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
Return to Matrix and Polynomial Computations
Return to Harry's Home Page
This page accessed 
times since
September 18, 2006.
Page created by: hjsmithh@sbcglobal.net
Changes last made on Monday, 06-Aug-07 20:22:57 PDT