较低的误差极限和近似的最佳性跳跃扩散SDEs与不连续的漂移漂移
Paweł Przybyłowicz1, Verena Schwarz2, Michaela Szölgyenyi2
1Faculty of Applied Mathematics, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, Poland.
概括
对于近似跳转扩散随机微分方程 (SDEs) 的数值方法,被证明具有不连续漂移的较低误差极限. 这些发现表明,针对这些复杂的SDEs,特定的跳跃适应方案的最佳性.
科学领域:
- 数字分析 数字分析
- 随机过程 随机过程
- 计算数学 计算数学 计算数学
背景情况:
- 随机微分方程 (SDEs) 对于模拟具有固有的随机性系统至关重要.
- 跳跃扩散SDEs包含突然的,不连续的变化,对数值近似提出了重大挑战.
- 不连续的漂移项进一步复杂化了这些方程的精确数值解决方案.
研究的目的:
- 为应用在跳转扩散的SDEs与不连续漂移的数值方法建立的较低误差极限.
- 分析非适应性和跳转适应性近似方案的性能.
- 确定这些数值方法的理论准确度极限.
主要方法:
- 使用数学分析推导分析较低误差极限.
- 研究近似方案,包括非适应性和跳转适应方法.
- 在不同近似策略中对错误界限的比较分析.
主要成果:
- 对于不连续漂移的跳转扩散SDEs的数值近似,已经证明了3/4级的较低误差边界.
- 这些限制适用于非适应性和跳转适应性近似方案.
- 结果证实了基于转换的跳跃适应的准米尔斯坦方案的最佳性.
结论:
- 建立的较低误差极限为这个领域的数值方法提供了对最好的精度的基本理解.
- 基于转换的跳转适应的准米尔斯坦方案的最佳性在理论上得到了验证.
- 这些发现指导了跳转扩散SDEs的高效数值技术的开发和选择.
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