All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Pages
qr_method.cpp

In this tutorial the eigenvalues and eigenvectors of a symmetric 9-by-9 matrix are calculated using the QR-method.

The first step is to include the necessary headers and to define the floating point type used.

// System headers:
#include <iostream>
#include <fstream>
#include <stdexcept>
#include <vector>
// ViennaCL headers:
#include "viennacl/matrix.hpp"
#include "viennacl/linalg/prod.hpp"
#include "viennacl/linalg/qr-method.hpp"

Helper routine for initializing a ViennaCL matrix and returning its associated eigenvalues

template <typename ScalarType>
void initialize(viennacl::matrix<ScalarType> & A, std::vector<ScalarType> & v)
{
// System matrix:
ScalarType M[9][9] = {{ 4, 1, -2, 2, -7, 3, 9, -6, -2},
{ 1, -2, 0, 1, -1, 5, 4, 7, 3},
{-2, 0, 3, 2, 0, 3, 6, 1, -1},
{ 2, 1, 2, 1, 4, 5, 6, 7, 8},
{-7, -1, 0, 4, 5, 4, 9, 1, -8},
{ 3, 5, 3, 5, 4, 9, -3, 3, 3},
{ 9, 4, 6, 6, 9, -3, 3, 6, -7},
{-6, 7, 1, 7, 1, 3, 6, 2, 6},
{-2, 3, -1, 8, -8, 3, -7, 6, 1}};
for(std::size_t i = 0; i < 9; i++)
for(std::size_t j = 0; j < 9; j++)
A(i, j) = M[i][j];
// Known eigenvalues:
ScalarType V[9] = {ScalarType(12.6005), ScalarType(19.5905), ScalarType(8.06067), ScalarType(2.95074), ScalarType(0.223506),
ScalarType(24.3642), ScalarType(-9.62084), ScalarType(-13.8374), ScalarType(-18.3319)};
for(std::size_t i = 0; i < 9; i++)
v[i] = V[i];
}

Helper routine: Print an STL vector

template <typename ScalarType>
void vector_print(std::vector<ScalarType>& v )
{
for (unsigned int i = 0; i < v.size(); i++)
std::cout << std::setprecision(6) << std::fixed << v[i] << "\t";
std::cout << std::endl;
}

Create a system of size 9-by-9 with known eigenvalues and compare it with the eigenvalues computed from the QR method implemented in ViennaCL.

int main()
{
// Change to 'double' if supported by your hardware.
typedef float ScalarType;
std::cout << "Testing matrix of size " << 9 << "-by-" << 9 << std::endl;
viennacl::matrix<ScalarType> A_input(9,9);
viennacl::matrix<ScalarType> Q(9, 9);
std::vector<ScalarType> eigenvalues_ref(9);
std::vector<ScalarType> eigenvalues(9);
viennacl::vector<ScalarType> vcl_eigenvalues(9);
initialize(A_input, eigenvalues_ref);
std::cout << std::endl <<"Input matrix: " << std::endl;
std::cout << A_input << std::endl;
viennacl::matrix<ScalarType> A_input2(A_input); // duplicate to demonstrate the use with both std::vector and viennacl::vector

Call the function qr_method_sym() to calculate eigenvalues and eigenvectors Parameters:

- A_input - input matrix to find eigenvalues and eigenvectors from
- Q - matrix, where the calculated eigenvectors will be stored in
- eigenvalues - vector, where the calculated eigenvalues will be stored in
  std::cout << "Calculation..." << std::endl;
  viennacl::linalg::qr_method_sym(A_input, Q, eigenvalues);
  Same as before, but writing the eigenvalues to a ViennaCL-vector:
  viennacl::linalg::qr_method_sym(A_input2, Q, vcl_eigenvalues);
  Print the computed eigenvalues and eigenvectors:
  std::cout << std::endl << "Eigenvalues with std::vector<T>:" << std::endl;
  vector_print(eigenvalues);
  std::cout << "Eigenvalues with viennacl::vector<T>: " << std::endl << vcl_eigenvalues << std::endl;
  std::cout << std::endl << "Reference eigenvalues:" << std::endl;
  vector_print(eigenvalues_ref);
  std::cout << std::endl;
  std::cout << "Eigenvectors - each column is an eigenvector" << std::endl;
  std::cout << Q << std::endl;
  That's it. Print a success message and exit.
  std::cout << std::endl;
  std::cout << "------- Tutorial completed --------" << std::endl;
  std::cout << std::endl;
  return EXIT_SUCCESS;
  }

  ## Full Example Code

/* =========================================================================
Copyright (c) 2010-2016, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
// System headers:
#include <iostream>
#include <fstream>
#include <stdexcept>
#include <vector>
// ViennaCL headers:
#include "viennacl/matrix.hpp"
#include "viennacl/linalg/prod.hpp"
#include "viennacl/linalg/qr-method.hpp"
template <typename ScalarType>
void initialize(viennacl::matrix<ScalarType> & A, std::vector<ScalarType> & v)
{
// System matrix:
ScalarType M[9][9] = {{ 4, 1, -2, 2, -7, 3, 9, -6, -2},
{ 1, -2, 0, 1, -1, 5, 4, 7, 3},
{-2, 0, 3, 2, 0, 3, 6, 1, -1},
{ 2, 1, 2, 1, 4, 5, 6, 7, 8},
{-7, -1, 0, 4, 5, 4, 9, 1, -8},
{ 3, 5, 3, 5, 4, 9, -3, 3, 3},
{ 9, 4, 6, 6, 9, -3, 3, 6, -7},
{-6, 7, 1, 7, 1, 3, 6, 2, 6},
{-2, 3, -1, 8, -8, 3, -7, 6, 1}};
for(std::size_t i = 0; i < 9; i++)
for(std::size_t j = 0; j < 9; j++)
A(i, j) = M[i][j];
// Known eigenvalues:
ScalarType V[9] = {ScalarType(12.6005), ScalarType(19.5905), ScalarType(8.06067), ScalarType(2.95074), ScalarType(0.223506),
ScalarType(24.3642), ScalarType(-9.62084), ScalarType(-13.8374), ScalarType(-18.3319)};
for(std::size_t i = 0; i < 9; i++)
v[i] = V[i];
}
template <typename ScalarType>
void vector_print(std::vector<ScalarType>& v )
{
for (unsigned int i = 0; i < v.size(); i++)
std::cout << std::setprecision(6) << std::fixed << v[i] << "\t";
std::cout << std::endl;
}
int main()
{
// Change to 'double' if supported by your hardware.
typedef float ScalarType;
std::cout << "Testing matrix of size " << 9 << "-by-" << 9 << std::endl;
viennacl::matrix<ScalarType> A_input(9,9);
viennacl::matrix<ScalarType> Q(9, 9);
std::vector<ScalarType> eigenvalues_ref(9);
std::vector<ScalarType> eigenvalues(9);
viennacl::vector<ScalarType> vcl_eigenvalues(9);
initialize(A_input, eigenvalues_ref);
std::cout << std::endl <<"Input matrix: " << std::endl;
std::cout << A_input << std::endl;
viennacl::matrix<ScalarType> A_input2(A_input); // duplicate to demonstrate the use with both std::vector and viennacl::vector
std::cout << "Calculation..." << std::endl;
viennacl::linalg::qr_method_sym(A_input, Q, eigenvalues);
viennacl::linalg::qr_method_sym(A_input2, Q, vcl_eigenvalues);
std::cout << std::endl << "Eigenvalues with std::vector<T>:" << std::endl;
vector_print(eigenvalues);
std::cout << "Eigenvalues with viennacl::vector<T>: " << std::endl << vcl_eigenvalues << std::endl;
std::cout << std::endl << "Reference eigenvalues:" << std::endl;
vector_print(eigenvalues_ref);
std::cout << std::endl;
std::cout << "Eigenvectors - each column is an eigenvector" << std::endl;
std::cout << Q << std::endl;
std::cout << std::endl;
std::cout << "------- Tutorial completed --------" << std::endl;
std::cout << std::endl;
return EXIT_SUCCESS;
}