Used to construct instances of the singular value decomposition class, RWSVDecomp. More...

#include <rw/lapack/sv.h>

Inherits SVDCalc.

Public Types
typedef rw_numeric_traits< TypeT >::norm_type	norm_type

Public Member Functions
	RWSVServer ()

void	computeAllLeftVectors ()

void	computeAllRightVectors ()

void	computeAllVectors ()

void	computeDefaultLeftVectors ()

void	computeDefaultRightVectors ()

void	computeDefaultVectors ()

void	computeLeftVectors (unsigned n)

void	computeRightVectors (unsigned n)

void	computeVectors (unsigned n)

virtual RWSVDecomp< TypeT, SVDCalc >	operator() (const RWGenMat< TypeT > &) const

void	setTolerance (norm_type)

Detailed Description

template<class TypeT, class SVDCalc>
class RWSVServer< TypeT, SVDCalc >

The singular value decomposition server class, RWSVServer, is used to construct instances of the singular value decomposition class, RWSVDecomp. Using a server rather than the standard singular value decomposition constructor gives you control over what is computed.

The template parameter <SVDCalc> determines the algorithm used by the RWSVDecomp class to compute the singular value decomposition and must implement the following method:

Note: For greater flexibility, the user can implement this method, or the Linear Algebra Module provides two classes to perform this function — RWSVDCalc and RWSVDDivConqCalc. Please see their descriptions for more information.

bool computeSVD(const RWGenMat<T>&   A,
                      RWGenMat<T>&   U,
                      RWGenMat<T>&  VT,
                      RWMathVec<norm_type>&  sigma,
                      norm_type  tolerance,
                  int numLeftVectors = -1,
                  int numRightVectors = -1);

where norm_type is a typedef for rw_numeric_traits<T>::norm_type.

Parameters:

A - The input matrix for which the singular value decomposition is being computed.

U - The output matrix of left singular victors (the columns of U are the left singular vectors).

VT - The output matrix of right singular victors (the rows of VT are the right singular victors).

sigma - The output vector of singular values in descending order.

tolerance - The input singular values with magnitude less than tolerance will be set to zero.

numLeftVectors - The input number of left vectors to compute. If the number is less than zero, the default number of vectors will be computed (it is up to the developer to determine what the default is).

numRightVectors - The input number of right vectors to compute. If the number is less than zero, the default number of vectors will be computed (it is up to the developer to determine what the default is).

The return value is true if the decomposition was successfully computed.

Synopsis: #include <rw/lapack/sv.h>

#include <rw/lapack/svddccalc.h>

RWSVServer<double, RWSVDDivConqCalc<double> > server;

Example: // Read a matrix from standard input and print its singular

// values. Use a SV decomposition server to prevent computing

// the singular vectors.

#include <iostream>

#include <rw/math/genmat.h>

#include <rw/lapack/sv.h>

#include <rw/lapack/svddccalc.h>

int main()

{

RWGenMat<double> A;

std::cin >> A;

RWSVServer<double, RWSVDDivConqCalc<double> > server;

server.computeVectors(0); // compute zero vectors

RWSVDecomp<double, RWSVDDivConqCalc<double> > SV = server(A);

std::cout << "Singular values are " << SV.SingularValues()

<< std::endl;

return 0;

}