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arXiv 2003.09362math.NAcs.NA
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Lanczos方法的一致误差估计

Uniform Error Estimates for the Lanczos Method

John C. Urschel

摘要:

Lanczos方法是求解极值对称本征值问题的最有力和最基本的方法之一。基于收敛的误差估计在很大程度上依赖于特征值间隙。在实践中,这个差距往往相对较小,导致错误的显着高估。避免此问题的一种方法是通过使用一致误差估计,即仅取决于矩阵的维度和迭代次数的界限。在这项工作中,我们证明了明确的上下一致误差估计的Lanczos方法。这些下界,与数值结果配对,意味着在所有$n \times n$对称矩阵上的Lanczos方法的$m$迭代的最大误差确实取决于实际中的维度$n$。改进的极值特征值的界立即转化为对称正定矩阵条件数的误差估计。此外,我们证明了更具体的结果,矩阵具有一定程度的特征值的规律性或其特征值收敛到一些限制经验谱分布。通过数值实验,我们表明,本文的理论估计适用于实际计算的合理大小的矩阵。

英文摘要:

The Lanczos method is one of the most powerful and fundamental techniques for solving an extremal symmetric eigenvalue problem. Convergence-based error estimates depend heavily on the eigenvalue gap. In practice, this gap is often relatively small, resulting in significant overestimates of error. One way to avoid this issue is through the use of uniform error estimates, namely, bounds that depend only on the dimension of the matrix and the number of iterations. In this work, we prove explicit upper and lower uniform error estimates for the Lanczos method. These lower bounds, paired with numerical results, imply that the maximum error of $m$ iterations of the Lanczos method over all $n \times n$ symmetric matrices does indeed depend on the dimension $n$ in practice. The improved bounds for extremal eigenvalues translate immediately to error estimates for the condition number of a symmetric positive definite matrix. In addition, we prove more specific results for matrices that possess some level of eigenvalue regularity or whose eigenvalues converge to some limiting empirical spectral distribution. Through numerical experiments, we show that the theoretical estimates of this paper do apply to practical computations for reasonably sized matrices.

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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