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

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

See the guide: Math > Matrix Math Functions

Computes the QR decompositions of one or more matrices.

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

# 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, 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. Orthonormal basis for range of a. If full_matrices is False then shape is [..., M, P]; if full_matrices is True then shape is [..., M, M]. * r: A Tensor. Has the same type as input. Triangular factor. If full_matrices is False then shape is [..., P, N]. If full_matrices is True then shape is [..., M, N].

Defined in tensorflow/python/ops/gen_linalg_ops.py.