Note
This module is not imported by default. You need to import it to use it.
Return a triangular matrix square root of positive semi-definite x
L = cholesky(X, lower=True) implies dot(L, L.T) == X
Implements the “reverse-mode” gradient [1] for the Cholesky factorization of a positive-definite matrix.
[1] | S. P. Smith. “Differentiation of the Cholesky Algorithm”. Journal of Computational and Graphical Statistics, Vol. 4, No. 2 (Jun.,1995), pp. 134-147 http://www.jstor.org/stable/1390762 |
Generalized eigenvalues of a Hermetian positive definite eigensystem
Gradient of generalized eigenvalues of a Hermetian positive definite eigensystem
Compute the matrix exponential of a square array
Gradient of the matrix exponential of a square array.
Solve a system of linear equations
Kronecker product
Same as scipy.linalg.kron(a, b).
Note: | numpy.kron(a, b) != scipy.linalg.kron(a, b)! They don’t have the same shape and order when a.ndim != b.ndim != 2. |
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Parameters: |
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Returns: | array_like with a.ndim + b.ndim - 2 dimensions. |