加快牛顿方法对-巴克斯特像矩阵方程的收
1Department of Mathematics, Physics and Informatics, Mkwawa University College of Education, P.O. Box 2513, Iringa, Tanzania.
Heliyon
|February 24, 2025
概括
精确线索搜索改善了-巴克斯特矩阵方程的牛顿方法收,优于连续过度放松. 数值分析证实,方程通常是良好的条件,尽管对某些扰动敏感.
科学领域:
- 数字分析 数字分析
- 矩阵理论是一个矩阵理论.
- 计算数学是指计算数学.
背景情况:
- -巴克斯特矩阵方程在量子组和可整合系统中至关重要.
- 对于非碎的解决方案,需要有效的数值方法.
- 牛顿的方法提供了一个框架,但需要趋同增强.
研究的目的:
- 应用和比较准确的线索搜索和顺序过度放松牛顿的方法.
- 分析-巴克斯特方程的条件数 (规范式,混合式,组件式).
- 评估拟议方法的数值稳定性和收性质.
主要方法:
- 使用牛顿方法实现精确线索搜索.
- 顺序过度放松的应用到牛顿的方法.
- 规范式,混合式和组件式条件数的导数和计算.
主要成果:
- 准确的线索搜索显著加快了与相继过度放松相比的趋同.
- -巴克斯特方程表现出良好的条件与混合和组件衡量措施 (接近一个).
- 标准化条件数字表明对干扰的敏感性更高.
结论:
- 精确线索搜索是加速牛顿方程用于-巴克斯特方程的优质技术.
- 这个方程在数值上总体上是很好的,但通常应该考虑敏感性.
- 这些发现为解决科学计算中的复杂矩阵方程提供了宝贵的见解.
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