We present the Parareal-CG algorithm for time-dependent differential equations in this work. The algorithm is a parallel in time iteration algorithm utilizes Chebyshev-Gauss spectral collocation method for fine propagator F and backward Euler method for coarse propagator G. As far as we know, this is the first time that the spectral method used as the F propagator of the parareal algorithm. By constructing the stable function of the Chebyshev-Gauss spectral collocation method for the symmetric positive definite (SPD) problem, we find out that the Parareal-CG algorithm and the Parareal-TR algorithm, whose F propagator is chosen to be a trapezoidal ruler, converge similarly, i.e., the Parareal-CG algorithm converge as fast as Parareal-Euler algorithm with sufficient Chebyhsev-Gauss points in every coarse grid. Numerical examples including ordinary differential equations and time-dependent partial differential equations are given to illustrate the high efficiency and accuracy of the proposed algorithm.
翻译:在本文中,我们提出了用于时间依赖性微分方程的Parareal-CG算法。该算法是一种并行迭代算法,利用Chebyshev-Gauss谱求解法进行精细传播器F和向后欧拉方法进行粗糙传播器G。据我们所知,这是首次将谱方法用作Parareal算法的F传播器。通过构建Chebyshev-Gauss谱求解法的稳定函数来解决对称正定问题,我们发现,Parareal-CG算法和Parareal-TR算法(其F传播器被选择为梯形规则)的收敛速度相似,即当每个粗网格具有足够的Chebyshev-Gauss点时,Parareal-CG算法的收敛速度与Parareal-Euler算法相同。给出了一些数值例子,包括普通微分方程和时间相关的偏微分方程,以说明所提出的算法的高效性和精度。