In this article, we propose an algorithm for approximating the action of $\varphi-$functions of matrices against vectors, which is a key operation in exponential time integrators. In particular, we consider matrices with Kronecker sum structure, which arise from problems admitting a tensor product representation. The method is based on quadrature approximations of the integral form of the $\varphi-$functions combined with a scaling and modified squaring method. Owing to the Kronecker sum representation, only actions of 1D matrix exponentials are needed at each quadrature node and assembly of the full matrix can be avoided. Additionally, we derive \emph{a priori} bounds for the quadrature error, which show that, as expected by classical theory, the rate of convergence of our method is supergeometric. Guided by our analysis, we construct a fast and robust method for estimating the optimal scaling factor and number of quadrature nodes that minimizes the total cost for a prescribed error tolerance. We investigate the performance of our algorithm by solving several linear and semilinear time-dependent problems in 2D and 3D. The results show that our method is accurate and orders of magnitude faster than the current state-of-the-art.
翻译:在此篇文章中, 我们提出一个算法, 以接近 $- $ 基质对矢量的作用, 这是指数化时间集成器中的关键操作。 我们特别考虑使用 克朗克尔 um 结构的矩阵, 由接受 Exception a Exgress Production 产生的问题产生 。 方法基于 $ vapher- $ 函数整体形式的二次近似, 加上一个缩放和修改的方位法 。 由于 克朗和 方位代表制, 只需在 方位节点和 完整 矩阵组装中 避免 1D 基质 指数 。 此外, 我们从 \ emph{ a sidi} 角度来计算二次曲线错误的边框, 这表明, 根据 古典理论, 我们方法的趋同率是超测的。 根据我们的分析, 我们设计了一个快速有力的方法, 来估计最佳的缩放系数和四度节点数, 以最小化为规定错误容忍度的总成本。 我们通过解决 几个线性和半线性时间基值问题来调查我们的算结果, 在 2D 和 3D 级中, 显示 的精确度 速度 。 。