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Accurate solutions of weighted least squares problems associated with rank-structured matrices

机译:与秩结构矩阵相关的加权最小二乘问题的精确解

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In this paper, we consider how to accurately solve the weighted least squares (WLS) problem associated with a class of rank-structured matrices admitting bidiagonal representations (BRs) from weighted polynomial regression. We develop a new algorithm to solve the structured WLS problem, provided that BRs are available. A mechanism is exploited to guarantee that all the solution components are computed with a desirable high accuracy. Forward error analysis and numerical experiments are performed to confirm the high accuracy. In particular, our random examples demonstrate the high relative accuracy of each computed solution component, independently of the ill-conditioning of weighted matrices. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.
机译:在本文中,我们考虑如何从加权多项式回归中准确解决与一类允许双对角表示(BR)的秩结构矩阵相关的加权最小二乘(WLS)问题。如果BR可用,我们将开发一种新的算法来解决结构化的WLS问题。利用一种机制来保证以理想的高精度计算所有解分量。进行前向误差分析和数值实验以确认高精度。尤其是,我们的随机示例证明了每个计算出的解决方案分量的相对精度都很高,而与加权矩阵的不良条件无关。 (C)2019年IMACS。由Elsevier B.V.发布。保留所有权利。

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