svd
Computes the Singular Value Decomposition (SVD) of this matrix.
For a matrix A of dimensions m x n, the SVD factors A into: A = U * S * V^T
where:
U is an orthogonal matrix containing the left singular vectors:
If fullMatrix is true, U is m x m.
If fullMatrix is false, U is m x k, where k = min(m, n).
S contains the singular values in descending order:
If fullMatrix is true, S is an m x n diagonal matrix.
If fullMatrix is false, S is a 1 x k matrix containing the singular values.
V is an orthogonal matrix containing the right singular vectors:
If fullMatrix is true, V is n x n.
If fullMatrix is false, V is n x k, where k = min(m, n).
Return
A Triple containing (U, S, V) such that this matrix is equal to U * S * V.transposed().
Parameters
If true, full matrices U and V and diagonal matrix S are computed. If false, reduced (economy size) matrices are computed.