数学
变量(数学)
后向微分公式
卷积(计算机科学)
规范(哲学)
理论(学习稳定性)
应用数学
方案(数学)
数学分析
微分方程
常微分方程
计算机科学
搭配法
人工神经网络
政治学
法学
机器学习
作者
Zhaoyi Li,Hong-lin Liao
摘要
We prove that the two-step backward differentiation formula (BDF2) method is stable on arbitrary time grids; while the variable-step BDF3 scheme is stable if almost all adjacent step ratios are less than 2.553. These results relax the severe mesh restrictions in the literature and provide a new understanding of variable-step BDF methods. Our main tools include the discrete orthogonal convolution kernels and an elliptic-type matrix norm.
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