The operator that performs SVD

svd.compute(A)
svd.compute(A, 'OptionName', optionValue, ...)

Input

• A decomposed matrix (floating-point type), A = u*diag(w)*vt

Options

• Flags operation flags. default 0
• ModifyA allow the algorithm to modify the decomposed matrix; it can save space and speed up processing. Currently ignored. default false
• NoUV indicates that only a vector of singular values w is to be processed, while u and vt will be set to empty matrices. default false
• FullUV when the matrix is not square, by default the algorithm produces u and vt matrices of sufficiently large size for the further A reconstruction; if, however, FullUV flag is specified, u and vt will be full-size square orthogonal matrices. default false

The previously allocated u, w and vt are released.

The operator performs the singular value decomposition of the supplied matrix. The u, vt, and the vector of singular values w are stored in the structure. The same SVD structure can be reused many times with different matrices. Each time, if needed, the previous u, vt, and w are reclaimed and the new matrices are created.