SolveS(M, V) = Solve for minimum vector U where matrix M * U = V


SolveS(M, V) = Solve for minimum vector U where matrix M * U = V:

Uses the Singular Value Decomposition SVD method and can solve linear equations with a singular matrix M. There can be more, the same, or fewer equations than unknowns. If M is singular, the solution with the smallest length is given. The output also contains information like:

Singular, Rank = 2, Residual = 0.0 (False)

The residual will be the length of the vector r = M * U − V. The solution minimizes this residue. If the residual is not zero, this is a least square solution.

See: Complex SVD -- Numerical Recipes Forum

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Changes last made on Monday, 06-Aug-07 20:22:57 PDT

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