void row_normalize_system(boost::numeric::ublas::compressed_matrix<NumericT> & A,
boost::numeric::ublas::vector<NumericT> & b)
{
typedef typename boost::numeric::ublas::compressed_matrix<NumericT>::iterator1 Iterator1;
typedef typename boost::numeric::ublas::compressed_matrix<NumericT>::iterator2 Iterator2;
//preprocessing: scale rows such that ||a_i|| = 1
for (Iterator1 iter1 = A.begin1();
iter1 != A.end1();
++iter1)
{
double row_norm = 0.0;
for (Iterator2 iter2 = iter1.begin();
iter2 != iter1.end();
++iter2)
{
row_norm += *iter2 * *iter2;
}
row_norm = sqrt(row_norm);
if (A(iter1.index1(), iter1.index1()) < 0.0)
row_norm *= -1.0;
for (Iterator2 iter2 = iter1.begin();
iter2 != iter1.end();
++iter2)
{
*iter2 /= row_norm;
}
b(iter1.index1()) /= row_norm;
}
}
matrix_map<T, ptrdiff_t>
map(const boost::numeric::ublas::compressed_matrix<T, boost::numeric::ublas::row_major> &A) {
return matrix_map<T, ptrdiff_t>(
A.size1(),
A.size2(),
// amgcl expects signed type for row and col data. This should work
// for decently sized matrices:
reinterpret_cast<const ptrdiff_t*>(A.index1_data().begin()),
reinterpret_cast<const ptrdiff_t*>(A.index2_data().begin()),
A.value_data().begin()
);
}
bool readMatrixFromFile(const std::string & filename, boost::numeric::ublas::compressed_matrix<TYPE> & matrix)
{
std::cout << "Reading ublas matrix" << std::endl;
std::ifstream file(filename.c_str());
if (!file) return false;
std::string id;
file >> id;
if (id != "Matrix") return false;
unsigned int num_rows, num_columns;
file >> num_rows >> num_columns;
if (num_rows != num_columns) return false;
matrix.resize(num_rows, num_rows, false);
for (unsigned int row = 0; row < num_rows; ++row)
{
int num_entries;
file >> num_entries;
for (int j = 0; j < num_entries; ++j)
{
unsigned int column;
TYPE element;
file >> column >> element;
//matrix.insert_element(row, column, element);
matrix(row, column) = element;
}
}
return true;
}
void axpy(
const boost::numeric::ublas::compressed_matrix<real, boost::numeric::ublas::row_major> &A,
const boost::numeric::ublas::vector<real> &x,
boost::numeric::ublas::vector<real> &y
)
{
const ptrdiff_t n = A.size1();
const size_t *Arow = A.index1_data().begin();
const size_t *Acol = A.index2_data().begin();
const real *Aval = A.value_data().begin();
#pragma omp parallel for schedule(dynamic, 1024)
for(ptrdiff_t i = 0; i < n; ++i) {
real buf = 0;
for(size_t j = Arow[i], e = Arow[i + 1]; j < e; ++j)
buf += Aval[j] * x[Acol[j]];
y[i] = buf;
}
}
std::tuple<
size_t,
boost::iterator_range<const size_t*>,
boost::iterator_range<const size_t*>,
boost::iterator_range<const T*>
>
map(const boost::numeric::ublas::compressed_matrix<T, boost::numeric::ublas::row_major> &A) {
return std::make_tuple(
A.size1(),
boost::make_iterator_range(
A.index1_data().begin(), A.index1_data().end()
),
boost::make_iterator_range(
A.index2_data().begin(), A.index2_data().end()
),
boost::make_iterator_range(
A.value_data().begin(), A.value_data().end()
)
);
}
void resize(boost::numeric::ublas::compressed_matrix<ScalarType> & matrix,
size_t rows,
size_t cols)
{
matrix.resize(rows, cols, false); //Note: omitting third parameter leads to compile time error (not implemented in ublas <= 1.42)
}