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arXiv math/0503100math.NAcs.NA
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理解数值稳定性的一个模型

A Model for Understanding Numerical Stability

Folkmar Bornemann

AI总结:

提出一个结合简洁性与预测能力的舍入误差分析模型,通过非平凡例子(如带单步迭代细化的高斯消元)展示其能正确预测算法稳定性并给出定量后向误差界。

摘要:

我们提出了一个舍入误差分析模型,它结合了简洁性与预测能力。尽管没有考虑算法中所有舍入误差来源,该模型与递归舍入误差分析相关,因此能够正确预测算法的稳定性或不稳定性。通过非平凡的例子,例如带单步迭代细化的高斯消元的逐分量后向稳定性分析,我们证明该模型甚至能给出定量后向误差界,这些误差界显示了所有已知的问题相关项(除了维度相关常数,这是任何先验分析的弱点)。该模型可作为教学中的便捷工具,或在进入进一步详细分析之前作为发现稳定性结果的启发式手段。

英文摘要:

We present a model of roundoff error analysis that combines simplicity with predictive power. Though not considering all sources of roundoff within an algorithm, the model is related to a recursive roundoff error analysis and therefore capable of correctly predicting stability or instability of an algorithm. By means of nontrivial examples, such as the componentwise backward stability analysis of Gaussian elimination with a single iterative refinement step, we demonstrate that the model even yields quantitative backward error bounds that show all the known problem-dependent terms (with the exception of dimension-dependent constants, which are the weak spot of any a priori analysis). The model can serve as a convenient tool for teaching or as a heuristic device to discover stability results before entering a further, detailed analysis.

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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