tf.linalg.qr | TensorFlow v2.16.1 (original) (raw)
Computes the QR decompositions of one or more matrices.
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Compat aliases for migration
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tf.compat.v1.linalg.qr, tf.compat.v1.qr
tf.linalg.qr(
input: Annotated[Any, TV_Qr_T], full_matrices: bool = False, name=None
)
Computes the QR decomposition of each inner matrix in tensor
such thattensor[..., :, :] = 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, whereP
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. |