Oct
21

## Singular Value Decomposition

In linear algebra, the singular value decomposition (SVD) is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics. Applications which employ the SVD include computing the pseudoinverse, least squares fitting of data, matrix approximation, and determining the rank, range and null space of a matrix.

Singular Value Decomposition to solve ill conditioned square matrices.

Excel, C++ Add-in and Demo Spreadsheet with application manual and on-line help are at http://www.financial-risk-manager.com/risks/analytics/multivar/an_mv_t.html#svd

wiki(Singular value decomposition)

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Singular Value Decomposition to solve ill conditioned square matrices.

Excel, C++ Add-in and Demo Spreadsheet with application manual and on-line help are at http://www.financial-risk-manager.com/risks/analytics/multivar/an_mv_t.html#svd

wiki(Singular value decomposition)

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