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Bidiagonal matrix

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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:

and the following matrix is lower bidiagonal:

The eigenvalues of a bidiagonal matrix (of either type) are given by the entries of the diagonal.

For a square bidiagonal matrix B, the determinant is simply the product of its diagonal elements:

Additionally, the inverse of a nonsingular upper bidiagonal matrix B is an upper triangular matrix whose entries can be computed explicitly without solving full systems of linear equations.

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.

Reducing a matrix to bidiagonal form is a standard first step in the Golub-Kahan algorithm for computing the SVD. Because Householder reflections or Given rotations can reduce a dense m x n matrix to an equivalent bidiagonal matrix in O(mn2) operations, working with the bidiagonal structure drastically reduces the computational cost of subsequent iterative SVD and QR steps.[2]

Bidiagonalization

Bidiagonalization allows guaranteed accuracy when using floating-point arithmetic to compute singular values.[3]

See also

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