11 October 2024
11 October 2024
| Function | Action | 
|---|---|
| Singular Value Decomposition, divide-and-conquer algorithm; considered the fastest among other SVD algorithms (lapack function GESDD). | |
| Singular Value Decomposition, QR algorithm; considered a classical SVD algorithm (lapack function GESVD). | |
| Singular Value Decomposition, QR with pivoting algorithm (lapack function GESVDQ). | |
| Singular Value Decomposition, bisection algorithm (lapack function GESVDX). | |
| Singular Value Decomposition, Jacobi high-level algorithm (lapack function GEJSV). | |
| Singular Value Decomposition, Jacobi low-level algorithm (lapack function GESVJ). The method computes small singular values and their singular vectors with much greater accuracy than other SVD routines in certain cases. | |
| Singular Value Decomposition, divide-and-conquer algorithm for bidiagonal matrices (lapack function BDSVDX). | |
| Singular Value Decomposition, bisection algorithm for bidiagonal matrices (lapack function BDSVDX). | |
| Compute eigenvalues and eigenvectors of a regular square matrix using the classical algorithm (lapack function GEEV). | |
| Compute eigenvalues and eigenvectors of a symmetric or Hermitian (complex conjugate) matrix using the divide-and-conquer algorithm (lapack functions SYEVD, HEEVD). | |
| A method function for calculating the relative contributions of spectral components based on their eigenvalues | |
| A method function for calculating reconstructed and predicted data using spectral components of the input time series. | |
| A method function for calculating reconstructed components of the input time series and their contributions. | |
| A method function for calculating the reconstructed time series using the first component_count components. |