𝐱0(t)=𝝋(t),t[t1,t0];\mathbf{x}^{0}(t)=\boldsymbol{\varphi}(t),t\in[t_{-1},t_{0}];

  • (p1)(p^{1})

    𝐱1(t)=𝝋(t0)+t0t𝐟(s,𝐱1(s),𝝋(sh))𝑑s,t[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})

    𝐱2(t)=𝐱1,(t1)+t1t𝐟(s,𝐱2(s),𝐱1,(sh))𝑑s,t[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}];

    \cdots

  • (pm1)(p^{m-1})

    𝐱m1(t)=𝐱m2,(tm2)+tm2t𝐟(s,𝐱m1(s),𝐱m2,(sh))𝑑s,t[tm2,tm1];\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})

    𝐱m(t)=𝐱m1,(tm1)+tm1t𝐟(s,𝐱m(s),𝐱m1,(sh))𝑑s,t[tm1,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 𝐱i,=(x1i,,x2i,)C([ti1,ti],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,m¯.(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 (H2)(H_{2}^{\prime}) hold. Then:

  • (i)

    the problem (1)-(2) has a unique solution 𝐱\mathbf{x}^{\ast} in C([t1,tm],2)C([t_{-1},t_{m}],\mathbb{R}^{2}), where

    𝐱(t)={𝝋(t),t[t1,t0]𝐱1,(t),t[t0,t1]𝐱m,(t),t[tm1,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 𝐱i,0=(x1i,0,x2i,0)C([ti1,ti],2),i=1,m¯\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:

    𝐱i,n+1(t)=𝐱i1,(ti1)+ti1t𝐟(s,𝐱i,n(s),𝐱i1,(sh))𝑑s,\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 t[ti1,ti],t\in[t_{i-1},t_{i}], (with 𝐱0,(t0):=𝝋(t0)\mathbf{x}^{0,\ast}(t_{0}):=\boldsymbol{\varphi}(t_{0})), converges and limn𝐱i,n=𝐱i,,i=1,m¯.\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 Perov’s theorem for each step [ti1,ti],i=1,m¯.[t_{i-1},t_{i}],\ i=\overline{1,m}.

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

1B:=maxt0tt1(𝐱(t)eτ(tt0)),τ>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:X1X1A_{1}:X_{1}\rightarrow X_{1} defined by

A1(𝐱)(t)=𝝋(t0)+t0t𝐟(s,𝐱(s),𝝋(sh))𝑑s.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 𝐱,𝐲X1\mathbf{x},\mathbf{y}\in X_{1}, we obtain

A1(𝐱)A1(𝐲)1B1τL𝐱𝐲1B.\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 τ\tau sufficiently large such that A1A_{1} is Q1:=1τLQ_{1}:=\frac{1}{\tau}L^{\prime}-contraction, therefore FA1:={𝐱1}.F_{A_{1}}:=\{\mathbf{x}_{1}^{\ast}\}.

For the next steps, we consider the Banach spaces Xi:=(C([ti1,ti],2),iB),i=2,m¯X_{i}:=(C([t_{i-1},t_{i}],\mathbb{R}^{2}),\left\|\cdot\right\|_{iB}),\ i=\overline{2,m}, where

𝐱iB:=maxti1tti(𝐱(t)eτ(tti1)),τ>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:XiXiA_{i}:X_{i}\rightarrow X_{i}, defined by

Ai(𝐱)(t)=𝐱i1,(ti1)+ti1t𝐟(s,𝐱i(s),𝐱i1,(sh))𝑑s.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 𝐱,𝐲Xi\mathbf{x},\mathbf{y}\in X_{i}, we obtain

Ai(𝐱)Ai(𝐲)iB1τL𝐱𝐲iB.\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 τ\tau\ sufficiently large such that AiA_{i} is Qi:=1τLQ_{i}:=\frac{1}{\tau}L^{\prime}-contraction, therefore FAi:={𝐱i,},i=2,m¯.F_{A_{i}}:=\{\mathbf{x}^{i,\ast}\},\ i=\overline{2,m}.

We have that 𝛗(t0)=𝐱1,(t0)\boldsymbol{\varphi}(t_{0})=\mathbf{x}^{1,\ast}(t_{0}) and from definition of Ai,i=2,m¯A_{i},i=\overline{2,m}, we obtain

𝐱i1,(ti1)=𝐱i,(ti1),i=2,m¯,\mathbf{x}^{i-1,\ast}(t_{i-1})=\mathbf{x}^{i,\ast}(t_{i-1}),\ i=\overline{2,m},

therefore

𝐱(t)={𝝋(t),t[t1,t0]𝐱1,(t),t[t0,t1]𝐱m,(t),t[tm1,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([t1,tm],2).C([t_{-1},t_{m}],\mathbb{R}^{2}).

Next, we will study if it is possible to replace 𝐱i,\mathbf{x}^{i,\ast} by the approximation 𝐱i,n,i=1,m¯\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 𝐱i,0C([ti1,ti],2),i=1,m¯,\mathbf{x}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m}, the sequences defined by:

𝐱1,n+1(t)\displaystyle\mathbf{x}^{1,n+1}(t) =𝝋(t0)+t0t𝐟(s,𝐱1,n(s),𝝋(sh))𝑑s, for t[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)
𝐱2,n+1(t)\displaystyle\mathbf{x}^{2,n+1}(t) =𝐱1,n(t1)+t1t𝐟(s,𝐱2,n(s),𝐱1,n(sh))𝑑s, for t[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}]
\displaystyle\cdots
𝐱m,n+1(t)\displaystyle\mathbf{x}^{m,n+1}(t) =𝐱m1,n(tm1)+tm1t𝐟(s,𝐱m,n(s),𝐱m1,n(sh))𝑑s, for t[tm1,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 limn𝐱i,n=𝐱i,,i=1,m¯.\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([t1,t0],2),0B)X_{0}:=(C([t_{-1},t_{0}],\mathbb{R}^{2}),\left\|\cdot\right\|_{0B}), where

0B:=maxt1tt0(𝐱(t)eτ(tt1)),τ>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([ti1,ti],2),iB),i=1,m¯X_{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:X0X0,A0(𝐱0)(t)=𝝋(t),t[t1,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:Xi1×XiXi,i=1,m¯\displaystyle A_{i}:X_{i-1}\times X_{i}\rightarrow X_{i},i=\overline{1,m}
Ai(𝐱i1,𝐱i)(t)=𝐱i1(ti1)+ti1t𝐟(s,𝐱i(s),𝐱i1(sh))𝑑s,t[ti1,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:X0×X1××XmX0×X1××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(𝐱0,𝐱1,,𝐱m)=(A0(𝐱0),A1(𝐱0,𝐱1),,Am(𝐱m1,𝐱m)).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 (𝐱0,𝐱1,,𝐱m)X0×X1××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 (𝐱0,n,𝐱1,n,,𝐱m,n)=An(𝐱0,𝐱1,,𝐱m).(\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:X0X0A_{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 𝐱0,=𝛗.\mathbf{x}^{0,\ast}=\boldsymbol{\varphi}. For i=1,m¯i=\overline{1,m}, we have the inequalities:

Ai(𝐱i1,𝐱i)Ai(𝐱i1,𝐲i)iB1τL𝐱i𝐲iiB\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 𝐱i1Xi1\mathbf{x}^{i-1}\in X_{i-1} and 𝐱i,𝐲iXi.\mathbf{x}^{i},\mathbf{y}^{i}\in X_{i}. For τ\tau sufficiently large we get that Ai(𝐱i1,):XiXiA_{i}(\mathbf{x}^{i-1},\cdot):X_{i}\rightarrow X_{i} are QiQ_{i}-contractions with Qi=1τLQ_{i}=\frac{1}{\tau}L^{\prime}, so we are in the conditions of Theorem 1.7, therefore AA is a Picard operator and FA={(𝐱0,,,𝐱m,)}F_{A}=\{(\mathbf{x}^{0,\ast},\ldots,\mathbf{x}^{m,\ast})\}, thus

(𝐱0,n,𝐱1,n,,𝐱m,n)=An(𝐱0,𝐱1,,𝐱m)(𝐱0,,,𝐱m,),(\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 𝐱0,n=𝛗\mathbf{x}^{0,n}=\boldsymbol{\varphi} and 𝐱1,n,,𝐱m,n\mathbf{x}^{1,n},\ldots,\mathbf{x}^{m,n} are defined by (4), for all nn\in\mathbb{N}. From the definitions of Ai,i=1,m¯A_{i},i=\overline{1,m}, we have

𝐱i1,(ti1)=𝐱i,(ti1),i=1,m¯\mathbf{x}^{i-1,\ast}(t_{i-1})=\mathbf{x}^{i,\ast}(t_{i-1}),\ i=\overline{1,m}

and therefore

𝐱(t)={𝝋(t),t[t1,t0]𝐱1,(t),t[t0,t1]𝐱m,(t),t[tm1,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([t1,tm],2)C([t_{-1},t_{m}],\mathbb{R}^{2}).

3 Application

We consider the following second order delay differential equation

x′′(t)=f(t,x(t),x(th)),t[a,b]-x^{\prime\prime}(t)=f(t,x(t),x(t-h)),\ t\in[a,b] (5)

with initial conditions

{x(t)=φ(t),t[ah,a]x(t)=φ(t),t[ah,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]××,h+,a,b,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, φ,φ:[ah,a].\varphi,\varphi^{\prime}:[a-h,a]\rightarrow\mathbb{R}.

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

(y1(t)y2(t))=(y2(t)f(t,y1(t),y1(th))),t[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))=(φ(t)φ(t)),t[ah,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 𝐲:=(y1y2)=(xx)\mathbf{y:}=\left(\begin{array}[]{c}y_{1}\\ y_{2}\end{array}\right)=\left(\begin{array}[]{c}x\\ x^{\prime}\end{array}\right), 𝐅C([a,b]×2,2)\mathbf{F}\in C([a,b]\times\mathbb{R}^{2},\mathbb{R}^{2}), 𝐅:=(f1f2)=(y2f)\mathbf{F:=}\left(\begin{array}[]{c}f_{1}\\ f_{2}\end{array}\right)=\left(\begin{array}[]{c}y_{2}\\ -f\end{array}\right), 𝝋C([ah,a],2)\boldsymbol{\varphi}\in C([a-h,a],\mathbb{R}^{2}), 𝝋:=(φ1φ2)=(φφ)\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)

    fC([a,b]×2,),φC1([ah,a],);f\in C([a,b]\times\mathbb{R}^{2},\mathbb{R}),\ \varphi\in C^{1}([a-h,a],\mathbb{R});

  • (C2)

    there exists lf+l_{f}\in\mathbb{R}_{+} such that

    |f(t,u1,v)f(t,u2,v)|lf|u1u2|,\left|f(t,u_{1},v)-f(t,u_{2},v)\right|\leq l_{f}\left|u_{1}-u_{2}\right|,

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

Let mm\in\mathbb{N}^{\ast} be such that:

a+(m1)h<b and a+mhb.a+(m-1)h<b\text{ and }a+mh\geq b.

We denote by t1:=aht_{-1}:=a-h, t0:=a\ t_{0}:=a, ti:=a+ih\ t_{i}:=a+ih, i=1,m1¯\ 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 𝐲\mathbf{y}^{\ast} in C([t1,tm],2)C([t_{-1},t_{m}],\mathbb{R}^{2}) where

    𝐲(t)={𝝋(t0),t[t1,t0]𝐲1,(t),t[t0,t1]𝐲m,(t),t[tm1,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 𝐲i,0C([ti1,ti],2),i=1,m1¯\mathbf{y}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m-1}, 𝐲m,0C([tm1,tm],2)\ \mathbf{y}^{m,0}\in C([t_{m-1},t_{m}],\mathbb{R}^{2}), the sequence defined by:

    𝐲i,n+1(t)=𝐲i1,(ti1)+ti1t𝐅(s,𝐲i,n(s),𝐲i1,(sh))𝑑s,\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 t[ti1,ti],t\in[t_{i-1},t_{i}], converges and limn𝐲i,n=𝐲i,,i=1,m¯.\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 𝐅C([a,b]×2,2),𝛗C([ah,a],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

𝐅(t,𝐮1,𝐯)𝐅(t,𝐮2,𝐯)\displaystyle\left\|\mathbf{F}(t,\mathbf{u}^{1},\mathbf{v})-\mathbf{F}(t,\mathbf{u}^{2},\mathbf{v})\right\| =(u21f(t,u11,v1))(u22f(t,u12,v1))\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\|
(01lf0)(|u11u12||u21u22|),\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 𝐮1=(u11,u21),𝐮2=(u12,u22),𝐯=(v1,v2)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 𝐲i,0C([ti1,ti],2),i=1,m¯,\mathbf{y}^{i,0}\in C([t_{i-1},t_{i}],\mathbb{R}^{2}),i=\overline{1,m},\ the sequences defined by:

𝐲1,n+1(t)\displaystyle\mathbf{y}^{1,n+1}(t) =𝝋(t0)+t0t𝐅(s,𝐲1,n(s),𝝋(sh))𝑑s, for t[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)
𝐲2,n+1(t)\displaystyle\mathbf{y}^{2,n+1}(t) =𝐲1,n(t1)+t1t𝐅(s,𝐲2,n(s),𝐲1,n(sh))𝑑s, for t[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}]
\displaystyle\cdots
𝐲m,n+1(t)\displaystyle\mathbf{y}^{m,n+1}(t) =𝐲m1,n(tm1)+tm1t𝐅(s,𝐲m,n(s),𝐲m1,n(sh))𝑑s, for t[tm1,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 limn𝐲i,n=𝐲i,,i=1,m¯.\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,,MP_{k}=k,\ k=0,1,\ldots,M, where M=8M=8 and represents the number of subintervals. On each subinterval Ik=[Pk1,Pk],k=1,,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(ξ)+c2,k1T2(ξ)++cn1,k1Tn1(ξ)\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(ξ)+c2,k2T2(ξ)++cn1,k2Tn1(ξ)\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(ξ)=cos(iarccos(ξ))T_{i}(\xi)=\cos(i\arccos(\xi)) are Chebyshev polynomials of ii degree, i=0,,n1i=0,\ldots,n-1 (n=25n=25), and t=αξ+βt=\alpha\xi+\beta where α=(PkPk1)/2\alpha=(P_{k}-P_{k-1})/2 and β=(Pk+Pk1)/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 10810^{-8} and the absolute error to 101210^{-12} to obtain a accuracy comparable with the step method. We display the graph of solutions.

Example 4.1.

Consider the following:

{x′′(t)=e2tx2(tτ)x(t),t[0,8],τ=1x(t)=et,x(t)=et,t[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)=etx(t)=e^{-t}.

For this example, the step method obtains the solution in 13771377 iterations with an error of 10910^{-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:

{x′′(t)=x2(tτ)14(1+t)3(1+t)+τ,t[0,8],τ=1x(t)=1+t,x(t)=121+t,t[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 10810^{-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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