LSE: Constrained Least Squares and Generalized QR Factorization

The solution of equality constrained least squares problem (LSE) is given through four analytics methods (Generalized QR Factorization, Lagrange Multipliers, Direct Elimination and Null Space method). We expose the orthogonal decomposition called Generalized QR Factorization (GQR) and also RQ factorization. Finally some codes for the solution of LSE applied in quaternions.

Version:	1.0.0
Imports:	MASS, pracma
Published:	2022-02-02
Author:	Sergio Andrés Cabrera Miranda <https://orcid.org/0000-0002-8126-8521>, Juan Gabriel Triana Laverde <https://orcid.org/0000-0003-2991-6082>
Maintainer:	Sergio Andrés Cabrera Miranda <sergio05acm at gmail.com>
License:	GPL-3
URL:	https://github.com/sergio05acm/LSE
NeedsCompilation:	no
CRAN checks:	LSE results

Documentation:

Reference manual:

LSE.pdf

Downloads:

Package source:	LSE_1.0.0.tar.gz
Windows binaries:	r-devel: LSE_1.0.0.zip, r-release: LSE_1.0.0.zip, r-oldrel: LSE_1.0.0.zip
macOS binaries:	r-release (arm64): LSE_1.0.0.tgz, r-oldrel (arm64): LSE_1.0.0.tgz, r-release (x86_64): LSE_1.0.0.tgz, r-oldrel (x86_64): LSE_1.0.0.tgz

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