ViennaCL - The Vienna Computing Library: qr.cpp

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qr.cpp

This tutorial shows how the QR factorization of matrices from ViennaCL or Boost.uBLAS can be computed.

// Activate ublas support in ViennaCL
#define VIENNACL_WITH_UBLAS
//
// Include necessary system headers
//
#include <iostream>
//
// ViennaCL includes
//
#include "viennacl/matrix.hpp"
#include "viennacl/linalg/prod.hpp"
#include "viennacl/linalg/qr.hpp"
//
// Boost includes
//
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>

A helper function comparing two matrices and returning the maximum entry-wise relative error encountered.

template<typename MatrixType>
double check(MatrixType const & qr, MatrixType const & ref)
{
bool do_break = false;
double max_error = 0;
for (std::size_t i=0; i<ref.size1(); ++i)
{
for (std::size_t j=0; j<ref.size2(); ++j)
{
if (qr(i,j) != 0.0 && ref(i,j) != 0.0)
{
double rel_err = fabs(qr(i,j) - ref(i,j)) / fabs(ref(i,j) );
if (rel_err > max_error)
max_error = rel_err;
}
/* Uncomment the following if you also want to check for NaNs.
if (qr(i,j) != qr(i,j))
{
std::cout << "!!!" << std::endl;
std::cout << "!!! NaN detected at i=" << i << " and j=" << j << std::endl;
std::cout << "!!!" << std::endl;
do_break = true;
break;
}*/
}
if (do_break)
break;
}
return max_error;
}

We set up a random matrix using Boost.uBLAS and use it to initialize a ViennaCL matrix. Then we compute the QR factorization directly for the uBLAS matrix as well as the ViennaCL matrix.

int main (int, const char **)
{
typedef double ScalarType; //feel free to change this to 'double' if supported by your hardware
typedef boost::numeric::ublas::matrix<ScalarType> MatrixType;
typedef viennacl::matrix<ScalarType, viennacl::column_major> VCLMatrixType;
std::size_t rows = 113; // number of rows in the matrix
std::size_t cols = 54; // number of columns

Create uBLAS matrices with some random input data.

MatrixType ublas_A(rows, cols);
MatrixType Q(rows, rows);
MatrixType R(rows, cols);
// Some random data with a bit of extra weight on the diagonal
for (std::size_t i=0; i<rows; ++i)
{
for (std::size_t j=0; j<cols; ++j)
{
ublas_A(i,j) = ScalarType(-1.0) + ScalarType((i+1)*(j+1))
+ ScalarType( (rand() % 1000) - 500.0) / ScalarType(1000.0);
if (i == j)
ublas_A(i,j) += ScalarType(10.0);
R(i,j) = 0.0;
}
for (std::size_t j=0; j<rows; ++j)
Q(i,j) = ScalarType(0.0);
}
// keep initial input matrix for comparison
MatrixType ublas_A_backup(ublas_A);

Setup the matrix in ViennaCL and copy the data from the uBLAS matrix:

VCLMatrixType vcl_A(ublas_A.size1(), ublas_A.size2());
viennacl::copy(ublas_A, vcl_A);

## QR Factorization with Boost.uBLAS Matrices

Compute QR factorization of A. A is overwritten with Householder vectors. Coefficients are returned and a block size of 3 is used. Note that at the moment the number of columns of A must be divisible by the block size

std::cout << "--- Boost.uBLAS ---" << std::endl;
std::vector<ScalarType> ublas_betas = viennacl::linalg::inplace_qr(ublas_A); //computes the QR factorization

Let us check for the correct result:

viennacl::linalg::recoverQ(ublas_A, ublas_betas, Q, R);
MatrixType ublas_QR = prod(Q, R);
double ublas_error = check(ublas_QR, ublas_A_backup);
std::cout << "Maximum relative error (ublas): " << ublas_error << std::endl;

## QR Factorization with Boost.uBLAS Matrices

We now compute the QR factorization from a ViennaCL matrix. Internally it uses Boost.uBLAS for the panel factorization.

std::cout << "--- Hybrid (default) ---" << std::endl;
viennacl::copy(ublas_A_backup, vcl_A);
std::vector<ScalarType> hybrid_betas = viennacl::linalg::inplace_qr(vcl_A);

Let us check for the correct result:

viennacl::copy(vcl_A, ublas_A);
Q.clear(); R.clear();
viennacl::linalg::recoverQ(ublas_A, hybrid_betas, Q, R);
double hybrid_error = check(ublas_QR, ublas_A_backup);
std::cout << "Maximum relative error (hybrid): " << hybrid_error << std::endl;

That's it. Print a success message and exit.

std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << 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
============================================================================= */
// Activate ublas support in ViennaCL
#define VIENNACL_WITH_UBLAS
//
// Include necessary system headers
//
#include <iostream>
//
// ViennaCL includes
//
#include "viennacl/matrix.hpp"
#include "viennacl/linalg/prod.hpp"
#include "viennacl/linalg/qr.hpp"
//
// Boost includes
//
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>
template<typename MatrixType>
double check(MatrixType const & qr, MatrixType const & ref)
{
bool do_break = false;
double max_error = 0;
for (std::size_t i=0; i<ref.size1(); ++i)
{
for (std::size_t j=0; j<ref.size2(); ++j)
{
if (qr(i,j) != 0.0 && ref(i,j) != 0.0)
{
double rel_err = fabs(qr(i,j) - ref(i,j)) / fabs(ref(i,j) );
if (rel_err > max_error)
max_error = rel_err;
}
/* Uncomment the following if you also want to check for NaNs.
if (qr(i,j) != qr(i,j))
{
std::cout << "!!!" << std::endl;
std::cout << "!!! NaN detected at i=" << i << " and j=" << j << std::endl;
std::cout << "!!!" << std::endl;
do_break = true;
break;
}*/
}
if (do_break)
break;
}
return max_error;
}
int main (int, const char **)
{
typedef double ScalarType; //feel free to change this to 'double' if supported by your hardware
typedef boost::numeric::ublas::matrix<ScalarType> MatrixType;
typedef viennacl::matrix<ScalarType, viennacl::column_major> VCLMatrixType;
std::size_t rows = 113; // number of rows in the matrix
std::size_t cols = 54; // number of columns
MatrixType ublas_A(rows, cols);
MatrixType Q(rows, rows);
MatrixType R(rows, cols);
// Some random data with a bit of extra weight on the diagonal
for (std::size_t i=0; i<rows; ++i)
{
for (std::size_t j=0; j<cols; ++j)
{
ublas_A(i,j) = ScalarType(-1.0) + ScalarType((i+1)*(j+1))
+ ScalarType( (rand() % 1000) - 500.0) / ScalarType(1000.0);
if (i == j)
ublas_A(i,j) += ScalarType(10.0);
R(i,j) = 0.0;
}
for (std::size_t j=0; j<rows; ++j)
Q(i,j) = ScalarType(0.0);
}
// keep initial input matrix for comparison
MatrixType ublas_A_backup(ublas_A);
VCLMatrixType vcl_A(ublas_A.size1(), ublas_A.size2());
viennacl::copy(ublas_A, vcl_A);
std::cout << "--- Boost.uBLAS ---" << std::endl;
std::vector<ScalarType> ublas_betas = viennacl::linalg::inplace_qr(ublas_A); //computes the QR factorization
viennacl::linalg::recoverQ(ublas_A, ublas_betas, Q, R);
MatrixType ublas_QR = prod(Q, R);
double ublas_error = check(ublas_QR, ublas_A_backup);
std::cout << "Maximum relative error (ublas): " << ublas_error << std::endl;
std::cout << "--- Hybrid (default) ---" << std::endl;
viennacl::copy(ublas_A_backup, vcl_A);
std::vector<ScalarType> hybrid_betas = viennacl::linalg::inplace_qr(vcl_A);
viennacl::copy(vcl_A, ublas_A);
Q.clear(); R.clear();
viennacl::linalg::recoverQ(ublas_A, hybrid_betas, Q, R);
double hybrid_error = check(ublas_QR, ublas_A_backup);
std::cout << "Maximum relative error (hybrid): " << hybrid_error << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}