mindspore.ops.lu_unpack

mindspore.ops.lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True)

Unpack the LU decomposition returned by mindspore.scipy.linalg.lu_factor() into the P, L, U matrices.

Note

• LU_data of shape $(\ast ,M,N)$ , LU_pivots of shape $(\ast ,min(M,N))$ , where $\ast$ is batch dimensions.

Parameters

• LU_data (Tensor) – The packed LU factorization data, the rank is greater than or equal to 2.

• LU_pivots (Tensor) – The packed LU factorization pivots.

• unpack_data (bool, optional) – A flag indicating if the LU_data should be unpacked. If False , then the returned L and U are None. Default True .

• unpack_pivots (bool, optional) – A flag indicating if the LU_pivots should be unpacked into a permutation matrix P. If False , then the returned P is None. Default True .

Returns

Tuple of tensors(Pivots, L, U).

Supported Platforms:

GPU CPU

Examples

>>> import mindspore
>>> LU_data = mindspore.tensor([[[-0.3806, -0.4872,  0.5536],
...                             [-0.1287,  0.6508, -0.2396],
...                             [ 0.2583,  0.5239,  0.6902]],
...                            [[ 0.6706, -1.1782,  0.4574],
...                             [-0.6401, -0.4779,  0.6701],
...                             [ 0.1015, -0.5363,  0.6165]]])
>>> LU_pivots = mindspore.tensor(([[1, 3, 3], [2, 3, 3]]), mindspore.int32)
>>> pivots, L, U = mindspore.ops.lu_unpack(LU_data, LU_pivots)
>>> print(pivots)
[[[1. 0. 0.]
  [0. 0. 1.]
  [0. 1. 0.]]
 [[0. 0. 1.]
  [1. 0. 0.]
  [0. 1. 0.]]]
>>> print(L)
[[[ 1.       0.       0.]
  [-0.1287   1.       0.]
  [ 0.2583   0.5239   1.]]
 [[ 1.0000   0.       0.]
  [-0.6401   1.       0.]
  [ 0.1015  -0.5363   1.]]]
>>> print(U)
[[[-0.3806  -0.4872   0.5536]
  [ 0.       0.6508  -0.2396]
  [ 0.       0.       0.6902]]
 [[ 0.6706  -1.1782   0.4574]
  [ 0.      -0.4779   0.6701]
  [ 0.       0.       0.6165]]]