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Numerical stability and oscillation of the RungeKutta methods for equation {x}^{\prime}(t)=ax(t)+{a}_{0}x(M[\frac{t+N}{M}])
Advances in Difference Equations volume 2012, Article number: 146 (2012)
Abstract
This paper deals with the numerical properties of RungeKutta methods for the alternately of retarded and advanced equation {x}^{\prime}(t)=ax(t)+{a}_{0}x(M[\frac{t+N}{M}]). Necessary and sufficient conditions for the stability and oscillation of the numerical solution are given. The conditions that the RungeKutta methods preserve the stability and oscillations of the analytic solutions are obtained. Some numerical experiments are illustrated.
1 Introduction
This paper deals with the numerical solutions of the alternately of retarded and advanced equation with piecewise continuous arguments (EPCA)
where [\cdot ] is the greatest integer function, and M and N are positive integers such that N<M. Differential equations of this form have simulated considerable interest and have been studied by Wiener and Aftabizadeh [11]. Cooke and Wiener [2] studied the special cases of this form with M=1, N=\frac{1}{2} and M=2, N=1. In these equations the argument derivation T(t)=tM[\frac{t+N}{M}] are piecewise linear periodic function with periodic M. Also, T(t) is negative for MnN\le t<Mn and positive for Mn<t<M(n+1)N (n is integer). Therefore, Eq. (1.1) is of advanced type on [MnN,Mn) and of retarded type on (Mn,M(n+1)N).
EPCA describe hybrid dynamics systems, combine properties of both differential and difference equations, and have applications in certain biomedical models in the work of Busenberg and Cooke [1]. For these equations of mixed type, the change of sign in the argument derivation leads not only to interesting periodic properties but also to complications in the asymptotic and oscillatory behavior of solutions. Oscillatory, stability and periodic properties of the linear EPCA of the form with alternately of retarded and advanced have been investigated in [2].
There exist some papers concerning the stability of the numerical solutions of delay differential equations with piecewise continuous arguments, such as [5, 8, 12]. Also, there have been results concerning oscillations of delay differential equations and delay difference equations, even including delay differential equations with piecewise continuous arguments [6]. But there is no paper concerning with the stability and oscillation of the numerical solution of these equations.
In this paper, we will investigate the numerical properties, including the stability and oscillation of RungeKutta methods for equation {x}^{\prime}(t)=ax(t)+{a}_{0}x(M[\frac{t+N}{M}]). In Section 2 we give some preliminary results for the analytic solutions of this equation. In Section 3 we consider the adaptation of the RungeKutta methods. In Section 4 we give the conditions of the stability and oscillation of the RungeKutta methods. In Section 5 we investigate the preservation of the stability and oscillations of the RungeKutta methods. In Section 6 some numerical experiments are illustrated.
2 Preliminary results
We consider the following equation
where a, {a}_{0} are constants, M and N are positive integers such that N<M (M\ne 2N) and [\cdot ] is the greatest integer function.
Definition 2.1[10]
A solution of Eq. (2.1) on [0,\mathrm{\infty}) is a function x(t) that satisfies the conditions:

1.
x(t) is continuous on [0,\mathrm{\infty}).

2.
The derivative {x}^{\prime}(t) exists at each point t\in [0,\mathrm{\infty}) with the possible exception of the points t=MnN (n=1,2,\dots), where onesided derivatives exist.

3.
Eq. (2.1) is satisfied on each interval [MnN,M(n+1)N) for n=1,2,\dots .
As follows, we use these notations
The following theorems give the existence and uniqueness of solutions and then provide necessary and sufficient conditions for the asymptotic stability and the oscillation of all solutions of Eq. (2.1).
Theorem 2.2[10]
Assume thata,{a}_{0},{x}_{0}\in \mathbb{R}, then the initial value problem (2.1) has on[0,\mathrm{\infty})a unique solutionx(t)given by
if{\lambda}_{1}\ne 0, whereT(t)=tM[\frac{t+N}{M}].
Theorem 2.3[10]
The solutionx=0of Eq. (2.1) is asymptotically stable ast\to +\mathrm{\infty}if and only if\frac{{\lambda}_{1}}{{\lambda}_{1}}<1.
Lemma 2.4[10]
ForM\ne 2N, the equation
has a unique nonzero solution with respect to a. This solution is negative ifM>2Nand positive ifM<2N.
Theorem 2.5[10]
Let{a}^{\ast}be the nonzero solution of (2.3). The solutionx=0of Eq. (2.1) is asymptotically stable (x(t)\to 0ast\to +\mathrm{\infty}) if any one of the following hypotheses is satisfied:
({A}_{1}) a>{a}^{\ast}, \frac{a({e}^{Ma}+1)}{\phi (a)}<{a}_{0}<a;
({A}_{2}) a<{a}^{\ast}, {a}_{0}>\frac{a({e}^{Ma}+1)}{\phi (a)}or{a}_{0}<a;
({A}_{3}) a={a}^{\ast}, {a}_{0}<a.
In the following, we will give the definition of the oscillation and the nonoscillation.
Definition 2.6 A nontrivial solution of (2.1) is said to be oscillatory if there exists a sequence \{{t}_{k}\} such that {t}_{k}\to \mathrm{\infty} as k\to \mathrm{\infty} and x({t}_{k})x({t}_{k1})\le 0. Otherwise it is called nonoscillatory. We say Eq. (2.1) is oscillatory if all nontrivial solutions of Eq. (2.1) are oscillatory. We say Eq. (2.1) is nonoscillatory if all nontrivial solutions of Eq. (2.1) are nonoscillatory.
Theorem 2.7[10]
A necessary and sufficient condition for all solutions of Eq. (2.1) to be oscillatory is that either of the conditions{a}_{0}<\frac{a}{{e}^{a(MN)}1}{e}^{a(MN)}, {a}_{0}>\frac{a}{{e}^{aN}1}holds.
3 The RungeKutta methods
In this section we consider the adaptation of the RungeKutta methods (A,b,c). Let h=\frac{1}{m} be a given stepsize with integer m\ge 1 and the gridpoints be defined by {t}_{n}=nh (n=0,1,2,\dots).
For the RungeKutta methods we always assume that {b}_{1}+{b}_{2}+\cdots +{b}_{\nu}=1 and 0\le {c}_{1}\le {c}_{2}\le \cdots \le {c}_{\nu}\le 1.
The adaptation of RungeKutta methods to Eq. (2.1) leads to a numerical process of the following type
where matrix A={({a}_{ij})}_{\nu \times \nu}, vectors b={({b}_{1},{b}_{2},\dots ,{b}_{\nu})}^{T}, c={({c}_{1},{c}_{2},\dots ,{c}_{\nu})}^{T} and {x}_{n} is an approximation to x(t) at {t}_{n}, {y}_{i}^{n} and {z}_{i}^{n} are approximation to x({t}_{n}+{c}_{i}h) and x(M[\frac{{t}_{n}+{c}_{i}h+N}{M}]), respectively.
Let n=Mkm+l, l=Nm,Nm+1,\dots ,(MN)m1 for k\ge 1; l=0,1,\dots ,(MN)m1 for k=0. Then {z}_{i}^{(Mkm+l)} can be defined as {x}_{Mkm} according to Definition 2.1 (i=1,\dots ,\nu).
Let {Y}^{n}=({y}_{1}^{n},{y}_{2}^{n},\dots ,{y}_{\nu}^{n}). Then (3.1) reduces to
where e={(1,1,\dots ,1)}^{T}. Hence we have
where x=ha, R(x)=1+x{b}^{T}{(IxA)}^{1}e is the stability function of the method.
We can obtain from (3.3)
4 Stability and oscillation of the RungeKutta methods
In this section we will discuss stability and oscillation of the RungeKutta methods.
4.1 Numerical stability
Definition 4.1 The RungeKutta method is called asymptotically stable at (a,{a}_{0}) if there exists a constant C such that Eq. (3.1) defines {x}_{n} (n\ge 1) that satisfy {lim}_{n\to \mathrm{\infty}}{x}_{n}=0 for all h=\frac{1}{m} (m>C) and any given {x}_{0}.
For any given RungeKutta methods, R(x)=\frac{P(x)}{Q(x)}, where P(x) and Q(x) are polynomials. R(x) is a continuous function at the neighborhood of zero, and R(0)={R}^{\prime}(0)=1, so there exist {\delta}_{1}, {\delta}_{2} with {\delta}_{1}<0<{\delta}_{2} such that
which implies 0<\frac{R(x)1}{x}<\mathrm{\infty}, {\delta}_{1}<x<{\delta}_{2}.
Remark 4.2[7]
It is known that R(x) is an increasing function in [1,1], and 1<R(x)<\mathrm{\infty} for 0<x<1, 0<R(x)<1 for 1\le x<0. Hence we can take {\delta}_{1}=1, {\delta}_{2}=1 for simplicity.
In the following, we always suppose h<\frac{1}{a}, i.e., C=a. It is easy to see from (3.4) and (3.5) that {x}_{n}\to 0 as n\to \mathrm{\infty} if and only if {x}_{Mkm}\to 0 as k\to \mathrm{\infty}. Hence we have the following theorem.
Theorem 4.3 The RungeKutta method is asymptotically stable if any one of the following hypotheses is satisfied:
({N}_{1}) \frac{a({R}^{Mm}(x)+1)}{\overline{\phi}(x)}<{a}_{0}<a, a>\overline{a};
({N}_{2}) {a}_{0}>\frac{a({R}^{Mm}(x)+1)}{\overline{\phi}(x)}or{a}_{0}<a, a<\overline{a};
({N}_{3}) {a}_{0}<a, a=\overline{a},
wherex=ha, \overline{\phi}(x)\equiv {R}^{Mm}(x)2{R}^{Nm}(x)+1, h\overline{a}is a nonzero solution of\overline{\phi}(x).
Proof Let \overline{{\lambda}_{1}}={R}^{(MN)m}(x)+\frac{{a}_{0}}{a}({R}^{(MN)m}(x)1), \overline{{\lambda}_{1}}={R}^{Nm}(x)+\frac{{a}_{0}}{a}({R}^{Nm}(x)1). From (3.5), we only need to verify the inequalities \frac{\overline{{\lambda}_{1}}}{\overline{{\lambda}_{1}}}<1. Assume \overline{{\lambda}_{1}}>0, i.e., {a}_{0}<\frac{a}{{R}^{Nm}(x)1}. Then 1<\frac{\overline{{\lambda}_{1}}}{\overline{{\lambda}_{1}}}<1 is equivalent to
Assume \overline{{\lambda}_{1}}<0, i.e., {a}_{0}>\frac{a}{{R}^{Nm}(x)1}. Then 1<\frac{\overline{{\lambda}_{1}}}{\overline{{\lambda}_{1}}}<1 is equivalent to
The theorem is proved. □
4.2 Numerical oscillation
Consider the difference equation
where k=1,2,\dots , {p}_{i}\in \mathbb{R}, i=1,2,\dots ,k and its associated characteristic equation is
Definition 4.4 A nontrivial solution \{{a}_{n}\} of (4.2) is said to be oscillatory if there exists a sequence \{{n}_{k}\} such that {n}_{k}\to \mathrm{\infty} as k\to \mathrm{\infty} and {a}_{{n}_{k}}{a}_{{n}_{k}1}\le 0. Otherwise, it is called nonoscillatory. Eq. (4.2) is said to be oscillatory if all nontrivial solutions of Eq. (4.2) are oscillatory. Eq. (4.2) is said to be nonoscillatory if all nontrivial solutions of Eq. (4.2) are nonoscillatory.
Theorem 4.5[3]
Eq. (4.2) is oscillatory if and only if the characteristic equation (4.3) has no positive roots.
We obtain the following theorem
Theorem 4.6 Suppose{x}_{n}and{x}_{Mkm}are given by (3.4) and (3.5) respectively, then the following statements are equivalent:

1.
{x}_{n} is oscillatory;

2.
{x}_{Mkm} is oscillatory;

3.
{a}_{0}<\frac{a{R}^{(MN)m}(x)}{{R}^{(MN)m}(x)1}, or {a}_{0}>\frac{a}{{R}^{Nm}(x)1}.
Proof\{{x}_{Mkm}\} is not oscillatory if and only if
i.e.,
Hence we have for l=Nm,Nm+1,\dots ,(MN)m1.
which is equivalent to
In view of (3.4), \{{x}_{n}\} is not oscillatory.
Moreover, \{{x}_{Mkm}\} is oscillatory if and only if
which is equivalent to
□
5 Preservation of stability and oscillations of the RungeKutta methods
In this section, we will investigate the conditions under which the numerical solution and the analytic solution are oscillatory simultaneously. We will study the stability and the oscillation of RungeKutta methods with the stability function which is given by the (r,s)padé approximation to {e}^{z}.
In order to do this, the following lemmas and corollaries will be useful.
The(r,s)padé approximation to{e}^{z}is given by
where
with error
It is the unique rational approximation to {e}^{z} of order r+s, such that the degree of numerator and denominator are r and s, respectively.
IfR(z)is the(r,s)padé approximation to{e}^{z}, then

1.
there are s bounded star sectors in the righthalf plane, each containing a pole of R(z);

2.
there are r bounded white sectors in the lefthalf plane, each containing a zero of R(z);

3.
all sectors are symmetric with respect to the real axis.
We can obtain the following corollary.
SupposeR(z)is the(r,s)padé approximation to{e}^{z}. Then

1.
x>0:
R(x)<{e}^{x}for allx>0if and only if s is even,
R(x)>{e}^{x}for all\xi >x>0if and only if s is odd,

2.
x<0:
R(x)>{e}^{x}for allx<0if and only if r is even,
R(x)<{e}^{x}for all\eta <x<0if and only if r is odd,
where\xi >1is a real zero of{Q}_{s}(z)and\eta <1is a real zero of{P}_{r}(z).
5.1 Preservation of the stability
In this subsection, in order to analyse preservation of stability, the following lemmas are useful.
Lemma 5.4 Letf(r)={r}^{M}2{r}^{N}+1. Then

1.
the function f(r) has a minimum at {r}_{1}={(\frac{2N}{M})}^{\frac{1}{MN}}, and f(r) is decreasing in [0,{r}_{1}) and increasing in ({r}_{1},\mathrm{\infty});

2.
the function f(r) has a unique solution {r}_{0}\ne 1, which is less than one when M>2N and larger than one when M<2N;

3.
when M>2N:
f(r)<0ifr\in ({r}_{0},1),
f(r)>0ifr\in [0,{r}_{0})orr\in (1,\mathrm{\infty});
whenM<2N:
f(r)<0ifr\in (1,{r}_{0}),
f(r)>0ifr\in [0,1)orr\in ({r}_{0},\mathrm{\infty}).
Lemma 5.5 Letg(y)=\frac{{y}^{M}+1}{{y}^{M}2{y}^{N}+1}. Then

1.
the function g(y) has extremum at {y}_{1}={(\frac{N}{MN})}^{\frac{1}{M}};

2.
when M>2N:
g(y)is increasing in(0,{r}_{0})and({r}_{0},{y}_{1}),
g(y)is decreasing in({y}_{1},1)and(1,\mathrm{\infty});

3.
when M<2N:
g(y)is increasing in(0,1)and(1,{y}_{1}),
g(y)is decreasing in({y}_{1},{r}_{0})and({r}_{0},\mathrm{\infty}).
Lemma 5.6 Assume that{r}_{0}\ne 1is a unique solution of the functionf(r)={r}^{M}2{r}^{N}+1, {r}_{1}={(\frac{2N}{M})}^{\frac{1}{MN}}, {y}_{1}={(\frac{N}{MN})}^{\frac{1}{M}}. Then the following hypothesis are satisfied:

1.
{r}_{0}<{y}_{1}<{r}_{1}<1, if M>2N;

2.
1<{y}_{1}<{r}_{1}<{r}_{0}, if M<2N.
Proof By virtue of Bernoulli inequality, we obtain
and
By virtue of Lemma 5.4, the proof is complete. □
Theorem 5.7 Assume that Eq. (2.1) is asymptotically stable andM>2N. Then the RungeKutta method is asymptotically stable if one of the following conditions is satisfied.

(i)
{R}^{m}(x)\le {e}^{a} (a\le ln{y}_{1});

(ii)
{R}^{m}(x)\ge {e}^{a} (ln{y}_{1}<a<0);

(iii)
{R}^{m}(x)\le {e}^{a} (a\ge 0).
Proof In view of Theorem 2.5 and Theorem 4.3 we will prove that the conditions ({N}_{1}), ({N}_{2}), ({N}_{3}) are satisfied under conditions ({A}_{1}), ({A}_{2}), ({A}_{3}). It is known from {R}^{m}(x)\le {e}^{a} (a\le ln{y}_{1}) and Lemma 5.4 and Lemma 5.5 that f(r) is decreasing and g(y) is increasing. Hence {a}^{\ast}<\overline{a} and
If a>{a}^{\ast}, then ({A}_{1}) holds which together with (5.4) imply ({N}_{1}), ({N}_{2}), ({N}_{3}). If a<{a}^{\ast}, then ({A}_{2}) holds which together with (5.4) imply ({N}_{2}). If a={a}^{\ast}, then ({A}_{3}) holds which implies ({N}_{2}). The proof is complete. □
Theorem 5.8 Assume that Eq. (2.1) is asymptotically stable andM<2N. Then the RungeKutta method is asymptotically stable if one of the following conditions is satisfied.

(i)
{R}^{m}(x)\le {e}^{a} (a\le 0);

(ii)
{R}^{m}(x)\ge {e}^{a} (a>0 and {R}^{m}(x)\le {y}_{1});

(iii)
{R}^{m}(x)\le {e}^{a} ({y}_{1}<{R}^{m}(x)).
Proof In view of Theorem 2.5 and Theorem 4.3 we will prove that the conditions ({N}_{1}), ({N}_{2}), ({N}_{3}) are satisfied under conditions ({A}_{1}), ({A}_{2}), ({A}_{3}). It is known from {R}^{m}(x)\le {e}^{a} ({y}_{1}\le {R}^{m}(x)) and Lemma 5.4 and Lemma 5.5 that f(r) is increasing and g(y) is decreasing. Hence {a}^{\ast}<\overline{a}. The rest of the proof is similar to that in Theorem 5.7. □
From Theorem 5.7 and Theorem 5.8, we obtain the following corollary.
Corollary 5.9 The vstage Astable higher order RungeKutta method preserves the stability of Eq. (2.1), if one of the following conditions is satisfied.
WhenM>2N:

(i)
for GaussLegendre and Lobatto IIIC methods,
v is odd, if{e}^{a}\le {y}_{1},
v is even, if{e}^{a}>{y}_{1};

(ii)
for Lobatto IIIA and Lobatto IIIC methods,
v is even, if{e}^{a}\le {y}_{1},
v is odd, if{e}^{a}>{y}_{1};

(iii)
for Radau IA and IIA methods,
v is even, if{y}_{1}<{e}^{a}<1,
v is odd, otherwise.
WhenM<2N:

(i)
for GaussLegendre and Lobatto IIIC methods,
v is odd, if{R}^{m}(x)\le {y}_{1},
v is even, if{R}^{m}(x)>{y}_{1};

(ii)
for Lobatto IIIA and Lobatto IIIC methods,
v is even, if{R}^{m}(x)\le {y}_{1},
v is odd, if{R}^{m}(x)>{y}_{1};

(iii)
for Radau IA and IIA methods,
v is odd, if{R}^{m}(x)\le {y}_{1},
v is even, otherwise.
5.2 Preservation of the oscillation
In the subsection we will investigate the conditions under which the RungeKutta method preserves the oscillation of Eq. (2.1).
We have from Theorem 2.7 and Theorem 4.6 that the RungeKutta method preserves the oscillation of Eq. (2.1) if and only if
Hence the following theorem is obvious.
Theorem 5.10 The RungeKutta method preserves the oscillation of Eq. (2.1) if and only if
Corollary 5.11 The vstage Astable higher order RungeKutta method preserves the oscillation of Eq. (2.1) if and only if

(i)
for GaussLegendre and Lobatto IIIC methods, v is odd;

(ii)
for Lobatto IIIA and Lobatto IIIC methods, v is even;

(iii)
for Radau IA and IIA methods, v is even if a<0, v is odd if a\ge 0.
5.3 The explicit RungeKutta methods
It is known that all νstage explicit RungeKutta methods with p=\nu =1,2,3,4 possess the stability function (see [4])
which is the (\nu ,0)padé approximation to {e}^{x}.
Theorem 5.12 The νstage explicit RungeKutta methods withp=\nu =1,2,3,4preserve the asymptotic stability of Eq. (2.1) ifa\ge 0and

(i)
ν is odd for a\le ln{y}_{1}\le 0 and M>2N;

(ii)
ν is even for ln{y}_{1}\le a<0 and M>2N;

(iii)
ν is odd for a<0 and M>2N.
Theorem 5.13 The νstage explicit RungeKutta methods withp=\nu =1,2,3,4preserve the oscillation of Eq. (2.1) witha<0if ν is odd.
6 Numerical examples
In this section, we will give some examples to illustrate the conclusions in the paper. In order to illustrate the stability, we consider the following two problems
In Figure 1Figure 3, we draw the numerical solutions for Eq. (6.1) and Eq. (6.2), respectively. It is easy to see that the numerical solutions are asymptotically stable.
In order to illustrate the oscillation, we consider the following two problems
In Figure 4Figure 6, we draw the numerical solutions for Eq. (6.3) and Eq. (6.4), respectively. It is easy to see that the numerical solutions are oscillatory.
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The financial support from the National Natural Science Foundation of China (No.11071050) is gratefully acknowledged.
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Song, M., Liu, M. Numerical stability and oscillation of the RungeKutta methods for equation {x}^{\prime}(t)=ax(t)+{a}_{0}x(M[\frac{t+N}{M}]). Adv Differ Equ 2012, 146 (2012). https://doi.org/10.1186/168718472012146
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DOI: https://doi.org/10.1186/168718472012146