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SVD_DEMO\ Demonstration of the Singular Value Decomposition {#svd_demo-demonstration-of-the-singular-value-decomposition align=”center”} =================================================

SVD_DEMO is a C++ program which demonstrates the computation of the singular value decomposition and a few of its properties.

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.

Usage: {#usage align=”center”}

svd_demo m n seed

where

• m is the number of rows in the random matrix;
• n is the number of columns in the random matrix;
• seed is an optional seed for the random number generator.

Licensing: {#licensing align=”center”}

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

Languages: {#languages align=”center”}

SVD_DEMO is available in a C version and a C++ version and a FORTRAN77 version and a FORTRAN90 version and a MATLAB version.

Related Data and Programs: {#related-data-and-programs align=”center”}

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

LAPACK_EXAMPLES, a FORTRAN90 program which demonstrates the use of the LAPACK linear algebra library.

LINPACK, a C++ library which includes routines to carry out the singular value decomposition.

SVD_BASIS, a C++ program which computes a reduced basis for a collection of data vectors using the SVD.

SVD_SNOWFALL, a C++ library which reads a file containing historical snowfall data and analyzes the data with the Singular Value Decomposition (SVD), and plots created by GNUPLOT.

SVD_TRUNCATED, a C++ program 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.

TOMS358, a FORTRAN77 routine which computes the singular value decomposition for a complex matrix.

Reference: {#reference align=”center”}

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: {#source-code align=”center”}

• svd_demo.cpp, the source code.

Examples and Tests: {#examples-and-tests align=”center”}

• svd_demo_5_3_output.txt, the output from the command “svd_demo 5 3 123456789”.
• svd_demo_5_5_output.txt, the output from the command “svd_demo 5 5 123456789”.
• svd_demo_5_8_output.txt, the output from the command “svd_demo 5 8 123456789”.

List of Routines: {#list-of-routines align=”center”}

• MAIN is the main program for SVD_DEMO.
• GET_SEED returns a random seed for the random number generator.
• GET_SVD_LINPACK gets the SVD of a matrix using a call to LINPACK.
• PSEUDO_INVERSE computes the pseudoinverse.
• PSEUDO_LINEAR_SOLVE_TEST uses the pseudoinverse for linear systems.
• PSEUDO_PRODUCT_TEST examines pseudoinverse products.
• R8_NINT returns the nearest integer to a double precision value.
• R8MAT_DIF_FRO returns the Frobenius norm of the difference of R8MAT’s.
• R8MAT_NORM_FRO returns the Frobenius norm of a R8MAT.
• R8MAT_PRINT prints an R8MAT.
• R8MAT_PRINT_SOME prints some of an R8MAT.
• R8VEC_NORM_L2 returns the L2 norm of an R8VEC.
• R8MAT_UNIFORM_01 returns a unit pseudorandom R8MAT.
• R8VEC_UNIFORM_01 returns a unit pseudorandom R8VEC.
• RANK_ONE_PRINT_TEST prints the sums of rank one matrices.
• RANK_ONE_TEST compares A to the sum of rank one matrices.
• S_LEN_TRIM returns the length of a string to the last nonblank.
• SVD_PRODUCT_TEST tests that A = U * S * V’.
• TIMESTAMP prints the current YMDHMS date as a time stamp.

You can go up one level to the C++ source codes.

Last revised on 17 June 2012.