Gram-Schmidt orthogonalization
Any set of linearly independent vectors v1,…,vn can be converted into a set of orthogonal vectors
q1,…,qn by the Gram-Schmidt process
. In three dimensions
, v1 determines a line; the vectors v1 and v2 determine a plane. The vector q1 is the unit vector
in the direction v1. The (unit) vector q2 lies in the plane of v1,v2, and is normal to v1 (on the same side as v2. The (unit) vector q3 is normal to the plane of v1,v2, on the same side as v3, etc.
In general, first set u1=v1, and then each ui is made orthogonal to the preceding u1,…ui-1 by subtraction of the projections of vi in the directions of u1,…,ui-1 :
ui=vi-i-1∑j=1uTjviuTjujuj |
The i vectors ui span the same subspace as the vi. The vectors qi=ui/||ui|| are orthonormal. This leads to the following theorem:
Theorem.
Any m×n matrix A with linearly independent columns can be factorized into a product, A=QR. The columns of Q are orthonormal and R is upper triangular and invertible.
This “classical” Gram-Schmidt method is often numerically unstable, see [Golub89] for a “modified” Gram-Schmidt method.
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Originally from The Data Analysis Briefbook (http://rkb.home.cern.ch/rkb/titleA.htmlhttp://rkb.home.cern.ch/rkb/titleA.html)
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Golub89
Gene H. Golub and Charles F. van Loan: Matrix Computations, 2nd edn., The John Hopkins University Press, 1989.
Title | Gram-Schmidt orthogonalization |
Canonical name | GramSchmidtOrthogonalization |
Date of creation | 2013-03-22 12:06:14 |
Last modified on | 2013-03-22 12:06:14 |
Owner | akrowne (2) |
Last modified by | akrowne (2) |
Numerical id | 9 |
Author | akrowne (2) |
Entry type | Algorithm |
Classification | msc 65F25 |
Synonym | Gram-Schmidt decomposition |
Synonym | Gram-Schmidt orthonormalization |
Synonym | Gram-Schmidt process |
Related topic | HouseholderTransformation |
Related topic | GivensRotation |
Related topic | QRDecomposition |
Related topic | AnExampleForSchurDecomposition |