scispace - formally typeset
Journal ArticleDOI

Linear least squares solutions by householder transformations

Peter A. Businger, +1 more
- 01 Jun 1965 - 
- Vol. 7, Iss: 3, pp 269-276
Reads0
Chats0
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.

read more

Citations
More filters
Journal ArticleDOI

Computer analysis of large structural systems.

TL;DR: Numerical procedure for structural systems analysis, discussing computer application to hydrodynamic, electric, magnetic, thermodynamic, elastostatic and elastodynamic problems.
Journal ArticleDOI

Algorithm 977: A QR--Preconditioned QR SVD Method for Computing the SVD with High Accuracy

TL;DR: The resulting procedure is numerically superior to xGESVD and that it is capable of reaching the accuracy of the Jacobi SVD, and can be used for accurate spectral decomposition of general (indefinite) Hermitian matrices.
Journal ArticleDOI

Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

TL;DR: This work studies a generalization that approximates the data with both selected and extracted features and comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm.
Book ChapterDOI

Chapter 6 A survey of matrix computations

TL;DR: This chapter presents three-level introduction to the field of matrix computations, including the language of matrix factorizations, the art of introducing zeros into a matrix, the exploitation of structure, and the distinction between problem sensitivity and algorithmic stability.
Proceedings ArticleDOI

A sparsity-aware QR decomposition algorithm for efficient cooperative localization

TL;DR: A Modified Householder QR algorithm is introduced which fully exploits the sparse structure of the Jacobian matrix H, and it is proved that the overall complexity of the EKF update, based on the QR factorization scheme, reduces to O(N3).
References
More filters
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.