lu

paddle.linalg. lu ( x, pivot=True, get_infos=False, name=None ) [source]

Computes the LU factorization of an N-D(N>=2) matrix x.

Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and upper triangular matrix U are combined to a single LU matrix.

Pivoting is done if pivot is set to True. P mat can be get by pivots:

ones = eye(rows) #eye matrix of rank rows
for i in range(cols):
    swap(ones[i], ones[pivots[i]])
return ones
Parameters

  • X (Tensor) – the tensor to factor of N-dimensions(N>=2).

  • pivot (bool, optional) – controls whether pivoting is done. Default: True.

  • get_infos (bool, optional) – if set to True, returns an info IntTensor. Default: False.

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

factorization (Tensor), LU matrix, the factorization of input X.

pivots (IntTensor), the pivots of size(∗(N-2), min(m,n)). pivots stores all the intermediate transpositions of rows. The final permutation perm could be reconstructed by this, details refer to upper example.

infos (IntTensor, optional), if get_infos is True, this is a tensor of size (∗(N-2)) where non-zero values indicate whether factorization for the matrix or each minibatch has succeeded or failed.

Examples

>>> import paddle

>>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
>>> lu,p,info = paddle.linalg.lu(x, get_infos=True)

>>> print(lu)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5.        , 6.        ],
 [0.20000000, 0.80000000],
 [0.60000000, 0.50000000]])
>>> print(p)
Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True,
[3, 3])
>>> print(info)
Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
[0])

>>> P,L,U = paddle.linalg.lu_unpack(lu,p)

>>> print(P)
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0., 1., 0.],
 [0., 0., 1.],
 [1., 0., 0.]])
>>> print(L)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1.        , 0.        ],
 [0.20000000, 1.        ],
 [0.60000000, 0.50000000]])
>>> print(U)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5.        , 6.        ],
 [0.        , 0.80000000]])

>>> # one can verify : X = P @ L @ U ;