We propose an accurate algorithm for a novel sum-of-exponentials (SOE) approximation of kernel functions, and develop a fast algorithm for convolution quadrature based on the SOE, which allows an order $N$ calculation for $N$ time steps of approximating a continuous temporal convolution integral. The SOE method is constructed by a combination of the de la Vall\'ee-Poussin sums for a semi-analytical exponential expansion of a general kernel, and a model reduction technique for the minimization of the number of exponentials under given error tolerance. We employ the SOE expansion for the finite part of the splitting convolution kernel such that the convolution integral can be solved as a system of ordinary differential equations due to the exponential kernels. The remaining part is explicitly approximated by employing the generalized Taylor expansion. The significant features of our algorithm are that the SOE method is efficient and accurate, and works for general kernels with controllable upperbound of positive exponents. We provide numerical analysis for the SOE-based convolution quadrature. Numerical results on different kernels, the convolution integral and integral equations demonstrate attractive performance of both accuracy and efficiency of the proposed method.
翻译:我们提出一个精确的算法,用于对内核功能进行新颖的耗量总和近似值,并基于SOE开发一个快速的进化二次算法,根据SOE为进化二次曲线的有限部分使用SOE扩展法,这样可以将进化整体以美元计时,以近于连续的瞬间进化整体。SOE方法的构建方式是结合一个半分析性指数性扩大总内核的半分析性指数性扩展法,以及一个在给定的差错容忍度下最大限度地减少指数数量的模型。我们用SOE扩展法对分裂二次进化核心的有限部分进行计算,这样,进化整体内核部分可以作为因指数内核而导致的普通差异方程的系统解决。其余部分显然是通过使用通用的泰勒扩张法来近似的。我们算法的重要特征是SOE方法是高效和准确的,为总内核内核内核中上层进行控制。我们为基于SOE的进核内核的内核整体内核的内核综合性效率以及不同的内核表现方法提供了数字分析。