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Diffstat (limited to 'doc/SparseLinearSystems.dox')
| -rw-r--r-- | doc/SparseLinearSystems.dox | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/doc/SparseLinearSystems.dox b/doc/SparseLinearSystems.dox index 9fb3282e7..ee4f53a4e 100644 --- a/doc/SparseLinearSystems.dox +++ b/doc/SparseLinearSystems.dox @@ -15,20 +15,20 @@ They are summarized in the following tables: <tr><th>Class</th><th>Solver kind</th><th>Matrix kind</th><th>Features related to performance</th> <th>License</th><th class="width20em"><p>Notes</p></th></tr> -<tr><td>SimplicialLLT \n <tt>#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing</td> +<tr><td>SimplicialLLT \n <tt>\#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing</td> <td>LGPL</td> <td>SimplicialLDLT is often preferable</td></tr> -<tr><td>SimplicialLDLT \n <tt>#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td> +<tr><td>SimplicialLDLT \n <tt>\#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td> <td>LGPL</td> <td>Recommended for very sparse and not too large problems (e.g., 2D Poisson eq.)</td></tr> -<tr><td>SparseLU \n <tt>#include<Eigen/\link SparseLU_Module SparseLU\endlink></tt></td> <td>LU factorization </td> +<tr><td>SparseLU \n <tt>\#include<Eigen/\link SparseLU_Module SparseLU\endlink></tt></td> <td>LU factorization </td> <td>Square </td><td>Fill-in reducing, Leverage fast dense algebra</td> <td>MPL2</td> <td>optimized for small and large problems with irregular patterns </td></tr> -<tr><td>SparseQR \n <tt>#include<Eigen/\link SparseQR_Module SparseQR\endlink></tt></td> <td> QR factorization</td> +<tr><td>SparseQR \n <tt>\#include<Eigen/\link SparseQR_Module SparseQR\endlink></tt></td> <td> QR factorization</td> <td>Any, rectangular</td><td> Fill-in reducing</td> <td>MPL2</td> <td>recommended for least-square problems, has a basic rank-revealing feature</td></tr> @@ -40,17 +40,17 @@ They are summarized in the following tables: <tr><th>Class</th><th>Solver kind</th><th>Matrix kind</th><th>Supported preconditioners, [default]</th> <th>License</th><th class="width20em"><p>Notes</p></th></tr> -<tr><td>ConjugateGradient \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td> <td>Classic iterative CG</td><td>SPD</td> +<tr><td>ConjugateGradient \n <tt>\#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td> <td>Classic iterative CG</td><td>SPD</td> <td>IdentityPreconditioner, [DiagonalPreconditioner], IncompleteCholesky</td> <td>MPL2</td> <td>Recommended for large symmetric problems (e.g., 3D Poisson eq.)</td></tr> -<tr><td>LeastSquaresConjugateGradient \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>CG for rectangular least-square problem</td><td>Rectangular</td> +<tr><td>LeastSquaresConjugateGradient \n <tt>\#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>CG for rectangular least-square problem</td><td>Rectangular</td> <td>IdentityPreconditioner, [LeastSquareDiagonalPreconditioner]</td> <td>MPL2</td> <td>Solve for min |A'Ax-b|^2 without forming A'A</td></tr> -<tr><td>BiCGSTAB \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>Iterative stabilized bi-conjugate gradient</td><td>Square</td> +<tr><td>BiCGSTAB \n <tt>\#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>Iterative stabilized bi-conjugate gradient</td><td>Square</td> <td>IdentityPreconditioner, [DiagonalPreconditioner], IncompleteLUT</td> <td>MPL2</td> <td>To speedup the convergence, try it with the \ref IncompleteLUT preconditioner.</td></tr> @@ -65,17 +65,17 @@ They are summarized in the following tables: <td>Requires the <a href="http://pastix.gforge.inria.fr">PaStiX</a> package, \b CeCILL-C </td> <td>optimized for tough problems and symmetric patterns</td></tr> <tr><td>CholmodSupernodalLLT</td><td>\link CholmodSupport_Module CholmodSupport \endlink</td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing, Leverage fast dense algebra</td> - <td>Requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td> + <td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td> <td></td></tr> <tr><td>UmfPackLU</td><td>\link UmfPackSupport_Module UmfPackSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td> - <td>Requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td> + <td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td> <td></td></tr> <tr><td>SuperLU</td><td>\link SuperLUSupport_Module SuperLUSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td> <td>Requires the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library, (BSD-like)</td> <td></td></tr> <tr><td>SPQR</td><td>\link SPQRSupport_Module SPQRSupport \endlink </td> <td> QR factorization </td> <td> Any, rectangular</td><td>fill-in reducing, multithreaded, fast dense algebra</td> - <td> requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td><td>recommended for linear least-squares problems, has a rank-revealing feature</tr> + <td> requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td><td>recommended for linear least-squares problems, has a rank-revealing feature</tr> </table> Here \c SPD means symmetric positive definite. |