pinv¶

paddle.linalg. pinv ( x, rcond=1e-15, hermitian=False, name=None ) [source]

Calculate pseudo inverse via SVD(singular value decomposition) of one matrix or batches of regular matrix.

\[if hermitian == False: x = u * s * vt (SVD) out = v * 1/s * ut else: x = u * s * ut (eigh) out = u * 1/s * u.conj().transpose(-2,-1)\]

If x is hermitian or symmetric matrix, svd will be replaced with eigh.

Parameters

• x (Tensor) – The input tensor. Its shape should be (*, m, n) where * is zero or more batch dimensions. m and n can be arbitraty positive number. The data type of x should be float32 or float64 or complex64 or complex128. When data type is complex64 or cpmplex128, hermitian should be set True.

• rcond (Tensor, optional) – the tolerance value to determine when is a singular value zero. Default:1e-15.

• hermitian (bool, optional) – indicates whether x is Hermitian if complex or symmetric if real. Default: False.

• name (str|None) – A name for this layer(optional). If set None, the layer will be named automatically.

Returns

The tensor with same data type with x. it represents pseudo inverse of x. Its shape should be (*, n, m).

Return type

Tensor

Examples

import paddle

x = paddle.arange(15).reshape((3, 5)).astype('float64')
input = paddle.to_tensor(x)
out = paddle.linalg.pinv(input)
print(input)
print(out)

# input:
# [[0. , 1. , 2. , 3. , 4. ],
# [5. , 6. , 7. , 8. , 9. ],
# [10., 11., 12., 13., 14.]]

# out:
# [[-0.22666667, -0.06666667,  0.09333333],
# [-0.12333333, -0.03333333,  0.05666667],
# [-0.02000000,  0.00000000,  0.02000000],
# [ 0.08333333,  0.03333333, -0.01666667],
# [ 0.18666667,  0.06666667, -0.05333333]]

# one can verify : x * out * x = x ;
# or              out * x * out = x ;