- scipy.linalg.pinv(a, atol=None, rtol=None, return_rank=False, check_finite=True, cond=None, rcond=None)#
Compute the (Moore-Penrose) pseudo-inverse of a matrix.
Calculate a generalized inverse of a matrix using its singular-value decomposition
U @ S @ Vin the economy mode and picking up only the columns/rows that are associated with significant singular values.
sis the maximum singular value of
a, then the significance cut-off value is determined by
atol + rtol * s. Any singular value below this value is assumed insignificant.
- a(M, N) array_like
Matrix to be pseudo-inverted.
- atolfloat, optional
Absolute threshold term, default value is 0.
New in version 1.7.0.
- rtolfloat, optional
Relative threshold term, default value is
max(M, N) * epswhere
epsis the machine precision value of the datatype of
New in version 1.7.0.
- return_rankbool, optional
If True, return the effective rank of the matrix.
- check_finitebool, optional
Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
- cond, rcondfloat, optional
In older versions, these values were meant to be used as
rtol=0. If both were given
condand hence the code was not correct. Thus using these are strongly discouraged and the tolerances above are recommended instead. In fact, if provided, atol, rtol takes precedence over these keywords.
Changed in version 1.7.0: Deprecated in favor of
atolparameters above and will be removed in future versions of SciPy.
Changed in version 1.3.0: Previously the default cutoff value was just
1e3for single precision and
1e6for double precision.
- B(N, M) ndarray
The pseudo-inverse of matrix a.
The effective rank of the matrix. Returned if return_rank is True.
If SVD computation does not converge.
Moore-Penrose pseudoinverse of a hermititan matrix.
Ais invertible then the Moore-Penrose pseudoinverse is exactly the inverse of
Ais not invertible then the Moore-Penrose pseudoinverse computes the
Ax = bsuch that
||Ax - b||is minimized .
m x nmatrix
n x mmatrix
Bthe four Moore-Penrose conditions are:
ABA = A(
Bis a generalized inverse of
BAB = B(
Ais a generalized inverse of
(AB)* = AB(
(BA)* = BA(
BAis hermitian) .
A*denotes the conjugate transpose. The Moore-Penrose pseudoinverse is a unique
Bthat satisfies all four of these conditions and exists for any
A. Note that, unlike the standard matrix inverse,
Adoes not have to be square or have independant columns/rows.
As an example, we can calculate the Moore-Penrose pseudoinverse of a random non-square matrix and verify it satisfies the four conditions.
>>> import numpy as np >>> from scipy import linalg >>> rng = np.random.default_rng() >>> A = rng.standard_normal((9, 6)) >>> B = linalg.pinv(A) >>> np.allclose(A @ B @ A, A) # Condition 1 True >>> np.allclose(B @ A @ B, B) # Condition 2 True >>> np.allclose((A @ B).conj().T, A @ B) # Condition 3 True >>> np.allclose((B @ A).conj().T, B @ A) # Condition 4 True