# tf.linalg.qr

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Computes the QR decompositions of one or more matrices.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.linalg.qr`](https://www.tensorflow.org/api_docs/python/tf/linalg/qr), [`tf.compat.v1.qr`](https://www.tensorflow.org/api_docs/python/tf/linalg/qr)

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    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)

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| ## 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). |

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| ## 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`. |

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