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Journal ArticleDOI

Linear least squares solutions by householder transformations

Peter A. Businger, +1 more
- 01 Jun 1965 - 
- Vol. 7, Iss: 3, pp 269-276
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TLDR
In this paper, the euclidean norm is unitarily invariant and a vector x is determined such that x is parallel b-Ax parallel = \parallel c - QAx parallel where c denotes the first n components of c.
Abstract
Let A be a given m×n real matrix with m≧n and of rank n and b a given vector. We wish to determine a vector x such that $$\parallel b - A\hat x\parallel = \min .$$ where ∥ … ∥ indicates the euclidean norm. Since the euclidean norm is unitarily invariant $$\parallel b - Ax\parallel = \parallel c - QAx\parallel $$ where c=Q b and Q T Q = I. We choose Q so that $$QA = R = {\left( {_{\dddot 0}^{\tilde R}} \right)_{\} (m - n) \times n}}$$ (1) and R is an upper triangular matrix. Clearly, $$\hat x = {\tilde R^{ - 1}}\tilde c$$ where c denotes the first n components of c.

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Citations
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A note on subset selection for matrices

TL;DR: In this paper, the authors gave a constructive proof and moreover a sharper bound for non-singularity of a matrix and showed that it is possible to select subsets of the matrix that are not as singular as possible in a numerical sense.
Journal ArticleDOI

Deviation maximization for rank-revealing QR factorizations

- 05 Apr 2022 - 
TL;DR: In this article , a column selection strategy, named deviation maximization, is introduced to compute rank-revealing QR factorizations as an alternative to the well-known block version of the QR factorization with the column pivoting method, called QP3 and currently implemented in LAPACK.
Book ChapterDOI

Least squares solution of weighted linear systems by G-transformations

TL;DR: Based on permutations and scaling of orthogonal Hessenberg matrices, a family of algorithms (the G- and partial G-algorithms) are presented in this paper which solve the weighted least squares problem (Ax−b)W(Ax −b)=ρ2 for x such that ρ2 is minimal.
Posted Content

Sensor Selection With Cost Constraints for Dynamically Relevant Bases

TL;DR: This work considers cost-constrained sparse sensor selection for full-state reconstruction, applying a well-known greedy algorithm to dynamical systems for which the usual singular value decomposition (SVD) basis may not be available or preferred.
References
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Journal ArticleDOI

Unitary Triangularization of a Nonsymmetric Matrix

TL;DR: This note points out that the same result can be obtained with fewer arithmetic operations, and, in particular, for inverting a square matrix of order N, at most 2(N-1) square roots are required.