inv(a)
Given a square matrix a, return the matrix :None:None:`ainv` satisfying dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])
.
Broadcasting rules apply, see the numpy.linalg
documentation for details.
Matrix to be inverted.
If a is not square or inversion fails.
Compute the (multiplicative) inverse of a matrix.
scipy.linalg.inv
Similar function in SciPy.
>>> from numpy.linalg import inv
... a = np.array([[1., 2.], [3., 4.]])
... ainv = inv(a)
... np.allclose(np.dot(a, ainv), np.eye(2)) True
>>> np.allclose(np.dot(ainv, a), np.eye(2)) True
If a is a matrix object, then the return value is a matrix as well:
>>> ainv = inv(np.matrix(a))
... ainv matrix([[-2. , 1. ], [ 1.5, -0.5]])
Inverses of several matrices can be computed at once:
>>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])See :
... inv(a) array([[[-2. , 1. ], [ 1.5 , -0.5 ]], [[-1.25, 0.75], [ 0.75, -0.25]]])
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._matfuncs.tanhm
scipy.linalg._matfuncs.coshm
scipy.linalg._matfuncs.sinhm
scipy.linalg._matfuncs.tanm
numpy.matrixlib.defmatrix.matrix.I
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