BasicTools.Linalg.SVD module

BasicTools.Linalg.SVD.CheckIntegrity(GUI=False)[source]
BasicTools.Linalg.SVD.TruncatedSVDSymLower(matrix, epsilon=None, nbModes=None)[source]

Computes a truncatd singular value decomposition of a symetric definite matrix in scipy.sparse.csr format. Only the lower triangular part needs to be defined

Parameters:

  • matrix (scipy.sparse.csr) – the input matrix

  • epsilon (float) – the truncation tolerence, determining the number of keps eigenvalues

  • nbModes (int) – the number of keps eigenvalues

Returns:

  • np.ndarray – kept eigenvalues, of size (numberOfEigenvalues)

  • np.ndarray – kept eigenvectors, of size (numberOfEigenvalues, numberOfSnapshots)