# tensorplay.functional.lu_unpack

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.lu_unpack.html

```python
tensorplay.functional.lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True, *, out=None)
```

Unpacks the LU decomposition returned by lu_factor() into the P, L, U matrices.

> **See also**
>
> lu() returns the matrices from the LU decomposition. Its gradient formula is more efficient than that of doing lu_factor() followed by lu_unpack().

Parameters:

- LU_data ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the packed LU factorization data

- LU_pivots ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the packed LU factorization pivots

- unpack_data ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – flag indicating if the data should be unpacked. If False, then the returned L and U are empty tensors. Default: True

- unpack_pivots ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – flag indicating if the pivots should be unpacked into a permutation matrix P. If False, then the returned P is an empty tensor. Default: True

Keyword Arguments:

out ([tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple), optional) – output tuple of three tensors. Ignored if None.

Returns:

A namedtuple (P, L, U)

Examples:

```
>>> A = tensorplay.randn(2, 3, 3)
>>> LU, pivots = tensorplay.linalg.lu_factor(A)
>>> P, L, U = tensorplay.lu_unpack(LU, pivots)
>>> # We can recover A from the factorization
>>> A_ = P @ L @ U
>>> tensorplay.allclose(A, A_)
True

>>> # LU factorization of a rectangular matrix:
>>> A = tensorplay.randn(2, 3, 2)
>>> LU, pivots = tensorplay.linalg.lu_factor(A)
>>> P, L, U = tensorplay.lu_unpack(LU, pivots)
>>> # P, L, U are the same as returned by linalg.lu
>>> P_, L_, U_ = tensorplay.linalg.lu(A)
>>> tensorplay.allclose(P, P_) and tensorplay.allclose(L, L_) and tensorplay.allclose(U, U_)
True
```