CGAL 4.12 - CGAL and Solvers
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▼NCGAL | |
CDefault_diagonalize_traits | The class Default_diagonalize_traits is a wrapper designed to automatically use Eigen_diagonalize_traits if Eigen is available and otherwise use the fallback Diagonalize_traits class of CGAL |
CDiagonalize_traits | The class Diagonalize_traits provides an internal implementation for the diagonalization of Variance-Covariance Matrices |
CEigen_diagonalize_traits | The class Eigen_diagonalize_traits provides an interface to the diagonalization of covariance matrices of Eigen |
CEigen_matrix | The class Eigen_matrix is a wrapper around Eigen matrix type Eigen::Matrix |
CEigen_solver_traits | The class Eigen_solver_traits provides an interface to the sparse solvers of Eigen |
CEigen_solver_traits< Eigen::BiCGSTAB< Eigen_sparse_matrix< double >::EigenType > > | |
CEigen_sparse_matrix | The class Eigen_sparse_matrix is a wrapper around Eigen matrix type Eigen::SparseMatrix that represents general matrices, be they symmetric or not |
CEigen_sparse_symmetric_matrix | The class Eigen_sparse_symmetric_matrix is a wrapper around Eigen matrix type Eigen::SparseMatrix |
CEigen_svd | The class Eigen_svd provides an algorithm to solve in the least square sense a linear system with a singular value decomposition using Eigen |
CEigen_vector | The class Eigen_vector is a wrapper around Eigen vector type , which is a simple array of numbers |
CLapack_matrix | In CLAPACK, matrices are one-dimensional arrays and elements are column-major ordered |
CLapack_svd | This class is a wrapper to the singular value decomposition algorithm of LAPACK |
CLapack_vector | A matrix class to be used in the class Lapack_svd |
CDiagonalizeTraits | Concept providing functions to extract eigenvectors and eigenvalues from covariance matrices represented by an array a , using symmetric diagonalization. For example, a matrix of dimension 3 is defined as follows: |
CNormalEquationSparseLinearAlgebraTraits_d | Concept describing the set of requirements for solving the normal equation \( A^t A X = A^t B \), \( A \) being a matrix, \( At \) its transpose matrix, \( B \) and \( X \) being two vectors |
▼CSparseLinearAlgebraTraits_d | The concept SparseLinearAlgebraTraits_d is used to solve sparse linear systems A \( \times \) X = B |
CMatrix | SparseLinearAlgebraTraits_d::Matrix is a concept of a sparse matrix class |
CVector | SparseLinearAlgebraTraits_d::Vector is a concept of a vector that can be multiplied by a sparse matrix |
CSparseLinearAlgebraWithFactorTraits_d | Concept describing the set of requirements for a direct sparse linear system solver with factorization. A model of this concept stores the left-hand matrix (denoted \( A \)) and provides an additional factorization method to solve the system for different right-hand vectors |
▼CSvdTraits | The concept SvdTraits describes the linear algebra types and algorithms needed to solve in the least square sense a linear system with a singular value decomposition |
CMatrix | Concept of matrix type used by the concept SvdTraits |
CVector | Concept of vector type used by the concept SvdTraits |