skfolio.utils.stats.safe_cholesky#

skfolio.utils.stats.safe_cholesky(covariance, ridge_scale=1e-12, max_tries=3)[source]#

Compute a Cholesky factor \(L\) from covariance \(\Sigma\).

Fast path: try plain Cholesky on the input as-is. Fallback: symmetrize and add ridge \(\lambda I\) with escalation until SPD:

\[\Sigma_{reg} = (\Sigma + \Sigma^T)/2 + \lambda I \approx L L^T\]

Parameters:

covariancendarray of shape (n_assets, n_assets): Covariance matrix \(\Sigma\).
ridge_scalefloat, default=1e-12: Relative ridge size, as a fraction of the average absolute covariance diagonal. If that scale is zero, a positive numerical floor is used.
max_triesint, default=3: Maximum number of ridge escalations before raising an error.

Returns:

cholndarray of shape (n_assets, n_assets): Lower triangular Cholesky factor \(L\) such that \(\Sigma \approx L L^T\).

Raises: