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arXiv 2202.08830math.NAcs.NA
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用于多重网格V循环的最优多项式光滑器

Optimal polynomial smoothers for multigrid V-cycles

James Lottes

AI总结:

本文针对SPD系统多重网格V循环,推导了一般多项式光滑器的V循环界,发现第四类切比雪夫迭代准最优,而数值求解的最优多项式可将误差收缩因子界降低约18%。

摘要:

重新审视了在对称正定(SPD)系统的多重网格方法中,使用多项式方法来改进简单光滑器迭代的思想。当单步光滑器本身对应于一个SPD算子时,存在一种非常简单的迭代,它是切比雪夫半迭代方法的近亲,基于第四类而非第一类切比雪夫多项式,该迭代优化了可追溯到Hackbusch的两层界。利用McCormick的V循环理论,推导了一般多项式光滑器的完整V循环界。第四类切比雪夫迭代对于V循环界是准最优的。V循环界的最优多项式可以通过数值方法找到,随着光滑步数趋于无穷,其误差收缩因子界比第四类切比雪夫迭代低约18%。讨论了优化迭代的实现,并通过一个简单的数值例子说明了多项式光滑器的性能。

英文摘要:

The idea of using polynomial methods to improve simple smoother iterations within a multigrid method for a symmetric positive definite (SPD) system is revisited. When the single-step smoother itself corresponds to an SPD operator, there is in particular a very simple iteration, a close cousin of the Chebyshev semi-iterative method, based on the Chebyshev polynomials of the fourth instead of first kind, that optimizes a two-level bound going back to Hackbusch. A full V-cycle bound for general polynomial smoothers is derived using the V-cycle theory of McCormick. The fourth-kind Chebyshev iteration is quasi-optimal for the V-cycle bound. The optimal polynomials for the V-cycle bound can be found numerically, achieving an about 18% lower error contraction factor bound than the fourth-kind Chebyshev iteration, asymptotically as the number of smoothing steps goes to infinity. Implementation of the optimized iteration is discussed, and the performance of the polynomial smoothers are illustrated with a simple numerical example.

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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