拉格朗日基础中的全正和最小二乘问题

IF 1.8 3区 数学 Q1 MATHEMATICS Numerical Linear Algebra with Applications Pub Date : 2024-03-08 DOI:10.1002/nla.2554
Ana Marco, José-Javier Martínez, Raquel Viaña
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引用次数: 0

摘要

本研究解决了在标准拉格朗日基础上的多项式最小二乘法拟合问题。虽然相应的超定线性系统中涉及的矩阵并非全正,但在构建解决所考虑问题的精确算法时,使用了矩形全正拉格朗日-凡德蒙矩阵,以利用全正性的优势。特别是,计算这种矩形全正矩阵的对角线分解的快速而精确的算法对解决问题至关重要。这种算法还能精确计算摩尔-彭罗斯逆和这些问题所涉及的配位矩阵的投影矩阵。数值实验显示了所提算法的良好性能。
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Total positivity and least squares problems in the Lagrange basis
The problem of polynomial least squares fitting in the standard Lagrange basis is addressed in this work. Although the matrices involved in the corresponding overdetermined linear systems are not totally positive, rectangular totally positive Lagrange-Vandermonde matrices are used to take advantage of total positivity in the construction of accurate algorithms to solve the considered problem. In particular, a fast and accurate algorithm to compute the bidiagonal decomposition of such rectangular totally positive matrices is crucial to solve the problem. This algorithm also allows the accurate computation of the Moore-Penrose inverse and the projection matrix of the collocation matrices involved in these problems. Numerical experiments showing the good behaviour of the proposed algorithms are included.
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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