One-Parameter Finite Difference Methods and Their Accelerated Schemes for Space-Fractional Sine-Gordon Equations with Distributed Delay

IF 0.9 4区 数学 Q2 MATHEMATICS
T. Sun, Chengjian Sun
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引用次数: 0

Abstract

This paper deals with numerical methods for solving one-dimensional (1D) and two-dimensional (2D) initial-boundary value problems (IBVPs) of space-fractional sine-Gordon equations (SGEs) with distributed delay. For 1D problems, we construct a kind of one-parameter finite difference (OPFD) method. It is shown that, under a suitable condition, the proposed method is convergent with second order accuracy both in time and space. In implementation, the preconditioned conjugate gradient (PCG) method with the Strang circulant preconditioner is carried out to improve the computational efficiency of the OPFD method. For 2D problems, we develop another kind of OPFD method. For such a method, two classes of accelerated schemes are suggested, one is alternative direction implicit (ADI) scheme and the other is ADI-PCG scheme. In particular, we prove that ADI scheme can arrive at second-order accuracy in time and space. With some numerical experiments, the computational effectiveness and accuracy of the methods are further verified. Moreover, for the suggested methods, a numerical comparison in computational efficiency is presented.
具有分布延迟的空间分数阶正弦戈登方程的单参数有限差分方法及其加速格式
本文研究了具有分布延迟的空间分数阶正弦-戈登方程的一维和二维初边值问题的数值解法。针对一维问题,构造了一种单参数有限差分(OPFD)方法。结果表明,在适当的条件下,该方法在时间和空间上都具有二阶精度的收敛性。在实现上,为了提高OPFD方法的计算效率,采用了Strang循环预条件的预条件共轭梯度(PCG)方法。对于二维问题,我们发展了另一种OPFD方法。针对这种方法,提出了两类加速方案,一种是替代方向隐式(ADI)方案,另一种是ADI- pcg方案。特别地,我们证明了ADI方案在时间和空间上都能达到二阶精度。通过数值实验,进一步验证了该方法的计算有效性和准确性。此外,还对所提方法的计算效率进行了数值比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.80
自引率
0.00%
发文量
1130
审稿时长
2 months
期刊介绍: Journal of Computational Mathematics (JCM) is an international scientific computing journal founded by Professor Feng Kang in 1983, which is the first Chinese computational mathematics journal published in English. JCM covers all branches of modern computational mathematics such as numerical linear algebra, numerical optimization, computational geometry, numerical PDEs, and inverse problems. JCM has been sponsored by the Institute of Computational Mathematics of the Chinese Academy of Sciences.
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