\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 5.0 - CGAL and Solvers
SvdTraits Concept Reference

Definition

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.

Has Models:
CGAL::Eigen_svd

Concepts

conceptMatrix
 Concept of matrix type used by the concept SvdTraits. More...
 
conceptVector
 Concept of vector type used by the concept SvdTraits. More...
 

Types

typedef unspecified_type FT
 The scalar type.
 
typedef unspecified_type Vector
 The vector type, model of the concept SvdTraits::Vector.
 
typedef unspecified_type Matrix
 The matrix type, model of the concept SvdTraits::Matrix.
 

Operations

The concept SvdTraits has a linear solver using a singular value decomposition algorithm.

FT solve (const Matrix &M, Vector &B)
 Solves the system \( MX=B\) (in the least square sense if \( M\) is not square) using a singular value decomposition and returns the condition number of \( M\). More...
 

Member Function Documentation

◆ solve()

FT SvdTraits::solve ( const Matrix M,
Vector B 
)

Solves the system \( MX=B\) (in the least square sense if \( M\) is not square) using a singular value decomposition and returns the condition number of \( M\).

The solution is stored in \( B\).