# svd_test

svd_test a MATLAB code which calls svd(), which computes the singular value decomposition of a matrix.

The singular value decomposition has uses in solving overdetermined or underdetermined linear systems, linear least squares problems, data compression, the pseudoinverse matrix, reduced order modeling, and the accurate computation of matrix rank and null space.

The singular value decomposition of an M by N rectangular matrix A has the form

```        A(mxn) = U(mxm) * S(mxn) * V'(nxn)
```
where
• U is an orthogonal matrix, whose columns are the left singular vectors;
• S is a diagonal matrix, whose min(m,n) diagonal entries are the singular values;
• V is an orthogonal matrix, whose columns are the right singular vectors;
Note that the transpose of V is used in the decomposition, and that the diagonal matrix S is typically stored as a vector.

### Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

### Languages:

svd_test is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version and a Python version.

### Related Data and Programs:

fingerprints, a dataset directory which contains a few images of fingerprints.

svd_basis, a MATLAB code which computes a reduced basis for a collection of data vectors using the svd.

svd_fingerprint, a MATLAB code which reads a file containing a fingerprint image and uses the singular value decomposition (svd) to compute and display a series of low rank approximations to the image.

svd_gray, a MATLAB code which reads a gray scale image, computes the singular value decomposition (svd), and constructs a series of low rank approximations to the image.

svd_snowfall, a MATLAB code which reads a file containing historical snowfall data and analyzes the data with the singular value decomposition (svd).

svd_truncated_test, a MATLAB code which demonstrates the computation of the reduced or truncated singular value decomposition (svd) that is useful for cases when one dimension of the matrix is much smaller than the other.

### Reference:

1. Edward Anderson, Zhaojun Bai, Christian Bischof, Susan Blackford, James Demmel, Jack Dongarra, Jeremy Du Croz, Anne Greenbaum, Sven Hammarling, Alan McKenney, Danny Sorensen,
LAPACK User's Guide,
Third Edition,
SIAM, 1999,
ISBN: 0898714478,
LC: QA76.73.F25L36
2. Jack Dongarra, Jim Bunch, Cleve Moler, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979,
ISBN13: 978-0-898711-72-1,
LC: QA214.L56.
3. Gene Golub, Charles VanLoan,
Matrix Computations, Third Edition,
Johns Hopkins, 1996,
ISBN: 0-8018-4513-X,
LC: QA188.G65.
4. David Kahaner, Cleve Moler, Steven Nash,
Numerical Methods and Software,
Prentice Hall, 1989,
ISBN: 0-13-627258-4,
LC: TA345.K34.
5. Lloyd Trefethen, David Bau,
Numerical Linear Algebra,
SIAM, 1997,
ISBN: 0-89871-361-7,
LC: QA184.T74.

### Source Code:

Last revised on 17 June 2021.