#ifndef EIGEN_ORDERINGMETHODS_MODULE_H #define EIGEN_ORDERINGMETHODS_MODULE_H #include "SparseCore" #include "src/Core/util/DisableStupidWarnings.h" /** * \defgroup OrderingMethods_Module OrderingMethods module * * This module is currently for internal use only * * It defines various built-in and external ordering methods for sparse matrices. * They are typically used to reduce the number of elements during * the sparse matrix decomposition (LLT, LU, QR). * Precisely, in a preprocessing step, a permutation matrix P is computed using * those ordering methods and applied to the columns of the matrix. * Using for instance the sparse Cholesky decomposition, it is expected that * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A). * * * Usage : * \code * #include * \endcode * * A simple usage is as a template parameter in the sparse decomposition classes : * * \code * SparseLU > solver; * \endcode * * \code * SparseQR > solver; * \endcode * * It is possible as well to call directly a particular ordering method for your own purpose, * \code * AMDOrdering ordering; * PermutationMatrix perm; * SparseMatrix A; * //Fill the matrix ... * * ordering(A, perm); // Call AMD * \endcode * * \note Some of these methods (like AMD or METIS), need the sparsity pattern * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, * Eigen computes internally the pattern of \f\$A^T*A\f\$ before calling the method. * If your matrix is already symmetric (at leat in structure), you can avoid that * by calling the method with a SelfAdjointView type. * * \code * // Call the ordering on the pattern of the lower triangular matrix A * ordering(A.selfadjointView(), perm); * \endcode */ #ifndef EIGEN_MPL2_ONLY #include "src/OrderingMethods/Amd.h" #endif #include "src/OrderingMethods/Ordering.h" #include "src/Core/util/ReenableStupidWarnings.h" #endif // EIGEN_ORDERINGMETHODS_MODULE_H