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Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs

机译:使用Adams类型方法和向后微分公式求解ODE的自动区间块划分

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摘要

Most methods for solving stiff systems of ordinary differential equations (ODEs) are based on backward differentiation formulas (BDFs) which normally require repeated solution of systems of linear equations with coefficient matrix, I - hβJ, where J is the Jacobian matrix as part of a Newton-like iteration on each time step. The matrix operations in the iteration scheme consumes a considerable amount of computational effort. Therefore, in this paper, our objective is to reduce the cost of the iteration scheme by technique of partitioning. The strategy adopted for partitioning is based on block Adams method and block BDF method. Numerical results demonstrates the efficiency of the proposed partitioning in improving both the accuracy and CPU time over traditional stiff methods.
机译:求解常微分方程(ODE)的刚性系统的大多数方法都是基于向后微分方程(BDF)的,通常需要对系数方程为I-hβJ的线性方程组进行重复求解,其中J是作为a的一部分的Jacobian矩阵每个时间步上都有类似牛顿的迭代。迭代方案中的矩阵运算消耗大量计算量。因此,在本文中,我们的目标是通过分区技术来降低迭代方案的成本。分区采用的策略基于块Adams方法和块BDF方法。数值结果表明,与传统的刚性方法相比,所建议的分区在提高准确性和CPU时间方面均有效。

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