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TensorFlow 1 version
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Computes the QR decompositions of one or more matrices.
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 | |
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input
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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.
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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).
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q
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A Tensor. Has the same type as input.
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r
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A Tensor. Has the same type as input.
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