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#include <cusp/precond/diagonal.h>
#include <cusp/krylov/cg.h>
#include <cusp/csr_matrix.h>
#include <cusp/io/matrix_market.h>
#include <iostream>
// where to perform the computation
typedef cusp::device_memory MemorySpace;
// which floating point type to use
typedef float ValueType;
int main(void)
{
// create an empty sparse matrix structure (HYB format)
cusp::csr_matrix<int, ValueType, MemorySpace> A;
// load a matrix stored in MatrixMarket format
cusp::io::read_matrix_market_file(A, "A.mtx");
// Note: A has poorly scaled rows & columns
// solve without preconditioning
{
std::cout << "\nSolving with no preconditioner" << std::endl;
// allocate storage for solution (x) and right hand side (b)
cusp::array1d<ValueType, MemorySpace> x(A.num_rows, 0);
cusp::array1d<ValueType, MemorySpace> b(A.num_rows, 1);
// set stopping criteria (iteration_limit = 100, relative_tolerance = 1e-6)
cusp::verbose_monitor<ValueType> monitor(b, 100, 1e-6);
// solve
cusp::krylov::cg(A, x, b, monitor);
}
// solve with diagonal preconditioner
{
std::cout << "\nSolving with diagonal preconditioner (M = D^-1)" << std::endl;
// allocate storage for solution (x) and right hand side (b)
cusp::array1d<ValueType, MemorySpace> x(A.num_rows, 0);
cusp::array1d<ValueType, MemorySpace> b(A.num_rows, 1);
// set stopping criteria (iteration_limit = 100, relative_tolerance = 1e-6)
cusp::verbose_monitor<ValueType> monitor(b, 100, 1e-6);
// setup preconditioner
cusp::precond::diagonal<ValueType, MemorySpace> M(A);
// solve
cusp::krylov::cg(A, x, b, monitor, M);
}
return 0;
}
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