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#include <cusp/precond/ainv.h>
#include <cusp/krylov/cg.h>
#include <cusp/gallery/poisson.h>
#include <cusp/csr_matrix.h>
#include <iostream>
template <typename Monitor>
void report_status(Monitor& monitor)
{
if (monitor.converged())
{
std::cout << "Solver converged to " << monitor.tolerance() << " tolerance";
std::cout << " after " << monitor.iteration_count() << " iterations";
std::cout << " (" << monitor.residual_norm() << " final residual)" << std::endl;
}
else
{
std::cout << "Solver reached iteration limit " << monitor.iteration_limit() << " before converging";
std::cout << " to " << monitor.tolerance() << " tolerance ";
std::cout << " (" << monitor.residual_norm() << " final residual)" << std::endl;
}
}
int main(void)
{
typedef int IndexType;
typedef float ValueType;
typedef cusp::device_memory MemorySpace;
// create an empty sparse matrix structure
cusp::coo_matrix<IndexType, ValueType, MemorySpace> A;
// create 2D Poisson problem
cusp::gallery::poisson5pt(A, 256, 256);
// 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 = 1000, relative_tolerance = 1e-6)
cusp::default_monitor<ValueType> monitor(b, 1000, 1e-6);
// solve
cusp::krylov::cg(A, x, b, monitor);
// report status
report_status(monitor);
}
// solve AINV preconditioner, using standard drop tolerance strategy
{
std::cout << "\nSolving with scaled bridson preconditioner (drop tolerance .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 = 1000, relative_tolerance = 1e-6)
cusp::default_monitor<ValueType> monitor(b, 1000, 1e-6);
// setup preconditioner
cusp::precond::scaled_bridson_ainv<ValueType, MemorySpace> M(A, .1);
// solve
cusp::krylov::cg(A, x, b, monitor, M);
// report status
report_status(monitor);
}
// solve AINV preconditioner, using static dropping strategy
{
std::cout << "\nSolving with scaled bridson preconditioner (10 nonzeroes per row)" << 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 = 1000, relative_tolerance = 1e-6)
cusp::default_monitor<ValueType> monitor(b, 1000, 1e-6);
// setup preconditioner
cusp::precond::scaled_bridson_ainv<ValueType, MemorySpace> M(A, 0, 10);
// solve
cusp::krylov::cg(A, x, b, monitor, M);
// report status
report_status(monitor);
}
// solve AINV preconditioner, using novel dropping strategy
{
std::cout << "\nSolving with AINV preconditioner (Lin strategy, p=2)" << 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 = 1000, relative_tolerance = 1e-6)
cusp::default_monitor<ValueType> monitor(b, 1000, 1e-6);
// setup preconditioner
cusp::precond::bridson_ainv<ValueType, MemorySpace> M(A, 0, -1, true, 2);
// solve
cusp::krylov::cg(A, x, b, monitor, M);
// report status
report_status(monitor);
}
return 0;
}
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