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Bidiagonal matrix

In mathematics, a bidiagonal matrix is a banded matrix with non-zero entries along the main diagonal and either the diagonal above or the diagonal below. This means there are exactly two non-zero diagonals in the matrix.

When the diagonal above the main diagonal has the non-zero entries the matrix is upper bidiagonal. When the diagonal below the main diagonal has the non-zero entries the matrix is lower bidiagonal.

For example, the following matrix is upper bidiagonal:

( 1 4 0 0 0 4 1 0 0 0 3 4 0 0 0 3 ) {\displaystyle {\begin{pmatrix}1&4&0&0\\0&4&1&0\\0&0&3&4\\0&0&0&3\\\end{pmatrix}}}

and the following matrix is lower bidiagonal:

( 1 0 0 0 2 4 0 0 0 3 3 0 0 0 4 3 ) . {\displaystyle {\begin{pmatrix}1&0&0&0\\2&4&0&0\\0&3&3&0\\0&0&4&3\\\end{pmatrix}}.}
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Usage

One variant of the QR algorithm starts with reducing a general matrix into a bidiagonal one,1 and the singular value decomposition (SVD) uses this method as well.

Bidiagonalization

Main article: Bidiagonalization

Bidiagonalization allows guaranteed accuracy when using floating-point arithmetic to compute singular values.2

See also

  • Stewart, G.W. (2001). Eigensystems. Matrix Algorithms. Vol. 2. Society for Industrial and Applied Mathematics. ISBN 0-89871-503-2.

References

  1. Anatolyevich, Bochkanov Sergey (2010-12-11). "Matrix operations and decompositions — Other operations on general matrices — SVD decomposition". ALGLIB User Guide, ALGLIB Project. Accessed: 2010-12-11. (Archived by WebCite at) https://www.alglib.net/matrixops/general/svd.php

  2. Fernando, K.V. (1 April 2007). "Computation of exact inertia and inclusions of eigenvalues (singular values) of tridiagonal (bidiagonal) matrices". Linear Algebra and Its Applications. 422 (1): 77–99. doi:10.1016/j.laa.2006.09.008. S2CID 122729700. https://doi.org/10.1016%2Fj.laa.2006.09.008