Cholesky Decomposition-powered Functions

This module contains algebraic functions powered by the Cholesky matrix decomposition (as provided by the <Eigen/Cholesky> include).

ceygen.llt.cholesky(x[, out=None])

Compute Cholesky decomposition of matrix x (which must be square, Hermitian and positive-definite) so that x = out * out.H (out.H being conjugate transpose of out)

Parameters:

• x (nonint_dtype[:, :]) – matrix to decompose
• out (nonint_dtype[:, :]) – memory view to write the result to. Specifying this optional argument means that Ceygen doesn’t have to allocate memory for the result (allocating memory involves acquiring the GIL and calling many expensive Python functions). Once specified, it must must have correct dimensions to store the result of this operation (otherwise you get ValueError); the same out instance will be also returned. Warning: don’t repeat x (or y) here, it would give incorrect result without any error. Perhaps there’s an in-place variant instead?

Raises :

ValueError if argument dimensions aren’t appropriate for this operation or if arguments are otherwise invalid.

Raises :

TypeError if you pass an argument that doesn’t support buffer interface (e.g. a plain list). Use preferrably a Cython memoryview and resort to Python array, Cython array or a NumPy array.

Return type:

nonint_dtype[:, :]