RWLeastSqSVDCalc
Class RWLeastSqSVDCalc employs singular value decomposition (SVD). The method solves the least squares problem by decomposing the regression matrix into the form 
, where P is an 
matrix consisting of p orthonormalized eigenvectors associated with the p largest eigenvalues of 
, Q is a 
orthogonal matrix consisting of the orthonormalized eigenvectors of , and Σ = diag(σ1, σ2, ... , σp) is a 
diagonal matrix of singular values of X. This singular value decomposition of X is used to solve the equation in Calculation Methods for Linear Regression .
, where P is an matrix consisting of p orthonormalized eigenvectors associated with the p largest eigenvalues of , Q is a orthogonal matrix consisting of the orthonormalized eigenvectors of , and Σ = diag(σ1, σ2, ... , σp) is a diagonal matrix of singular values of X. This singular value decomposition of X is used to solve the equation in Calculation Methods for Linear Regression .Pros: | Works on matrices of less than full rank. Produces accurate results when X has full rank, but is highly ill-conditioned. |
Cons: | Slower than the straight QR technique described in RWLeastSqQRCalc. |
