Integro-Differential Equation with two time lags

Abstract

We consider an integro-differential equation with two time lags and we prove the existence, uniqueness and convergence of the sequence of the successive approximation by using contraction principle and step method with a weaker Lipschitz condition.

Also, we propose a new algorithm of successive approximation sequence generated by the step method and we give an example to illustrate the applications of the abstract results.

Authors

V. Ilea
Department of Mathematics, Babes-Bolyai University Cluj-Napoca, Romania

Diana Otrocol
Tiberiu Popoviciu Institutue of Numerical Analysis

Diana Otrocol

M.A. Serban
Department of Mathematics, Babes-Bolyai University Cluj-Napoca, Romania

D.Trif
Department of Mathematics, Babes-Bolyai University  Cluj-Napoca, Romania

Keywords

Integro-differential equation; two time lags, step method; Picard operators; fibre contraction principle.

Paper coordinates

V.Ilea, D. Otrocol, M.A. Serban, D. Trif, Integro-Differential Equation with two time lags, Fixed Point Theory, 13 (2012) no. 1, pp. 85-97.

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About this paper

Journal

Fixed Point Theory

Publisher Name

House of the Book of Science Cluj-Napoca

DOI
Print ISSN

1583-5022

Online ISSN

2066-9208

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[2] D. Guo, V. Lakshmikantham, X. Liu, Nonlinear Integral Equations in Abstract Spaces, Kluwer Acad. Publ., Dordrecht, 1996.
[3] V.A. Ilea, Functional Differential Equations of First Order with Advanced and Retarded Arguments, Cluj University Press, 2006, (in Romanian).
[4] V.A. Ilea, D. Otrocol, Integro-differential equation with two times modifications, Carpathian J. Math., 27(2011), No. 2, 209-216.
[5] V. Kolmanovskii, A. Mishkis, Applied Theory of Functional Differential Equations, Kluwer Acad. Publ., 1992.
[6] D. Otrocol, Lotka-Volterra Systems with Retarded Argument, Cluj University Press, 2007, (in Romanian).
[7] R. Precup, Positive solution of initial value problem for an integral equation modelling infectious diseases, Seminar of Fixed Point Theory, Cluj-Napoca, 1991, 25-30.
[8] R. Precup, E. Kirr, Analysis of nonlinear integral equation modelling infectious diseases, Proc. Conf. West. Univ. of Timi¸soara, 1997, 178-195.
[9] I.A. Rus, Abstract models of step method which imply the convergence of successive approximations, Fixed Point Theory, 9(2008), No. 1, 293-307.
[10] I.A. Rus, Picard operators and applications, Sci. Math. Jpn., 58(2003), No. 1, 191-219.
[11] I.A. Rus, Picard operators and applications, Seminar on Fixed Point Theory, Cluj-Napoca, 2(2001), 41-58.
[12] I.A. Rus, Some nonlinear functional differential and integral equations, via weakly Picard operator theory: a survey, Carpathian J. Math., 26(2010), No. 2, 230-258.
[13] I.A. Rus, M.A. S¸erban, D. Trif, Step method for some integral equations from biomathematics, Bull. Math. Soc. Sci. Math. Roumanie, 54(102)(2011), No. 2, 167-183.
[14] S. Sakata, T. Hara, Stability regions for linear differential equations with two kinds of time lags, Funkcialaj Ekvacioj, 47(2004), 129-144.
[15] N.L. Trefethen, An extension of Matlab to continuous functions and operators, SIAM J. Sci. Comput., 25(2004), No. 5, 1743-1770.
[16] D. Trif, LibScEig 1.0, > Mathematics > Differential Equations > LibScEig 1.0, http://www.mathworks.com/matlabcentral/fileexchange, 2005.

Paper (preprint) in HTML form

INTEGRO-DIFFERENTIAL EQUATION WITH TWO TIME LAGS

V. ILEA*, D. OTROCOL**, M.-A. ŞERBAN* AND D. TRIF*
* Department of Mathematics, Babeş-Bolyai University
Kogălniceanu Street No. 1, 400084 Cluj-Napoca, Romania
E-mails:{vdarzu,mserban,dtrif}@math.ubbcluj.ro
** Tiberiu Popoviciu Institute of Numerical Analysis of Romanian Academy
Cluj-Napoca, Romania
E-mail: dotrocol@ictp.acad.ro
Abstract

We consider an integro-differential equation with two time lags and we prove the existence, uniqueness and convergence of the sequence of the successive approximation by using contraction principle and step method with a weaker Lipschitz condition. Also, we propose a new algorithm of successive approximation sequence generated by the step method and we give an example to illustrate the applications of the abstract results.

Key Words and Phrases: Integro-differential equation, two time lags, step method, Picard operators, fibre contraction principle.
2010 Mathematics Subject Classification: 47H10, 47N20.

1. Introduction

This paper is concerned with the following integro-differential equation

x′​(t)=g​(t,x​(t),x​(t−τ))+∫t−htK​(s,x​(s))​𝑑s,t∈Sx^{\prime}(t)=g(t,x(t),x(t-\tau))+\int_{t-h}^{t}K(s,x(s))ds,t\in S (1.1)

where gg and KK are continuous functions on a Banach space and satisfy certain conditions to be specified later.

Regarding the earlier works on existence, uniqueness and convergence of the sequence of the successive approximation to integro-differential equations with delays and functional-differential equations with delays under different conditions, we refer to Guo et al [2], Kolmanowskii and Mishkis [5], Precup [7], Precup and Kirr [8], I.A. Rus [12] and the references therein. The related results for the existence and uniqueness, convergence of the sequence of the successive approximation, lower and upper solutions to the differential equations with delays can be found in Dobritioiu et al [1], Ilea [3] and Otrocol [6].

The authors Rus, Şerban, Trif [13] have considered the following integral equation

x​(t)=g​(t,x​(t−τ))+∫t−τtK​(t,s,x​(s))​𝑑s,t∈[a,b],τ>0\displaystyle x(t)=g(t,x(t-\tau))+\int_{t-\tau}^{t}K(t,s,x(s))ds,t\in[a,b],\tau>0
x​(t)=ϕ​(t),t∈[a−τ,a]\displaystyle x(t)=\phi(t),t\in[a-\tau,a]

in a Banach space and proved that the sequence of the successive approximation generated by the step method converges to the solution of this integral equation using the results of Rus [9].

Sakata and Hara [14] have considered the linear differential equation with two kinds of time lags

x′​(t)=a​x​(t−τ)+b​∫t−htx​(s)​𝑑sx^{\prime}(t)=ax(t-\tau)+b\int_{t-h}^{t}x(s)ds

where τ>0,h>0\tau>0,h>0 and a,ba,b are both real and they have studied the dependence on delays towards stability regions.

In the present work we use the ideas of Rus, Şerban, Trif [13] to establish the convergence of the sequence of successive approximation to equation (1.1). Regarding the two delays we have the following cases: h>0,τ>0,τ>hh>0,\tau>0,\tau>h and h>0,τ<0,|τ|>hh>0,\tau<0,|\tau|>h. Here, the authors study the first case, while the second case is studied in [4].

The aim of this paper is to obtain existence and uniqueness theorems using contraction principle and step method. Such kind of results have been proved in [13]. The approach proposed in the present paper is different to the ones in [13] and [1] and it is based on the different time lags. Also, we present here some lower and upper solution result, and a numerical example concerning equation (1.1).

We note that Sakata and Hara study in [14] the stability regions for similar integrodifferential equation with two time lags.

2. Preliminaries

Let τ>0,h>0,h<τ\tau>0,h>0,h<\tau and

x′​(t)=g​(t,x​(t),x​(t−τ))+∫t−htK​(s,x​(s))​𝑑s,t∈[0,T]\displaystyle x^{\prime}(t)=g(t,x(t),x(t-\tau))+\int_{t-h}^{t}K(s,x(s))ds,t\in[0,T] (2.1)
x​(t)=φ​(t),t∈[−τ,0]\displaystyle x(t)=\varphi(t),t\in[-\tau,0] (2.2)

Relative to (2.1)-(2.2) we consider the following conditions:
(C1)(𝔹,∥⋅∥)\left(C_{1}\right)(\mathbb{B},\|\cdot\|) is a Banach space, g∈C​([0,T]×𝔹2,𝔹),K∈C​([0,T]×𝔹,𝔹),φ∈C​([−τ,0],𝔹)g\in C\left([0,T]\times\mathbb{B}^{2},\mathbb{B}\right),K\in C([0,T]\times\mathbb{B},\mathbb{B}),\varphi\in C([-\tau,0],\mathbb{B});
(C2)\left(C_{2}\right) there exists Lg>0L_{g}>0 such that

‖g​(t,u1,v1)−g​(t,u2,v2)‖≤Lg​(‖u1−u2‖+‖v1−v2‖),ui,vi∈𝔹,t∈[0,T]\left\|g\left(t,u_{1},v_{1}\right)-g\left(t,u_{2},v_{2}\right)\right\|\leq L_{g}\left(\left\|u_{1}-u_{2}\right\|+\left\|v_{1}-v_{2}\right\|\right),u_{i},v_{i}\in\mathbb{B},t\in[0,T]

(C2′)\left(C_{2}^{\prime}\right) there exists Lg′>0L_{g}^{\prime}>0 such that

‖g​(t,u1,v)−g​(t,u2,v)‖≤Lg′​‖u1−u2‖,u1,u2,v∈𝔹,t∈[0,T]\left\|g\left(t,u_{1},v\right)-g\left(t,u_{2},v\right)\right\|\leq L_{g}^{\prime}\left\|u_{1}-u_{2}\right\|,u_{1},u_{2},v\in\mathbb{B},t\in[0,T]

(C3)\left(C_{3}\right) there exists LK>0L_{K}>0 such that

‖K​(t,u)−K​(t,v)‖≤LK​‖u−v‖,u,v∈𝔹,t∈[0,T]\|K(t,u)-K(t,v)\|\leq L_{K}\|u-v\|,u,v\in\mathbb{B},t\in[0,T]

(C4)​φ′​(0)=g​(0,φ​(0),φ​(−τ))+∫−h0K​(s,φ​(s))​𝑑s\left(C_{4}\right)\varphi^{\prime}(0)=g(0,\varphi(0),\varphi(-\tau))+\int_{-h}^{0}K(s,\varphi(s))ds.
We consider the space X=C​([−τ,T],𝔹)X=C([-\tau,T],\mathbb{B}) endowed with the norms ∥⋅∥∞\|\cdot\|_{\infty} and ∥⋅∥λ\|\cdot\|_{\lambda} where

‖x‖∞:=maxt∈[−τ,T]⁡{‖x​(t)‖},‖x‖λ:=supt∈[−τ,T]{‖x​(t)‖​e−λ​(t+τ)}\|x\|_{\infty}:=\max_{t\in[-\tau,T]}\{\|x(t)\|\},\quad\|x\|_{\lambda}:=\sup_{t\in[-\tau,T]}\left\{\|x(t)\|e^{-\lambda(t+\tau)}\right\}

The following relation between the Cebyshev and Bielecki norms holds

‖x‖∞≤‖x‖λ⋅eλ​(t+τ),∀t∈[−τ,T]\|x\|_{\infty}\leq\|x\|_{\lambda}\cdot e^{\lambda(t+\tau)},\forall t\in[-\tau,T]

The problem (2.1)-(2.2) is equivalent with the following fixed point problem:

x​(t)={φ​(t),t∈[−τ,0]φ​(0)+∫0tg​(ξ,x​(ξ),x​(ξ−τ))​𝑑ξ+∫0t∫ξ−hξK​(s,x​(s))​𝑑s​𝑑ξ,t∈[0,T]x(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ \varphi(0)+\int_{0}^{t}g(\xi,x(\xi),x(\xi-\tau))d\xi+\int_{0}^{t}\int_{\xi-h}^{\xi}K(s,x(s))dsd\xi,t\in[0,T]\end{array}\right.

3. Fibre weakly Picard operator

Let ( X,dX,d ) be a metric space and A:X→XA:X\rightarrow X an operator. In this paper we shall 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:=A∘An,n∈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. Also

P​(X):={Y⊆X∣Y≠∅},FA:={x∈X∣A​(x)=x}I​(A):={Y∈P​(X)∣A​(Y)⊆Y}\begin{gathered}P(X):=\{Y\subseteq X\mid Y\neq\emptyset\},F_{A}:=\{x\in X\mid A(x)=x\}\\ I(A):=\{Y\in P(X)\mid A(Y)\subseteq Y\}\end{gathered}

Definition 3.1. A:X→XA:X\rightarrow X is called a Picard operator (briefly PO) if:
(i) FA={x∗}F_{A}=\left\{x^{*}\right\};
(ii) An​(x)→x∗A^{n}(x)\rightarrow x^{*} as n→∞n\rightarrow\infty, for all x∈Xx\in X.

Definition 3.2. A:X→XA:X\rightarrow X is said to be a weakly Picard operator (briefly WPO) if the sequence (An​(x))n∈N\left(A^{n}(x)\right)_{n\in N} converges for all x∈Xx\in X and the limit (which may depend on xx ) is a fixed point of AA.

If A:X→XA:X\rightarrow X is a WPO, then we may define the operator A∞:X→XA^{\infty}:X\rightarrow X by

A∞​(x):=limn→∞An​(x)A^{\infty}(x):=\lim_{n\rightarrow\infty}A^{n}(x)

Obviously A∞​(X)=FAA^{\infty}(X)=F_{A}. Moreover, if AA is a PO and we denote by x∗x^{*} its unique fixed point, then A∞​(x)=x∗A^{\infty}(x)=x^{*}, for each x∈Xx\in X.

Lemma 3.3. Let (X,d,≤)(X,d,\leq) an ordered metric space and A,B,C:X→XA,B,C:X\rightarrow X be such that:
(i) the operator A,B,CA,B,C are WPOs;
(ii) A≤B≤CA\leq B\leq C;
(iii) the operator BB is increasing.

Then x≤y≤zx\leq y\leq z implies that A∞​(x)≤B∞​(y)≤C∞​(z)A^{\infty}(x)\leq B^{\infty}(y)\leq C^{\infty}(z).

Theorem 3.4. (Fibre contraction principle, Rus [10]) Let ( X,dX,d ) be a metric space and (Y,ρ)(Y,\rho) be a complete metric space. Let B:X→XB:X\rightarrow X and C:X×Y→YC:X\times Y\rightarrow Y be two operators. We suppose that:
(i) BB is a W​P​OWPO;
(ii) C​(x,⋅):Y→YC(x,\cdot):Y\rightarrow Y is α\alpha-contraction, for all x∈Xx\in X;
(iii) if (x∗,y∗)∈FA\left(x^{*},y^{*}\right)\in F_{A}, where A:X×Y→X×Y,A​(x,y)=(B​(x),C​(x,y))A:X\times Y\rightarrow X\times Y,A(x,y)=(B(x),C(x,y)), then C​(⋅,y∗)C\left(\cdot,y^{*}\right) is continuous in x∗x^{*}.
Then AA is a WPO. Moreover, if BB is PO then AA is PO.
By induction, from the above result we have:
Theorem 3.5. (Rus [11]) Let ( Xi,diX_{i},d_{i} ), i=0,m¯,m≥1i=\overline{0,m},m\geq 1, be some metric spaces. Let

Ai:X0×⋯×Xi→Xi,i=0,m¯A_{i}:X_{0}\times\cdots\times X_{i}\rightarrow X_{i},\quad i=\overline{0,m}

be some operator. We suppose that:
(i) (Xi,di),i=1,m¯\left(X_{i},d_{i}\right),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,…,xi−1,⋅):Xi→Xi,i=1,m¯A_{i}\left(x_{0},\ldots,x_{i-1},\cdot\right):X_{i}\rightarrow X_{i},i=\overline{1,m}

are αi\alpha_{i}-contractions;
(iv) the operator Ai,i=1,m¯A_{i},i=\overline{1,m}, are continuous.

Then the operator A:X0×⋯×Xm→X0×⋯×XmA: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\left(x_{0},\ldots,x_{m}\right)=\left(A_{0}\left(x_{0}\right),A_{1}\left(x_{0},x_{1}\right),\ldots,A_{m}\left(x_{0},\ldots,x_{m}\right)\right)

is W​P​OWPO. If A0A_{0} is PO, then AA is PO.

4. Existence and uniqueness

In this section we give an existence theorem for the solution of the problem (2.1)(2.2).

Theorem 4.1. In the condition (C1),(C2),(C3)\left(C_{1}\right),\left(C_{2}\right),\left(C_{3}\right) and (C4)\left(C_{4}\right), the problem (2.1)-(2.2) has in C​([−τ,T],𝔹)C([-\tau,T],\mathbb{B}) a unique solution x∗x^{*} and the sequence of successive approximation, (xn)n∈ℕ\left(x^{n}\right)_{n\in\mathbb{N}}

xn+1​(t)={φ​(t),t∈[−τ,0]φ​(0)+∫0tg​(ξ,xn​(ξ),xn​(ξ−τ))​𝑑ξ+∫0t∫ξ−hξK​(s,xn​(s))​𝑑s​𝑑ξt∈[0,T]x^{n+1}(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ \varphi(0)+\int_{0}^{t}g\left(\xi,x^{n}(\xi),x^{n}(\xi-\tau)\right)d\xi+\int_{0}^{t}\int_{\xi-h}^{\xi}K\left(s,x^{n}(s)\right)dsd\xi\\ t\in[0,T]\end{array}\right.

converges uniformly to x∗x^{*}, for every x0∈C​([−τ,T],𝔹)x^{0}\in C([-\tau,T],\mathbb{B}), with x0|[−τ,0]=φ\left.x^{0}\right|_{[-\tau,0]}=\varphi.
Proof. Let Xφ⊂X,Xφ={x∈X∣x​(t)=φ​(t),t∈[−τ,0]}X_{\varphi}\subset X,X_{\varphi}=\{x\in X\mid x(t)=\varphi(t),t\in[-\tau,0]\} and A:Xφ→XφA:X_{\varphi}\rightarrow X_{\varphi} defined by

A​(x)​(t)={φ​(t),t∈[−τ,0]φ​(0)+∫0tg​(ξ,x​(ξ),x​(ξ−τ))​𝑑ξ+∫0t∫ξ−hξK​(s,x​(s))​𝑑s​𝑑ξt∈[0,T]A(x)(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ \varphi(0)+\int_{0}^{t}g(\xi,x(\xi),x(\xi-\tau))d\xi+\int_{0}^{t}\int_{\xi-h}^{\xi}K(s,x(s))dsd\xi\\ \quad t\in[0,T]\end{array}\right.

Note that XφX_{\varphi} is a closed subset of XX, so (Xφ,d∥⋅∥λ)\left(X_{\varphi},d_{\|\cdot\|_{\lambda}}\right) is a complete metric space.

In a standard way we have

‖A​(x)−A​(y)‖λ≤1λ​(Lg+LK​h)​‖x−y‖λ, for all ​x,y∈Xφ\|A(x)-A(y)\|_{\lambda}\leq\frac{1}{\lambda}\left(L_{g}+L_{K}h\right)\|x-y\|_{\lambda},\text{ for all }x,y\in X_{\varphi}

which proves that AA is Lipschitz with LA=1λ​(Lg+LK​h)L_{A}=\frac{1}{\lambda}\left(L_{g}+L_{K}h\right). We can choose λ\lambda sufficiently large such that LA=1λ​(Lg+LK​h)<1L_{A}=\frac{1}{\lambda}\left(L_{g}+L_{K}h\right)<1, so AA is contraction. Applying the contraction principle we get the conclusion.

Remark 4.2. From the proof of Theorem 4.1, it follows that the operator AA is PO.

5. Step method

Using step method and contraction principle on each step for the problem (2.1)(2.2), in this section we obtain a better result by replacing the condition ( C​2C2 ) from Theorem 4.1 with ( C​2′C2^{\prime} ).

Let m∈ℕ∗m\in\mathbb{N}^{*} such that (m−1)​h≤T,m​h>T(m-1)h\leq T,mh>T. To simplify our presentation we suppose that h<τ≤2​hh<\tau\leq 2h. In the conditions (C1),(C2′),(C3)\left(C_{1}\right),\left(C_{2}^{\prime}\right),\left(C_{3}\right) and (C4)\left(C_{4}\right) the step method for (2.1)-(2.2) consist in the following:
(p0)x0​(t)=φ​(t),t∈[−τ,0]\left(p_{0}\right)\quad x_{0}(t)=\varphi(t),\quad t\in[-\tau,0]
(p1)x1(t)=φ(0)+∫0tg(ξ,x1(ξ),φ(ξ−τ))dξ+∫0t∫ξ−h0K(s,φ(s))dsdξ++∫0t∫0ξK(s,x1(s))dsdξ,t∈[0,h]\left(p_{1}\right)\quad x_{1}(t)=\varphi(0)+\int_{0}^{t}g\left(\xi,x_{1}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{0}^{t}\int_{\xi-h}^{0}K(s,\varphi(s))dsd\xi++\int_{0}^{t}\int_{0}^{\xi}K\left(s,x_{1}(s)\right)dsd\xi,t\in[0,h]
p2)x2(t)=x1∗(h)+∫hτg(ξ,x2(ξ),φ(ξ−τ))dξ+∫τtg(ξ,x2(ξ),x1∗(ξ−τ))dξ++∫ht∫ξ−hhK(s,x1∗(s))dsdξ+∫ht∫hξK(s,x2(s))dsdξ,t∈[h,2h]\left.p_{2}\right)\quad x_{2}(t)=x_{1}^{*}(h)+\int_{h}^{\tau}g\left(\xi,x_{2}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{\tau}^{t}g\left(\xi,x_{2}(\xi),x_{1}^{*}(\xi-\tau)\right)d\xi++\int_{h}^{t}\int_{\xi-h}^{h}K\left(s,x_{1}^{*}(s)\right)dsd\xi+\int_{h}^{t}\int_{h}^{\xi}K\left(s,x_{2}(s)\right)dsd\xi,t\in[h,2h]
(pi)xi(t)=xi−1∗((i−1)h)+∫(i−1)​h(i−2)​h+τg(ξ,xi(ξ),xi−2∗(ξ−τ))dξ++∫(i−2)​h+τtg(ξ,xi(ξ),xi−1∗(ξ−τ))dξ++∫(i−1)​ht∫ξ−h(i−1)​hK(s,xi−1∗(s))dsdξ++∫(i−1)​ht∫(i−1)​hξK(s,xi(s))dsdξ,t∈[(i−1)h,ih]\left(p_{i}\right)\quad x_{i}(t)=x_{i-1}^{*}((i-1)h)+\int_{(i-1)h}^{(i-2)h+\tau}g\left(\xi,x_{i}(\xi),x_{i-2}^{*}(\xi-\tau)\right)d\xi++\int_{(i-2)h+\tau}^{t}g\left(\xi,x_{i}(\xi),x_{i-1}^{*}(\xi-\tau)\right)d\xi++\int_{(i-1)h}^{t}\int_{\xi-h}^{(i-1)h}K\left(s,x_{i-1}^{*}(s)\right)dsd\xi++\int_{(i-1)h}^{t}\int_{(i-1)h}^{\xi}K\left(s,x_{i}(s)\right)dsd\xi,t\in[(i-1)h,ih]

(pm−1)xm−1​(t)=\displaystyle\left(p_{m-1}\right)\quad x_{m-1}(t)=\quad
xm−2∗​((m−2)​h)+∫(m−2)​h(m−3)​h+τg​(ξ,xm−1​(ξ),xm−3∗​(ξ−τ))​𝑑ξ+\displaystyle\quad x_{m-2}^{*}((m-2)h)+\int_{(m-2)h}^{(m-3)h+\tau}g\left(\xi,x_{m-1}(\xi),x_{m-3}^{*}(\xi-\tau)\right)d\xi+
+∫(m−3)​h+τtg​(ξ,xm−1​(ξ),xm−2∗​(ξ−τ))​𝑑ξ+\displaystyle\quad+\quad\int_{(m-3)h+\tau}^{t}g\left(\xi,x_{m-1}(\xi),x_{m-2}^{*}(\xi-\tau)\right)d\xi+
+∫(m−2)​ht∫ξ−h(m−2)​hK​(s,xm−2∗​(s))​𝑑s​𝑑ξ+\displaystyle\quad+\int_{(m-2)h}^{t}\int_{\xi-h}^{(m-2)h}K\left(s,x_{m-2}^{*}(s)\right)dsd\xi+
+∫(m−2)​ht∫(m−2)​hξK​(s,xm−1​(s))​𝑑s​𝑑ξ,t∈[(m−2)​h,(m−1)​h]\displaystyle\quad+\int_{(m-2)h}^{t}\int_{(m-2)h}^{\xi}K\left(s,x_{m-1}(s)\right)dsd\xi,t\in[(m-2)h,(m-1)h]
(pm)xm(t)=∗((m−1)​h)+∫m−1)h+τ(m−2)​hg​(ξ,xm​(ξ),xm−2∗​(ξ−τ))​𝑑ξ++∫(m−2)​h+τtg​(ξ,xm​(ξ),xm−1∗​(ξ−τ))​𝑑ξ++∫(m−1)​ht∫ξ−h−1)h(m−1)K​(s,xm−1∗​(s))​𝑑s​𝑑ξ++∫(m−1)​ht∫(m−1)​hξK​(s,xm​(s))​𝑑s​𝑑ξ,t∈[(m−1)​h,T]\displaystyle\left(p_{m}\right)\quad x_{m}(t)=\quad\begin{aligned} *&((m-1)h)+\int_{m-1)h+\tau}^{(m-2)h}g\left(\xi,x_{m}(\xi),x_{m-2}^{*}(\xi-\tau)\right)d\xi+\\ +&\int_{(m-2)h+\tau}^{t}g\left(\xi,x_{m}(\xi),x_{m-1}^{*}(\xi-\tau)\right)d\xi+\\ +&\int_{(m-1)h}^{t}\int_{\xi-h-1)h}^{(m-1)}K\left(s,x_{m-1}^{*}(s)\right)dsd\xi+\\ +&\int_{(m-1)h}^{t}\int_{(m-1)h}^{\xi}K\left(s,x_{m}(s)\right)dsd\xi,t\in[(m-1)h,T]\end{aligned}

where xi∗x_{i}^{*} is the unique solution of ( pip_{i} ), i=1,m¯i=\overline{1,m}.
So, we have the following result:
Theorem 5.1. In the conditions (C1),(C2′),(C3)\left(C_{1}\right),\left(C_{2}^{\prime}\right),\left(C_{3}\right) and (C4)\left(C_{4}\right) we have:
a) the problem (2.1)-(2.2) has in C​([−τ,T],𝔹)C([-\tau,T],\mathbb{B}) a unique solution x∗x^{*},

x∗​(t)={φ​(t),t∈[−τ,0]x1∗​(t),t∈[0,h]⋯xm∗​(t),t∈[(m−1)​h,T]x^{*}(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ x_{1}^{*}(t),t\in[0,h]\\ \cdots\\ x_{m}^{*}(t),t\in[(m-1)h,T]\end{array}\right.

b) for each xi0∈C​([(i−1)​h,i​h],𝔹),i=1,m−1¯x_{i}^{0}\in C([(i-1)h,ih],\mathbb{B}),i=\overline{1,m-1}, xm0∈C​([(m−1)​h,T],𝔹)x_{m}^{0}\in C([(m-1)h,T],\mathbb{B}), the sequence defined by:

x1n+1​(t)=φ​(0)+∫0tg​(ξ,x1n​(ξ),φ​(ξ−τ))​𝑑ξ+∫0t∫ξ−h0K​(s,φ​(s))​𝑑s​𝑑ξ+\displaystyle x_{1}^{n+1}(t)=\varphi(0)+\int_{0}^{t}g\left(\xi,x_{1}^{n}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{0}^{t}\int_{\xi-h}^{0}K(s,\varphi(s))dsd\xi+
+∫0t∫0ξK​(s,x1n​(s))​𝑑s​𝑑ξ,t∈[0,h]\displaystyle+\int_{0}^{t}\int_{0}^{\xi}K\left(s,x_{1}^{n}(s)\right)dsd\xi,t\in[0,h]
x2n+1​(t)=x1∗​(h)+∫hτg​(ξ,x2n​(ξ),φ​(ξ−τ))​𝑑ξ+∫τtg​(ξ,x2n​(ξ),x1∗​(ξ−τ))​𝑑ξ+\displaystyle x_{2}^{n+1}(t)=x_{1}^{*}(h)+\int_{h}^{\tau}g\left(\xi,x_{2}^{n}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{\tau}^{t}g\left(\xi,x_{2}^{n}(\xi),x_{1}^{*}(\xi-\tau)\right)d\xi+
+∫ht∫ξ−hhK​(s,x1∗​(s))​𝑑s​𝑑ξ+∫ht∫hξK​(s,x2n​(s))​𝑑s​𝑑ξ,t∈[h,2​h]\displaystyle+\int_{h}^{t}\int_{\xi-h}^{h}K\left(s,x_{1}^{*}(s)\right)dsd\xi+\int_{h}^{t}\int_{h}^{\xi}K\left(s,x_{2}^{n}(s)\right)dsd\xi,t\in[h,2h]
⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯​⋯\displaystyle\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots
xmn+1​(t)=xm−1∗​((m−1)​h)+∫(m−1)​h(m−2)​h+τg​(ξ,xmn​(ξ),xm−2∗​(ξ−τ))​𝑑ξ+\displaystyle x_{m}^{n+1}(t)=x_{m-1}^{*}((m-1)h)+\int_{(m-1)h}^{(m-2)h+\tau}g\left(\xi,x_{m}^{n}(\xi),x_{m-2}^{*}(\xi-\tau)\right)d\xi+
+∫(m−2)​h+τtg​(ξ,xmn​(ξ),xm−1∗​(ξ−τ))​𝑑ξ+\displaystyle+\int_{(m-2)h+\tau}^{t}g\left(\xi,x_{m}^{n}(\xi),x_{m-1}^{*}(\xi-\tau)\right)d\xi+
+∫(m−1)​ht∫ξ−h(m−1)​hK​(s,xm−1∗​(s))​𝑑s​𝑑ξ+\displaystyle+\int_{(m-1)h}^{t}\int_{\xi-h}^{(m-1)h}K\left(s,x_{m-1}^{*}(s)\right)dsd\xi+
+∫(m−1)​ht∫(m−1)​hξK​(s,xmn​(s))​𝑑s​𝑑ξ,t∈[(m−1)​h,T]\displaystyle+\int_{(m-1)h}^{t}\int_{(m-1)h}^{\xi}K\left(s,x_{m}^{n}(s)\right)dsd\xi,t\in[(m-1)h,T]
converge and ​limn→∞nxin=xi∗,i=1,m¯.\displaystyle\text{ converge and }\lim_{n\rightarrow\infty}^{n}x_{i}^{n}=x_{i}^{*},i=\overline{1,m}.

Proof. In order to proof this theorem we apply the contraction principle for each step: [(i−1)​h,i​h],[(m−1)​h,T][(i-1)h,ih],[(m-1)h,T], where i=1,m−1¯i=\overline{1,m-1}.

For the first step we consider the Banach space X1:=(C([0,h],𝔹),∥⋅∥λ1)X_{1}:=\left(C([0,h],\mathbb{B}),\|\cdot\|_{\lambda_{1}}\right), where ‖x‖λ1=maxt∈[0,h]⁡{‖x​(t)‖​e−λ1​t}\|x\|_{\lambda_{1}}=\max_{t\in[0,h]}\left\{\|x(t)\|e^{-\lambda_{1}t}\right\} and the operator A1:X1→X1A_{1}:X_{1}\rightarrow X_{1} defined by

A1​(x)​(t)\displaystyle A_{1}(x)(t) =φ​(0)+∫0tg​(ξ,x​(ξ),φ​(ξ−τ))​𝑑ξ+\displaystyle=\varphi(0)+\int_{0}^{t}g(\xi,x(\xi),\varphi(\xi-\tau))d\xi+
+∫0t∫ξ−h0K​(s,φ​(s))​𝑑s​𝑑ξ+∫0t∫0ξK​(s,x​(s))​𝑑s​𝑑ξ\displaystyle+\int_{0}^{t}\int_{\xi-h}^{0}K(s,\varphi(s))dsd\xi+\int_{0}^{t}\int_{0}^{\xi}K(s,x(s))dsd\xi

For x,y∈X1x,y\in X_{1}, we have

‖A1​(x)−A1​(y)‖λ1≤1λ1​(Lg′+LK​h)​‖x−y‖λ1\left\|A_{1}(x)-A_{1}(y)\right\|_{\lambda_{1}}\leq\frac{1}{\lambda_{1}}\left(L_{g}^{\prime}+L_{K}h\right)\|x-y\|_{\lambda_{1}}

We can choose a λ1>0\lambda_{1}>0 such that 1λ1​(Lg′+LK​h)<1\frac{1}{\lambda_{1}}\left(L_{g}^{\prime}+L_{K}h\right)<1, so A1A_{1} is a contraction, therefore FA1={x1∗}F_{A_{1}}=\left\{x_{1}^{*}\right\}.

For the next steps let us consider the following Banach spaces: for i=2,m−1¯i=\overline{2,m-1} given by

Xi:=(C([(i−1)h,ih];𝔹),∥⋅∥λi), with ∥x∥λi:=maxt∈[(i−1)​h,i​h]{∥x(t)∥e−λi​(t−(i−1)​h)}X_{i}:=\left(C([(i-1)h,ih];\mathbb{B}),\|\cdot\|_{\lambda_{i}}\right),\text{ with }\|x\|_{\lambda_{i}}:=\max_{t\in[(i-1)h,ih]}\left\{\|x(t)\|e^{-\lambda_{i}(t-(i-1)h)}\right\}

and
Xm:=(C([(m−1)h,T];𝔹),∥⋅∥λm)X_{m}:=\left(C([(m-1)h,T];\mathbb{B}),\|\cdot\|_{\lambda_{m}}\right), with ‖x‖λm:=maxt∈[(m−1)​h,T]⁡{‖x​(t)‖​e−λm​(t−(m−1)​h)}\|x\|_{\lambda_{m}}:=\max_{t\in[(m-1)h,T]}\left\{\|x(t)\|e^{-\lambda_{m}(t-(m-1)h)}\right\}
and the operators Ai:Xi→Xi,i=2,m¯A_{i}:X_{i}\rightarrow X_{i},i=\overline{2,m} defined by

Ai​(x)​(t):=xi−1∗​((i−1)​h)+∫(i−1)​h(i−2)​h+τg​(ξ,x​(ξ),xi−2∗​(ξ−τ))​𝑑ξ+\displaystyle A_{i}(x)(t)=x_{i-1}^{*}((i-1)h)+\int_{(i-1)h}^{(i-2)h+\tau}g\left(\xi,x(\xi),x_{i-2}^{*}(\xi-\tau)\right)d\xi+
+∫(i−2)​h+τtg​(ξ,x​(ξ),xi−1∗​(ξ−τ))​𝑑ξ+∫(i−1)​ht∫ξ−h(i−1)​hK​(s,xi−1∗​(s))​𝑑s​𝑑ξ+\displaystyle+\int_{(i-2)h+\tau}^{t}g\left(\xi,x(\xi),x_{i-1}^{*}(\xi-\tau)\right)d\xi+\int_{(i-1)h}^{t}\int_{\xi-h}^{(i-1)h}K\left(s,x_{i-1}^{*}(s)\right)dsd\xi+
+∫(i−1)​ht∫(i−1)​hξK​(s,x​(s))​𝑑s​𝑑ξ\displaystyle+\int_{(i-1)h}^{t}\int_{(i-1)h}^{\xi}K(s,x(s))dsd\xi

For x,y∈Xix,y\in X_{i} we have ‖Ai​(x)−Ai​(y)‖λi≤1λi​(Lg′+LK​h)​‖x−y‖λi\left\|A_{i}(x)-A_{i}(y)\right\|_{\lambda_{i}}\leq\frac{1}{\lambda_{i}}\left(L_{g}^{\prime}+L_{K}h\right)\|x-y\|_{\lambda_{i}}, so AiA_{i} is a contraction for a suitable choice of λi\lambda_{i} such that 1λi​(Lg′+LK​h)<1\frac{1}{\lambda_{i}}\left(L_{g}^{\prime}+L_{K}h\right)<1. Therefore, we get that FAi={xi∗},i=2,m¯F_{A_{i}}=\left\{x_{i}^{*}\right\},i=\overline{2,m}.

From condition ( C4C_{4} ) we get φ​(0)=x1∗​(0)\varphi(0)=x_{1}^{*}(0) and from definition of Ai,i=1,m¯A_{i},i=\overline{1,m}, we have

xi−1∗​((i−1)​h)=xi∗​((i−1)​h),i=1,m¯x_{i-1}^{*}((i-1)h)=x_{i}^{*}((i-1)h),i=\overline{1,m}

therefore

x∗​(t)={φ​(t),t∈[−τ,0]x1∗​(t),t∈[0,h]⋯xm∗​(t),t∈[(m−1)​h,T]x^{*}(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ x_{1}^{*}(t),t\in[0,h]\\ \cdots\\ x_{m}^{*}(t),t\in[(m-1)h,T]\end{array}\right.

is the unique solution in C​([−h,T],𝔹)C([-h,T],\mathbb{B}).
Now the question is: Can we put an approximation of xin,i=1,m¯x_{i}^{n},i=\overline{1,m} instead of xi∗x_{i}^{*}, i=1,m¯i=\overline{1,m} ?

The answer of this question is given by the following theorem:
Theorem 5.2. In the condition of Theorem 5.1, for each xi0∈C​([(i−1)​h,i​h],𝔹),i=1,m−1¯,xm0∈C​([(m−1)​h,T],𝔹)x_{i}^{0}\in C([(i-1)h,ih],\mathbb{B}),i=\overline{1,m-1},x_{m}^{0}\in C([(m-1)h,T],\mathbb{B}), the sequences defined by:

x1n+1​(t)=\displaystyle x_{1}^{n+1}(t)= φ​(0)+∫0tg​(ξ,x1n​(ξ),φ​(ξ−τ))​𝑑ξ+∫0t∫ξ−h0K​(s,φ​(s))​𝑑s​𝑑ξ+\displaystyle\varphi(0)+\int_{0}^{t}g\left(\xi,x_{1}^{n}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{0}^{t}\int_{\xi-h}^{0}K(s,\varphi(s))dsd\xi+
+∫0t∫0ξK​(s,x1n​(s))​𝑑s​𝑑ξ,t∈[0,h]\displaystyle+\int_{0}^{t}\int_{0}^{\xi}K\left(s,x_{1}^{n}(s)\right)dsd\xi,t\in[0,h]
x2n+1​(t)=\displaystyle x_{2}^{n+1}(t)= x1n​(h)+∫hτg​(ξ,x2n​(ξ),φ​(ξ−τ))​𝑑ξ+∫τtg​(ξ,x2n​(ξ),x1n​(ξ−τ))​𝑑ξ+\displaystyle x_{1}^{n}(h)+\int_{h}^{\tau}g\left(\xi,x_{2}^{n}(\xi),\varphi(\xi-\tau)\right)d\xi+\int_{\tau}^{t}g\left(\xi,x_{2}^{n}(\xi),x_{1}^{n}(\xi-\tau)\right)d\xi+
+∫ht∫ξ−hhK​(s,x1n​(s))​𝑑s​𝑑ξ+∫ht∫hξK​(s,x2n​(s))​𝑑s​𝑑ξ,t∈[h,2​h]\displaystyle+\int_{h}^{t}\int_{\xi-h}^{h}K\left(s,x_{1}^{n}(s)\right)dsd\xi+\int_{h}^{t}\int_{h}^{\xi}K\left(s,x_{2}^{n}(s)\right)dsd\xi,t\in[h,2h]
xmn+1​(t)=\displaystyle x_{m}^{n+1}(t)= xm−1n​((m−1)​h)+∫(m−1)​h(m−2)​h+τg​(ξ,xmn​(ξ),xm−2n​(ξ−τ))​𝑑ξ+\displaystyle x_{m-1}^{n}((m-1)h)+\int_{(m-1)h}^{(m-2)h+\tau}g\left(\xi,x_{m}^{n}(\xi),x_{m-2}^{n}(\xi-\tau)\right)d\xi+
+∫(m−2)​h+τtg​(ξ,xmn​(ξ),xm−1n​(ξ−τ))​𝑑ξ+\displaystyle+\int_{(m-2)h+\tau}^{t}g\left(\xi,x_{m}^{n}(\xi),x_{m-1}^{n}(\xi-\tau)\right)d\xi+ (5.1)
+∫(m−1)​ht∫ξ−h(m−1)​hK​(s,xm−1n​(s))​𝑑s​𝑑ξ+\displaystyle+\int_{(m-1)h}^{t}\int_{\xi-h}^{(m-1)h}K\left(s,x_{m-1}^{n}(s)\right)dsd\xi+
+∫(m−1)​ht∫(m−1)​hξK​(s,xmn​(s))​𝑑s​𝑑ξ,t∈[(m−1)​h,T]\displaystyle+\int_{(m-1)h}^{t}\int_{(m-1)h}^{\xi}K\left(s,x_{m}^{n}(s)\right)dsd\xi,t\in[(m-1)h,T]

converge and limn→∞xin=xi∗,i=1,m¯\lim_{n\rightarrow\infty}x_{i}^{n}=x_{i}^{*},i=\overline{1,m}.

Proof. We consider the following Banach spaces (with λ>0\lambda>0 ):

X0\displaystyle X_{0} =(C([−τ,0],𝔹),∥⋅∥λ0),∥⋅∥λ0\displaystyle=\left(C([-\tau,0],\mathbb{B}),\|\cdot\|_{\lambda_{0}}\right),\|\cdot\|_{\lambda_{0}}
=maxt∈[−τ,0]⁡{‖x​(t)‖​e−λ0​(t+τ)}\displaystyle=\max_{t\in[-\tau,0]}\left\{\|x(t)\|e^{-\lambda_{0}(t+\tau)}\right\}
Xi\displaystyle X_{i} =(C([(i−1)h,ih],𝔹),∥⋅∥λi),∥⋅∥λi\displaystyle=\left(C([(i-1)h,ih],\mathbb{B}),\|\cdot\|_{\lambda_{i}}\right),\|\cdot\|_{\lambda_{i}}
=maxt∈[(i−1)​h,i​h]⁡{‖x​(t)‖​e−λi​(t−(i−1)​h)},i=1,m−1¯,\displaystyle=\max_{t\in[(i-1)h,ih]}\left\{\|x(t)\|e^{-\lambda_{i}(t-(i-1)h)}\right\},i=\overline{1,m-1},
Xm\displaystyle X_{m} =(C([(m−1)h,T],𝔹),∥⋅∥λm),∥⋅∥λm\displaystyle=\left(C([(m-1)h,T],\mathbb{B}),\|\cdot\|_{\lambda_{m}}\right),\|\cdot\|_{\lambda_{m}}
=maxt∈[(m−1)​h,T]⁡{‖x​(t)‖​e−λm​(t−(m−1)​h)},\displaystyle=\max_{t\in[(m-1)h,T]}\left\{\|x(t)\|e^{-\lambda_{m}(t-(m-1)h)}\right\},

and the operators:

A0:\displaystyle A_{0}: X0→X0,A0​(x0)​(t)=φ​(t),t∈[−τ,0]\displaystyle X_{0}\rightarrow X_{0},A_{0}\left(x_{0}\right)(t)=\varphi(t),t\in[-\tau,0]
A1:X0×X1→X1\displaystyle A_{1}:X_{0}\times X_{1}\rightarrow X_{1}
A1​(x0,x1)​(t)=φ​(0)+\displaystyle A_{1}\left(x_{0},x_{1}\right)(t)=\varphi(0)+ ∫0tg​(ξ,x1​(ξ),x0​(ξ−τ))​𝑑ξ+∫0t∫ξ−h0K​(s,x0​(s))​𝑑s​𝑑ξ+\displaystyle\int_{0}^{t}g\left(\xi,x_{1}(\xi),x_{0}(\xi-\tau)\right)d\xi+\int_{0}^{t}\int_{\xi-h}^{0}K\left(s,x_{0}(s)\right)dsd\xi+
+∫0t∫0ξK​(s,x1​(s))​𝑑s​𝑑ξ,t∈[0,h]\displaystyle+\int_{0}^{t}\int_{0}^{\xi}K\left(s,x_{1}(s)\right)dsd\xi,t\in[0,h]
Ai:\displaystyle A_{i}: Xi−2×Xi−1×Xi→Xi,i=2,m−1¯\displaystyle X_{i-2}\times X_{i-1}\times X_{i}\rightarrow X_{i},i=\overline{2,m-1}
Ai​(xi−2,xi−1,xi)​(t)=\displaystyle A_{i}\left(x_{i-2},x_{i-1},x_{i}\right)(t)= xi−1​((i−1)​h)+∫(i−1)​h(i−2)​h+τg​(ξ,xi​(ξ),xi−2​(ξ−τ))​𝑑ξ+\displaystyle x_{i-1}((i-1)h)+\int_{(i-1)h}^{(i-2)h+\tau}g\left(\xi,x_{i}(\xi),x_{i-2}(\xi-\tau)\right)d\xi+
+\displaystyle+ ∫(i−2)​h+τtg​(ξ,xi​(ξ),xi−1​(ξ−τ))​𝑑ξ+\displaystyle\int_{(i-2)h+\tau}^{t}g\left(\xi,x_{i}(\xi),x_{i-1}(\xi-\tau)\right)d\xi+
+\displaystyle+ ∫(i−1)​ht∫ξ−h(i−1)​hK​(s,xi−1​(s))​𝑑s​𝑑ξ+\displaystyle\int_{(i-1)h}^{t}\int_{\xi-h}^{(i-1)h}K\left(s,x_{i-1}(s)\right)dsd\xi+
+\displaystyle+ ∫(i−1)​ht∫(i−1)​hξK​(s,xi​(s))​𝑑s​𝑑ξ,t∈[(i−1)​h,i​h]\displaystyle\int_{(i-1)h}^{t}\int_{(i-1)h}^{\xi}K\left(s,x_{i}(s)\right)dsd\xi,t\in[(i-1)h,ih]
Am:Xm−2×Xm−1×Xm→Xm\displaystyle A_{m}:X_{m-2}\times X_{m-1}\times X_{m}\rightarrow X_{m}
Am​(xm−2,xm−1,xm)​(t)=\displaystyle A_{m}\left(x_{m-2},x_{m-1},x_{m}\right)(t)= xm−1​((m−1)​h)+∫(m−1)​h(m−2)​h+τg​(ξ,xm​(ξ),xm−2​(ξ−τ))​𝑑ξ+\displaystyle x_{m-1}((m-1)h)+\int_{(m-1)h}^{(m-2)h+\tau}g\left(\xi,x_{m}(\xi),x_{m-2}(\xi-\tau)\right)d\xi+
+\displaystyle+ ∫(m−2)​h+τtg​(ξ,xm​(ξ),xm−1​(ξ−τ))​𝑑ξ+\displaystyle\int_{(m-2)h+\tau}^{t}g\left(\xi,x_{m}(\xi),x_{m-1}(\xi-\tau)\right)d\xi+
+\displaystyle+ ∫(m−1)​ht∫ξ−h(m−1)​hK​(s,xm−1​(s))​𝑑s​𝑑ξ+\displaystyle\int_{(m-1)h}^{t}\int_{\xi-h}^{(m-1)h}K\left(s,x_{m-1}(s)\right)dsd\xi+
+\displaystyle+ ∫(m−1)​ht∫(m−1)​hξK​(s,xm​(s))​𝑑s​𝑑ξ,t∈[(m−1)​h,T]\displaystyle\int_{(m-1)h}^{t}\int_{(m-1)h}^{\xi}K\left(s,x_{m}(s)\right)dsd\xi,t\in[(m-1)h,T]

and

A:X0×…×Xm→X0×…×XmA​(x0,…,xm)=(A0​(x0),A1​(x0,x1),A2​(x0,x1,x2),…,Am​(xm−2,xm−1,xm)).\begin{aligned} A&:X_{0}\times\ldots\times X_{m}\rightarrow X_{0}\times\ldots\times X_{m}\\ A\left(x_{0},\ldots,x_{m}\right)&=\left(A_{0}\left(x_{0}\right),A_{1}\left(x_{0},x_{1}\right),A_{2}\left(x_{0},x_{1},x_{2}\right),\ldots,A_{m}\left(x_{m-2},x_{m-1},x_{m}\right)\right)\end{aligned}.

It is easy to see that for fixed (x0,…,xm)∈X0×…×Xm\left(x_{0},\ldots,x_{m}\right)\in X_{0}\times\ldots\times X_{m} the sequence defined by (5.1) means

(x0n,…,xmn)=An​(x0,…,xm)\left(x_{0}^{n},\ldots,x_{m}^{n}\right)=A^{n}\left(x_{0},\ldots,x_{m}\right)

To prove the conclusion we need to prove that the operator AA is PO and for this we apply Theorem 3.5.

Since A0:X0→X0A_{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}}=\left\{x_{0}^{*}\right\}, where x0∗=φx_{0}^{*}=\varphi. We have the inequalities:

‖A1​(x0,x1)−A1​(x0,y1)‖λ1≤1λ1​(Lg′+LK​h)​‖x1−y1‖λ1\left\|A_{1}\left(x_{0},x_{1}\right)-A_{1}\left(x_{0},y_{1}\right)\right\|_{\lambda_{1}}\leq\frac{1}{\lambda_{1}}\left(L_{g}^{\prime}+L_{K}h\right)\left\|x_{1}-y_{1}\right\|_{\lambda_{1}}

for all x0∈X0,x1,y1∈X1x_{0}\in X_{0},x_{1},y_{1}\in X_{1}, and

‖Ai​(xi−2,xi−1,xi)−Ai​(xi−2,xi−1,yi)‖λi≤1λi​(Lg′+LK​h)​‖xi−yi‖λi\left\|A_{i}\left(x_{i-2},x_{i-1},x_{i}\right)-A_{i}\left(x_{i-2},x_{i-1},y_{i}\right)\right\|_{\lambda_{i}}\leq\frac{1}{\lambda_{i}}\left(L_{g}^{\prime}+L_{K}h\right)\left\|x_{i}-y_{i}\right\|_{\lambda_{i}}

for all xi−2∈Xi−2,xi−1∈Xi−1,xi,yi∈Xi,i=2,m¯x_{i-2}\in X_{i-2},x_{i-1}\in X_{i-1},x_{i},y_{i}\in X_{i},i=\overline{2,m}. For λi\lambda_{i} sufficiently large, (λi>Lg′+LK​h)\left(\lambda_{i}>\right.\left.L_{g}^{\prime}+L_{K}h\right), we get that A1​(x0,⋅):X1→X1A_{1}\left(x_{0},\cdot\right):X_{1}\rightarrow X_{1} is α1\alpha_{1}-contraction and Ai​(xi−2,xi−1,⋅)A_{i}\left(x_{i-2},x_{i-1},\cdot\right) : Xi→XiX_{i}\rightarrow X_{i} are αi\alpha_{i}-contractions with αi=1λ​(Lg′+LK​h),i=1,m¯\alpha_{i}=\frac{1}{\lambda}\left(L_{g}^{\prime}+L_{K}h\right),i=\overline{1,m}, so we are in the conditions of Theorem 3.5, therefore AA is PO and FA={(x0∗,…,xm∗)}F_{A}=\left\{\left(x_{0}^{*},\ldots,x_{m}^{*}\right)\right\}, thus

(x0n,…,xmn)=An​(x0,…,xm)→(x0∗,…,xm∗)\left(x_{0}^{n},\ldots,x_{m}^{n}\right)=A^{n}\left(x_{0},\ldots,x_{m}\right)\rightarrow\left(x_{0}^{*},\ldots,x_{m}^{*}\right)

with x0n=φx_{0}^{n}=\varphi and x1n,…,xmnx_{1}^{n},\ldots,x_{m}^{n}, for all n∈ℕn\in\mathbb{N}, are defined by (5.1). From condition (C4)\left(C_{4}\right) and from the definitions of Ai,i=1,m¯A_{i},i=\overline{1,m}, we have

xi−1∗​((i−1)​h)=xi∗​((i−1)​h),i=1,m¯x_{i-1}^{*}((i-1)h)=x_{i}^{*}((i-1)h),i=\overline{1,m}

therefore

x∗​(t)={φ​(t),t∈[−τ,0]x1∗​(t),t∈[0,h]⋯xm∗​(t),t∈[(m−1)​h,T]x^{*}(t)=\left\{\begin{array}[]{l}\varphi(t),t\in[-\tau,0]\\ x_{1}^{*}(t),t\in[0,h]\\ \cdots\\ x_{m}^{*}(t),t\in[(m-1)h,T]\end{array}\right.

is the unique solution in C​([−τ,T],𝔹)C([-\tau,T],\mathbb{B}).

6. Lower solutions, upper solutions and the solution

In this section we shall prove that the solution of the equation (1.1) is an upper bound of the lower solutions set and a lower bound of the upper solutions set.

Let the integro-differential equation (1.1) under the conditions (C1),(C2),(C3)\left(C_{1}\right),\left(C_{2}\right),\left(C_{3}\right), (C4)\left(C_{4}\right) and we denote by xA∗∈(C​[0,T],𝔹)x_{A}^{*}\in(C[0,T],\mathbb{B}) the unique fixed point of the operator AA. In addition, we suppose that:
(C5)​g​(t,⋅,⋅):𝔹2→𝔹\left(C_{5}\right)g(t,\cdot,\cdot):\mathbb{B}^{2}\rightarrow\mathbb{B} is increasing, for every t∈[0,T];t\in[0,T];
(C6)​K​(t,⋅):𝔹→𝔹\left(C_{6}\right)K(t,\cdot):\mathbb{B}\rightarrow\mathbb{B} is increasing, for every t∈[0,T]t\in[0,T].
We have

Theorem 6.1. We suppose that the conditions (C1)−(C6)\left(C_{1}\right)-\left(C_{6}\right) are satisfied. The following implications hold:
(a) If x​(t)≤g​(t,x​(t),x​(t−τ))+∫t−htK​(s,x​(s))​𝑑s,x∈𝔹x(t)\leq g(t,x(t),x(t-\tau))+\int_{t-h}^{t}K(s,x(s))ds,x\in\mathbb{B} then x≤xA∗x\leq x_{A}^{*}.
(b) If x​(t)≥g​(t,x​(t),x​(t−τ))+∫t−htK​(s,x​(s))​𝑑s,x∈𝔹x(t)\geq g(t,x(t),x(t-\tau))+\int_{t-h}^{t}K(s,x(s))ds,x\in\mathbb{B} then x≥xA∗x\geq x_{A}^{*}.

Proof. (a) We consider the operator AA defined by

A​(x)​(t)=∫0tg​(ξ,x​(ξ),x​(ξ−τ))​𝑑ξ+∫0t∫ξ−hξK​(s,x​(s))​𝑑s​𝑑ξA(x)(t)=\int_{0}^{t}g(\xi,x(\xi),x(\xi-\tau))d\xi+\int_{0}^{t}\int_{\xi-h}^{\xi}K(s,x(s))dsd\xi

Under the conditions (C1)−(C4)\left(C_{1}\right)-\left(C_{4}\right) the operator AA is PO and by (C5)−(C6)\left(C_{5}\right)-\left(C_{6}\right) we have that the operator AA is increasing. Since all the conditions of the Abstract Gronwall Lemma 3.3 are satisfied, we obtain x≤xA∗x\leq x_{A}^{*} and the proof is complete.

For (b) the proof is similar.

7. Numerical example

In this section we give a numerical example to illustrate the convergence of the sequence defined in theorem 5.1 to the solution. We consider the following integrodifferential equation:

x′​(t)=−(6+sin⁡(t))​x​(t)+x​(t−π2)−−∫t−π4tsin⁡(s)​x​(s)​𝑑s+5​ecos⁡(t)+ecos⁡(t−π4)−ecos⁡(t−π2),t∈[π4;π]x​(t)=ecos⁡(t),t∈[0;π4]\begin{array}[]{rlrl}x^{\prime}(t)&=-(6+\sin(t))x(t)+x\left(t-\frac{\pi}{2}\right)-&\\ &-\int_{t-\frac{\pi}{4}}^{t}\sin(s)x(s)ds+5e^{\cos(t)}+e^{\cos\left(t-\frac{\pi}{4}\right)}-e^{\cos\left(t-\frac{\pi}{2}\right)}&,&t\in\left[\frac{\pi}{4};\pi\right]\\ x(t)&=e^{\cos(t)}&,&t\in\left[0;\frac{\pi}{4}\right]\end{array}

which has the exact solution x​(t)=ecos⁡(t)x(t)=e^{\cos(t)}.
Numerical method. (For more details see N.L. Trefethen [15], D. Trif [16]) We divide the working interval by the points Pk=k,k=0,1,…,MP_{k}=k,k=0,1,\ldots,M, (concretely M=4M=4 and represents the number of subintervals). On each subinterval Ik=[Pk−1;Pk]I_{k}=\left[P_{k-1};P_{k}\right], k=1,…,Mk=1,\ldots,M, we find the numerical solution by the form

xk​(t)=c0,k​T02+c1,k​T1​(ξ)+c2,k​T2​(ξ)+…+cn−1,k​Tn−1​(ξ),x_{k}(t)=c_{0,k}\frac{T_{0}}{2}+c_{1,k}T_{1}(\xi)+c_{2,k}T_{2}(\xi)+\ldots+c_{n-1,k}T_{n-1}(\xi),

where Ti​(ξ)=cos⁡(i​arccos⁡(ξ))T_{i}(\xi)=\cos(i\arccos(\xi)) are Chebyshev polynomials of ii degree, i=0,…,n−1i=0,\ldots,n-1, (concretely n=8n=8 ), and t=α​ξ+βt=\alpha\xi+\beta with α=(Pk−Pk−1)/2\alpha=\left(P_{k}-P_{k-1}\right)/2, respectively β=(Pk+Pk−1)/2\beta=\left(P_{k}+P_{k-1}\right)/2.

Choosing a mesh ξj,j=1,…,n\xi_{j},j=1,\ldots,n, on interval [ −1;1-1;1 ] consisting by the knots of Gauss quadrature formula generated by Matlab subprogram [csi,w]=pd(n), the transformation t=α​ξ+βt=\alpha\xi+\beta corresponding to each interval Ik=[Pk−1;Pk]I_{k}=\left[P_{k-1};P_{k}\right] construct a local mesh on that subinterval. The coefficients ci,kc_{i,k} of xkx_{k} expansion after the Chebishev polynomials TiT_{i} are obtained from xkx_{k} values on the local mesh using Fast Fourier Transforms (if nn is large) or using a matrix TT generated by the subprogram T=x2t\mathrm{T}=\mathrm{x}2\mathrm{t} (n, csi)
(for nn small)

(c0,kc1,k⋮cn−2,kcn−1,k)=(T′)−1⋅(xk​(t1)xk​(t2)⋮xk​(tn−1)xk​(tn))\left(\begin{array}[]{c}c_{0,k}\\ c_{1,k}\\ \vdots\\ c_{n-2,k}\\ c_{n-1,k}\end{array}\right)=\left(T^{\prime}\right)^{-1}\cdot\left(\begin{array}[]{c}x_{k}\left(t_{1}\right)\\ x_{k}\left(t_{2}\right)\\ \vdots\\ x_{k}\left(t_{n-1}\right)\\ x_{k}\left(t_{n}\right)\end{array}\right)

The same formula allows the quick pass from the local coefficients to the values on local mesh.

The formulae

∫ξ−hξTi​(s)​𝑑s=Ti+1​(ξ)2​(i+1)−Ti−1​(ξ)2​(i−1)\int_{\xi-h}^{\xi}T_{i}(s)ds=\frac{T_{i+1}(\xi)}{2(i+1)}-\frac{T_{i-1}(\xi)}{2(i-1)}

allow to obtain the coefficients CiC_{i} of a primitive FF for a function ff given by its coefficients cic_{i}, from multiplication of them with a sparse matrix JJ generated by the subprogram J=\mathrm{J}= tchebj (n)(\mathrm{n})

(C0C1⋮Cn−2Cn−1)=J⋅(c0c1⋮cn−2cn−1)\left(\begin{array}[]{c}C_{0}\\ C_{1}\\ \vdots\\ C_{n-2}\\ C_{n-1}\end{array}\right)=J\cdot\left(\begin{array}[]{c}c_{0}\\ c_{1}\\ \vdots\\ c_{n-2}\\ c_{n-1}\end{array}\right)

Of course, if the primitive is calculated for other interval [Pk−1;Pk]\left[P_{k-1};P_{k}\right] instead of [−1;1][-1;1], the matrix JJ is replaced by α​J\alpha J, where α=(Pk−Pk−1)/2\alpha=\left(P_{k}-P_{k-1}\right)/2.

The algorithm from Theorem 5.1 is implemented in the following way in program [X,sol]=step_meth2, which can be obtained from the authors (mserban@math.ubbcluj.ro):

Step 0. We generate a global mesh XX on [0;π][0;\pi] by the union of all local meshes on which we also add the points PkP_{k} of subintervals. We calculate the values of x(0)x^{(0)} on the global mesh from the values of the function φ\varphi on the local mesh of the first interval [0;π4]\left[0;\frac{\pi}{4}\right] and from the constant value φ​(π4)\varphi\left(\frac{\pi}{4}\right) on the other knots.

Step kk. Taking the values of x(k)x^{(k)} on the global mesh, we obtain the values of sin⁡(X)⋅x(k)\sin(X)\cdot x^{(k)} on the local mesh, we calculate the coefficients of sin⁡(X)⋅x(k)\sin(X)\cdot x^{(k)} on each subinterval, then we get the coefficients of a primitive for sin⁡(X)⋅x(k)\sin(X)\cdot x^{(k)} on each subinterval and finally we obtain the values of that primitive on the local mesh. We add the contribution of nonintegrated part (where it is used the history from the previous intervals with two steps). The implementation of the formulae from Theorem 5.1 is now immediately, getting the values of the new iteration x(k+1)x^{(k+1)} on the global mesh by a new integration: we pass from the values on the mesh to coefficients, then we use the integration matrix JJ and finally we return to the values in order to find x(k+1)x^{(k+1)}.

Stoping test. We evaluate the difference in norm between the values of x(k)x^{(k)} and x(k+1)x^{(k+1)} and iterations stop when this is below than a chosen value (concretely 10−910^{-9} ). We represent the graph of solution and the norm of difference for different kk.

For the efficiency estimation of this algorithm, the integro-differential equation is written in the form of delay differential equation system and we use the Matlab command dde23 to solve it. We impose the relative error to 10−910^{-9} and the absolute error to 10−1210^{-12} to obtain a accuracy comparable with the step method. We display the graph of solution.

Results. Running the program we get the following results:

Conclusions. For the chosen example, the step method obtains the solution in 68 iterations with an error of 10−1010^{-10} in 0.054 CPU seconds. The Matlab program dde23 needs 0.671 CPU seconds ( 12 times bigger) for a similar precision. The above comparisons validate the step method from the accuracy and efficiency point of view.

Acknowledgment

This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS - UEFISCDI, project number PN-II-ID-PCE-2011-3-0094.

References

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