scipy.linalg.interpolative.svd¶

scipy.linalg.interpolative.svd(A, eps_or_k, rand=True)[source]

Compute SVD of a matrix via an ID.

An SVD of a matrix A is a factorization:

A = numpy.dot(U, numpy.dot(numpy.diag(S), V.conj().T))


where U and V have orthonormal columns and S is nonnegative.

The SVD can be computed to any relative precision or rank (depending on the value of eps_or_k).