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arXiv 1809.04337math.NAcs.NA
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基于投影的自适应牛顿型方案

Adaptive Newton-Type Schemes Based on Projections

Applied University of Zurich(苏黎世应用科学大学)

Mario Amrein

AI总结:

本文提出了一种基于微分方程概念的自适应牛顿型方法,通过自适应步长控制来改进牛顿法的收敛性,同时保持其二次收敛特性。

摘要:

在本文中,我们提出并讨论了牛顿型方法的一种可能的全局化概念。我们考虑在R^n中求解非线性问题f(x)=0,基于常微分方程的概念作为所提出的数值求解过程的基础。因此,我们的方法起点是在数值求解常微分方程的框架内。相应地,我们能够利用自适应步长控制过程来重新表述一般的牛顿型迭代方案。在此过程中,我们推导并讨论了离散的自适应求解方案,试图在不失去经典牛顿方法在正则解邻域内著名二次收敛特性的情况下,数值上模拟底层的连续问题。所提出自适应迭代方案的推导依赖于一个简单的正交投影论证,考虑到在足够接近正则解的地方,牛顿方案对应的向量场近似线性。我们通过几个低维例子测试并举例说明我们的自适应求根方案。基于所展示的例子,我们最后展示了一些性能数据。

英文摘要:

In this work we present and discuss a possible globalization concept for Newton-type methods. We consider nonlinear problems $f(x)=0$ in $\mathbb{R}^{n}$ using the concepts from ordinary differential equations as a basis for the proposed numerical solution procedure. Thus, the starting point of our approach is within the framework of solving ordinary differential equations numerically. Accordingly, we are able to reformulate general Newton-type iteration schemes using an adaptive step size control procedure. In doing so, we derive and discuss a discrete adaptive solution scheme thereby trying to mimic the underlying continuous problem numerically without losing the famous quadratic convergence regime of the classical Newton method in a vicinity of a regular solution. The derivation of the proposed adaptive iteration scheme relies on a simple orthogonal projection argument taking into account that, sufficiently close to regular solutions, the vector field corresponding to the Newton scheme is approximately linear. We test and exemplify our adaptive root-finding scheme using a few low-dimensional examples. Based on the presented examples, we finally show some performance data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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