\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.12 - CGAL and Solvers
Class and Concept List
Here is the list of all concepts and classes of this package. Classes are inside the namespace CGAL. Concepts are in the global namespace.
[detail level 12]
 NCGAL
 CDefault_diagonalize_traitsThe 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_traitsThe class Diagonalize_traits provides an internal implementation for the diagonalization of Variance-Covariance Matrices
 CEigen_diagonalize_traitsThe class Eigen_diagonalize_traits provides an interface to the diagonalization of covariance matrices of Eigen
 CEigen_matrixThe class Eigen_matrix is a wrapper around Eigen matrix type Eigen::Matrix
 CEigen_solver_traitsThe 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_matrixThe 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_matrixThe class Eigen_sparse_symmetric_matrix is a wrapper around Eigen matrix type Eigen::SparseMatrix
 CEigen_svdThe class Eigen_svd provides an algorithm to solve in the least square sense a linear system with a singular value decomposition using Eigen
 CEigen_vectorThe class Eigen_vector is a wrapper around Eigen vector type , which is a simple array of numbers
 CLapack_matrixIn CLAPACK, matrices are one-dimensional arrays and elements are column-major ordered
 CLapack_svdThis class is a wrapper to the singular value decomposition algorithm of LAPACK
 CLapack_vectorA matrix class to be used in the class Lapack_svd
 CDiagonalizeTraitsConcept 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:
\( \begin{bmatrix} a[0] & a[1] & a[2] \\ a[1] & a[3] & a[4] \\ a[2] & a[4] & a[5] \\ \end{bmatrix}\)
 CNormalEquationSparseLinearAlgebraTraits_dConcept 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_dThe concept SparseLinearAlgebraTraits_d is used to solve sparse linear systems A \( \times \) X = B
 CMatrixSparseLinearAlgebraTraits_d::Matrix is a concept of a sparse matrix class
 CVectorSparseLinearAlgebraTraits_d::Vector is a concept of a vector that can be multiplied by a sparse matrix
 CSparseLinearAlgebraWithFactorTraits_dConcept 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
 CSvdTraitsThe 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
 CMatrixConcept of matrix type used by the concept SvdTraits
 CVectorConcept of vector type used by the concept SvdTraits