Matrix adapters in AMGCL allow to construct a solver from some common matrix formats. Internally, the CRS format is used, but it is easy to adapt any matrix format that allows row-wise access to the nonzero matrix values. An example of creating an adapter is provided in Adapting custom matrix class.

#include <amgcl/adapter/crs_tuple.hpp>

The Boost tuple adapter allows to use a std::tuple of a matrix size and its three CRS format components (row pointer array, column indices array, and values array) as input matrix to AMGCL solvers. The arrays are allowed to be in any format recognized by the Boost.Range library as a random access range. Common examples are STL vectors and Boost iterator ranges.

Example:

// std::tie creates a tuple of references, which avoids copying.
Solver solve( std::tie(n, ptr, col, val) );

// A (cheap) copy is required when iterator ranges are created on the fly:
Solver solve( std::make_tuple(
n,
boost::make_iterator_range(ptr.data(), ptr.data() + ptr.size()),
boost::make_iterator_range(col.data(), col.data() + col.size()),
boost::make_iterator_range(val.data(), val.data() + val.size())
) );


#include <amgcl/adapter/ublas.hpp>

The Boost.uBLAS adapter allows to use uBLAS sparse matrices as input to AMGCL solvers. It also allows to use uBLAS dense vectors with amgcl::backend::builtin.

Example:

namespace ublas = boost::numeric::ublas;

ublas::compressed_matrix<double> A;
...
Solver solve(A);

ublas::vector<double> rhs, x;
...
solve(rhs, x);


#include <amgcl/adapter/zero_copy.hpp>
In general, AMGCL copies the adapted input matrix into its internal structures, so that the matrix may be safely destroyed or reused as soon as the solver setup is complete. However, the memory overhead of the copying may be too large, especially for large problems that eat up almost all of available RAM. The zero copy adapter allows to use raw pointers to CRS arrays as input matrix for MAGCL solvers. The data from the arrays is never copied during setup, and the user has to make sure the arrays stay alive long enough. However, unless the backend used is amgcl::backend::builtin, the input matrix will be copied into the backend structures when the setup is finished. This would still allow to save some memory in case of GPGPU backends.
The one requirement is that the integer types stored in row pointers and column indices arrays have to be binary compatible with ptrdiff_t, and the value type has to be the value type of the backend.
Solver solve( amgcl::adapter::zero_copy(n, &ptr[0], &col[0], &val[0]) );