diff options
Diffstat (limited to 'doc/TopicLinearAlgebraDecompositions.dox')
| -rw-r--r-- | doc/TopicLinearAlgebraDecompositions.dox | 16 |
1 files changed, 14 insertions, 2 deletions
diff --git a/doc/TopicLinearAlgebraDecompositions.dox b/doc/TopicLinearAlgebraDecompositions.dox index 491470627..0965da872 100644 --- a/doc/TopicLinearAlgebraDecompositions.dox +++ b/doc/TopicLinearAlgebraDecompositions.dox @@ -4,7 +4,7 @@ namespace Eigen { This page presents a catalogue of the dense matrix decompositions offered by Eigen. For an introduction on linear solvers and decompositions, check this \link TutorialLinearAlgebra page \endlink. -To get an overview of the true relative speed of the different decomposition, check this \link DenseDecompositionBenchmark benchmark \endlink. +To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink. \section TopicLinAlgBigTable Catalogue of decompositions offered by Eigen @@ -114,6 +114,18 @@ To get an overview of the true relative speed of the different decomposition, ch <tr><th class="inter" colspan="9">\n Singular values and eigenvalues decompositions</th></tr> <tr> + <td>BDCSVD (divide \& conquer)</td> + <td>-</td> + <td>One of the fastest SVD algorithms</td> + <td>Excellent</td> + <td>Yes</td> + <td>Singular values/vectors, least squares</td> + <td>Yes (and does least squares)</td> + <td>Excellent</td> + <td>Blocked bidiagonalization</td> + </tr> + + <tr> <td>JacobiSVD (two-sided)</td> <td>-</td> <td>Slow (but fast for small matrices)</td> @@ -248,7 +260,7 @@ To get an overview of the true relative speed of the different decomposition, ch <dt><b>Blocking</b></dt> <dd>Means the algorithm can work per block, whence guaranteeing a good scaling of the performance for large matrices.</dd> <dt><b>Implicit Multi Threading (MT)</b></dt> - <dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product rountines.</dd> + <dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product routines.</dd> <dt><b>Explicit Multi Threading (MT)</b></dt> <dd>Means the algorithm is explicitly parallelized to take advantage of multicore processors via OpenMP.</dd> <dt><b>Meta-unroller</b></dt> |