\returns A const reference to the vectors containing the alpha values * * This vector permits to reconstruct the j-th eigenvalues as alpha

\returns A const reference to the vectors containing the beta values * * This vector permits to reconstruct the j-th eigenvalues as alphas

\returns the number of columns grouped by blocks */

* \returns the starting position of the block \p id in the array of values */

* Number of columns */

Apply V -> VQ and compute the new f. Should be called after compress_H(), since m_k is updated there. Only need to update the first k+1 columns of V T

Conducts the major computation procedure. \param selection An enumeration value indicating the selection rule of the requested eigenvalues, for exam

* Compute the left and right diagonal matrices to scale the input matrix @p mat * * FIXME This algorithm will be modified such that the dia

r""" Create a connection matrix of walks of a specific size based on :cite:`e-Cajas10` formula. Parameters ---------- returns : Data

* Calculates cokurtosis square matrix of size "n" as shown in Cajas, D. (2022). * Convex Optimization of Portfolio Kurtosis. In SSRN Electronic Journ

* Calculates cokurtosis square matrix of size "n" for scenarios with probabilities * * @Y array of size n_samples x n_features. * @P array of size

* Calculates coskewness tensor of size "n" as shown in Cajas, D. (2022). * Convex Optimization of Portfolio Kurtosis. In SSRN Electronic Journal. *

* Calculates coskewness tensor of size "n" for scenarios with probabilities * * @Y array of size n_samples x n_features. * @P array of size n_sampl

* Calculates covariance matrix of size "n" as shown in Cajas, D. (2022). * Convex Optimization of Portfolio Kurtosis. In SSRN Electronic Journal. *

* Calculates covariance matrix of size "n" for scenarios with probabilities * * @Y array of size n_samples x n_features. * @P array of size n_sampl

* Calculates the distance correlation of matrices X and Y. * * @X A matrix. * @Y A matrix. */

* Calculates the euclidean distance matrix of matrix X. * * @X A matrix. */