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 boost::numeric::ublas::vector<ScalarType> VectorType;
typedef viennacl::matrix<ScalarType, viennacl::column_major> VCLMatrixType;
typedef viennacl::vector<ScalarType> VCLVectorType;
std::size_t rows = 113; //number of rows in the matrix
std::size_t cols = 54; //number of columns
//
// Create matrices with some 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) = -1.0 + (i+1)*(j+1)
+ ( (rand() % 1000) - 500.0) / 1000.0;
if (i == j)
ublas_A(i,j) += 10.0;
R(i,j) = 0.0;
}
for (std::size_t j=0; j<rows; ++j)
Q(i,j) = 0.0;
}
// keep initial input matrix for comparison
MatrixType ublas_A_backup(ublas_A);
//
// Setup the matrix in ViennaCL:
//
VCLVectorType dummy(10);
VCLMatrixType vcl_A(ublas_A.size1(), ublas_A.size2());
viennacl::copy(ublas_A, vcl_A);
//
// 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
//
// A 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 << "Max rel error (ublas): " << ublas_error << std::endl;
//
// QR factorization in ViennaCL using 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);
//
// A 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 << "Max rel error (hybrid): " << hybrid_error << std::endl;
//
// That's it.
//
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}
/**
* 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);
/**
* <h2>QR Factorization with Boost.uBLAS Matrices</h2>
* 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;
/**
* <h2>QR Factorization with Boost.uBLAS Matrices</h2>
* 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;
}
