Symmetric eigenvalue decomposition and the SVD

Eigendecomposition of a symmetric matrix or the singular value decomposition of an arbitrary matrix
Updated 23 May 2012

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This submission contains functions for computing the eigenvalue decomposition of a symmetric matrix (QDWHEIG.M) and the singular value decomposition (QDWHSVD.M) by efficient and stable algorithms based on spectral divide-and-conquer. The computed results tend to be more accurate than those given by MATLAB's built-in functions EIG.M and SVD.M.

Function TEST.M runs a simple test of the codes.

Details on the underlying algorithms can be found in

Y. Nakatsukasa and N. J. Higham. Stable and Efficient Spectral Divide and Conquer Algorithms for the Symmetric Eigenvalue Decomposition and the SVD. MIMS EPrint 2012.52, The University of Manchester, May 2012.

Cite As

Yuji Nakatsukasa (2024). Symmetric eigenvalue decomposition and the SVD (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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