diff options
author | Yi Kong <yikong@google.com> | 2022-02-25 17:02:53 +0000 |
---|---|---|
committer | Automerger Merge Worker <android-build-automerger-merge-worker@system.gserviceaccount.com> | 2022-02-25 17:02:53 +0000 |
commit | edb0ad5bb04b48aab7dd0978f0475edd3550de7c (patch) | |
tree | fb979fb4cf4f8052c8cc66b1ec9516d91fcd859b /doc/TopicLinearAlgebraDecompositions.dox | |
parent | 8fd413e275f78a4c240f1442ce5cf77c73a20a55 (diff) | |
parent | bc0f5df265caa21a2120c22453655a7fcc941991 (diff) | |
download | eigen-edb0ad5bb04b48aab7dd0978f0475edd3550de7c.tar.gz |
Merge changes Iee153445,Iee274471 am: 79df15ea88 am: 10f298fc41 am: 7cb5001398 am: bc0f5df265aml_uwb_331910010aml_uwb_331820070aml_uwb_331613010aml_uwb_331611010aml_uwb_331410010aml_uwb_331310030aml_uwb_331115000aml_uwb_331015040aml_uwb_330810010aml_tz4_332714070aml_tz4_332714050aml_tz4_332714010aml_tz4_331910000aml_tz4_331314030aml_tz4_331314020aml_tz4_331314010aml_tz4_331012050aml_tz4_331012040aml_tz4_331012000aml_ase_331311020aml_ase_331112000aml_ase_331011020android13-mainline-uwb-releaseandroid13-mainline-tzdata4-releaseandroid13-mainline-appsearch-releaseaml_tz4_332714010
Original change: https://android-review.googlesource.com/c/platform/external/eigen/+/1999079
Change-Id: Ife39d10c8b23d3eeb174cd52f462f9d20527ad03
Diffstat (limited to 'doc/TopicLinearAlgebraDecompositions.dox')
-rw-r--r-- | doc/TopicLinearAlgebraDecompositions.dox | 32 |
1 files changed, 28 insertions, 4 deletions
diff --git a/doc/TopicLinearAlgebraDecompositions.dox b/doc/TopicLinearAlgebraDecompositions.dox index 491470627..402b3769e 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 @@ -72,7 +72,7 @@ To get an overview of the true relative speed of the different decomposition, ch <td>Orthogonalization</td> <td>Yes</td> <td>Excellent</td> - <td><em>Soon: blocking</em></td> + <td><em>-</em></td> </tr> <tr> @@ -88,6 +88,18 @@ To get an overview of the true relative speed of the different decomposition, ch </tr> <tr class="alt"> + <td>CompleteOrthogonalDecomposition</td> + <td>-</td> + <td>Fast</td> + <td>Good</td> + <td>Yes</td> + <td>Orthogonalization</td> + <td>Yes</td> + <td>Excellent</td> + <td><em>-</em></td> + </tr> + + <tr> <td>LLT</td> <td>Positive definite</td> <td>Very fast</td> @@ -99,7 +111,7 @@ To get an overview of the true relative speed of the different decomposition, ch <td>Blocking</td> </tr> - <tr> + <tr class="alt"> <td>LDLT</td> <td>Positive or negative semidefinite<sup><a href="#note1">1</a></sup></td> <td>Very fast</td> @@ -114,6 +126,18 @@ 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 +272,7 @@ 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> |