Efficient numerical computations for solving high-dimensional stochastic differential equations

IF 2.6 2区数学 Q1 MATHEMATICS, APPLIED

Journal of Computational and Applied Mathematics Pub Date : 2025-09-11 DOI:10.1016/j.cam.2025.117043

Yoshio Komori , Kevin Burrage

引用次数: 0

Abstract

The efficient numerical computations of matrix exponentials in a tensor framework have been recently studied by some researchers. We consider this approach and apply it to exponential methods for stochastic differential equations (SDEs). As a result, we will show that the approach is available for the methods to solve high-dimensional SDEs efficiently.

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求解高维随机微分方程的有效数值计算

近年来，一些研究者对张量框架中矩阵指数的有效数值计算进行了研究。我们考虑这种方法，并将其应用于随机微分方程的指数方法。因此，我们将证明该方法可用于有效求解高维SDEs的方法。

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

Journal of Computational and Applied Mathematics 数学-应用数学

CiteScore

5.40

自引率

4.20%

发文量

437

审稿时长

3.0 months

期刊介绍： The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.