cr.sparse.la.row_space¶
- cr.sparse.la.row_space(A, rcond=None)[source]¶
Constructs an orthonormal basis for the row space of A using SVD
- Parameters
A (jax.numpy.ndarray) – Input matrix of size (M, N) where M is the dimension of the ambient vector space and N is the number of vectors in A
rcond (float) – Relative condition number. Singular values
s
smaller thanrcond * max(s)
are considered zero. Default: floating point eps * max(M,N).
- Returns
- Returns a tuple consisting of
the right singular vectors of A
the effective rank of A
- Return type
To get the ONB for the row space, follow the two step process:
Q, r = orth(A) Q = Q[:, :r]
Examples
>>> A = jnp.array([[2, 0, 0], [0, 5, 0]]).T >>> print(A) [[2 0] [0 5] [0 0]] >>> Q, rank = crla.row_space(A) >>> print(Q[:, :rank]) [[0. 1.] [1. 0.]]