tf.linalg.qr

Computes the QR decompositions of one or more matrices.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.linalg.qr, tf.compat.v1.qr

tf.linalg.qr(
    input, full_matrices=False, name=None
)

Computes the QR decomposition of each inner matrix in tensor such that tensor[..., :, :] = q[..., :, :] * r[..., :,:])

Currently, the gradient for the QR decomposition is well-defined only when the first P columns of the inner matrix are linearly independent, where P is the minimum of M and N, the 2 inner-most dimmensions of tensor.

# a is a tensor.
# q is a tensor of orthonormal matrices.
# r is a tensor of upper triangular matrices.
q, r = qr(a)
q_full, r_full = qr(a, full_matrices=True)

Args
input	A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, N] whose inner-most 2 dimensions form matrices of size [M, N]. Let P be the minimum of M and N.
full_matrices	An optional bool. Defaults to False. If true, compute full-sized q and r. If false (the default), compute only the leading P columns of q.
name	A name for the operation (optional).

Returns
A tuple of Tensor objects (q, r).
q	A Tensor. Has the same type as input.
r	A Tensor. Has the same type as input.