\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.9 - CGAL and Solvers
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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]
oNCGAL
|oCDiagonalize_traitsThe class Diagonalize_traits provides an internal implementation for the diagonalization of Variance-Covariance Matrices
|oCEigen_diagonalize_traitsThe class Eigen_diagonalize_traits provides an interface to the diagonalization of covariance matrices of Eigen
|oCEigen_sparse_matrixThe class Eigen_sparse_matrix is a wrapper around Eigen matrix type Eigen::SparseMatrix that represents general matrices, be they symmetric or not
|oCEigen_sparse_symmetric_matrixThe class Eigen_sparse_symmetric_matrix is a wrapper around Eigen matrix type Eigen::SparseMatrix
|oCEigen_matrixThe class Eigen_matrix is a wrapper around Eigen matrix type Eigen::Matrix
|oCEigen_solver_traitsThe class Eigen_solver_traits provides an interface to the sparse solvers of Eigen
|oCEigen_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
oCDiagonalizeTraitsConcept 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:
\( \begin{bmatrix} a[0] & a[1] & a[2] \\ a[1] & a[3] & a[4] \\ a[2] & a[4] & a[5] \\ \end{bmatrix}\)
oCNormalEquationSparseLinearAlgebraTraits_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
oCSparseLinearAlgebraTraits_dThe concept SparseLinearAlgebraTraits_d is used to solve sparse linear systems A \( \times \) X = B
|oCMatrixSparseLinearAlgebraTraits_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
oCSparseLinearAlgebraWithFactorTraits_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
 oCMatrixConcept of matrix type used by the concept SvdTraits
 \CVectorConcept of vector type used by the concept SvdTraits