SVD(M) = Singular Value Decomposition, M = U * W * V^H


SVD(M) = Singular Value Decomposition, M = U * W * V^H:

Singular Value Decomposition, M = U * W * V^H where V^H = Conj(Tran(V)). W is a diagonal matrix and is returned as a row vector. The number of rows in M should be >= the number of columns in M, if smaller, M will be filled with zero rows. This function returns U, W, and V. The elements of W will always be real, non-negative and sorted in ascending order.

If there are complex elements in M, the ACM Algorithm 358 for Singular Value Decomposition of a complex matrix is used.

See: Complex SVD -- Numerical Recipes Forum
See: TOMS358 SUBROUTINE CSVD -- From John Burkardt, Florida State University
See: TOMS358_PRB tests the CSVD routine -- From John Burkardt, Florida State University

SVD(M) can be performed by my programs >>> Algo358 >>> XMCalc and >>> XZCalc

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Changes last made on Wednesday, 24-Sep-08 08:13:52 PDT

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