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Higher-order trigonometrically fitted RKN schemes via compositi...
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求解扰动振子的三角拟合RKN方法及其高阶组合方法,王斌,吴新元,基于Tocino等的修正Runge-Kutta-Nystrom (RKN)方法 ([Math. Comput. Model., (42) 2005]),本文探讨求解扰动振子y“+ω2y(t)=f(y(t)) 高效率的显式三角拟合RKN�
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˖ڍመڙጲ
http://www.paper.edu.cn
求解扰动振子的三角拟合RKN方法及其高阶
组合方法
王斌 ,吴新元
南京大学数学系,南京 210093
摘要:基于Tocino等的修正Runge-Kutta-Nystr¨om (RKN)方法 ([Math. Comput. Model., (42)
2005]),本文探讨求解扰动振子y
00
+ ω
2
y(t) = f(y(t)) 高效率的显式三角拟合RKN方法。将组
合方法技术应用于三角拟合RKN方法,本文导出了一些高阶方法。与文献中已有的经典方法
相比,数值试验结果表明本文的高阶显式三角拟合RKN方法更加有效。
关键词:三角拟合RKN方法,辛条件,扰动振子,组合方法
中图分类号: O241.81
Higher-order trigonometrically fitted RKN
schemes via composition methods for solving
perturbed oscillators
WANG Bin , WU Xin-Yuan
Department of Mathematics, Nanjing University, Nanjing 210093
Abstract: This paper devotes to exploring efficient explicit trigonometrically fitted
Runge-Kutta-Nystr¨om methods for the perturbed oscillators y
00
+ ω
2
y(t) = f(y(t)) based on
the modified Runge-Kutta-Nystr¨om methods proposed by Tocino et al. [Math. Comput.
Model., (42) 2005]. Based on composition methods, some efficient explicit trigonometrically
fitted RKN methods are proposed. Numerical results demonstrate that our higher-order
explicit trigonometrically fitted Runge-Kutta-Nystr¨om methods are more efficient than the
well-known methods in the scientific literature.
Key words: trigonometrically fitted RKN methods, symplecticity conditions, perturbed
oscillators, composition methods.
基金项目: Specialized Research Foundation for the Doctoral Program of Higher Education (20100091110033),
Natural Science Foundation of China (11271186),985 Project at Nanjing University (9112020301), University Post-
graduate Research and Innovation Project of Jiangsu Province 2012 (CXZZ12
−
0028).
作者简介: Wang Bin(1986-),male,PHD student,major research direction:Numerical methods for ordinary
differential equations. Correspondence author:Wu Xin-Yuan(1953-),male,professor,major research direction:
Numerical methods for ordinary differential equations.
- 1 -
˖ڍመڙጲ
http://www.paper.edu.cn
0 Introduction
In the past over two decades, symplectic methods for Hamiltonian systems have formed
a very important category in the numerical solution of ordinary differential equations. For
differential equations with particular structures, it has become a common practice to require
numerical algorithms to adapt the special structure of the problems and preserve as much as
possible the qualitative behavior of the true solutions. Recent survey of structure-preserving
algorithms for ordinary differential equations, we refer to [1, 2].
Oscillatory systems often arise in different fields of applied sciences such as the Kepler
problem, the outer solar system, the H´enon-Heiles model and problems in molecular dynamics,
etc (see [1, 3] for example). Several of these problems can be expressed in the following system
of second order differential equations:
y
00
+ ω
2
y(t) = f(y(t)), y(t
0
) = y
0
, y
0
(t
0
) = y
0
0
, (1)
where ω is the main frequency, y : R → R
d
, and f(y) is the perturbing force with the form
f(y) = −∇U(y). The system (1) is in fact a Hamiltonian system with Hamiltonian H(y
0
, y) =
1
2
y
0T
y
0
+
1
2
ω
2
y
T
y + U(y).
In order to deal with the problem (1), some novel approaches to exploring numerical
methods have been proposed and we refer to [4, 5, 6, 7, 8, 9, 10, 11, 12] for example. For problem
(1), Tocino et al. propose a scheme of modified RKN methods and discuss the symplecticity
conditions in [7]. In this paper, we apply trigonometrical fitting technique to the modified
RKN methods to achieve the so called trigonometrically fitted RKN methods for problem
(1). The corresponding symplecticity conditions for trigonometrically fitted RKN methods
are obtained by using the results given in [7]. Based on the symplecticity conditions and
composition methods, we propose some practical explicit symplectic trigonometrically fitted
RKN methods. Three numerical experiments are carried out to demonstrate the efficiency of
these new methods.
1 Trigonometrically fitted RKN methods
For the numerical solution of problem (1), modified RKN methods of the following form
are introduced in [7]:
Y
i
= C
i
y
n
+ hD
i
y
0
n
+ h
2
s
P
j=1
a
ij
f(Y
j
), i = 1, 2, · · · , s,
y
n+1
= Cy
n
+ hDy
0
n
+ h
2
s
P
i=1
β
i
f(Y
i
),
y
0
n+1
= Ay
0
n
+
B
h
y
n
+ h
s
P
i=1
b
i
f(Y
i
),
(2)
- 2 -
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