Merge branch 'TO/linear-solvers-doc' into 'develop'

ADD_SUBDIRECTORY( Containers )

ADD_SUBDIRECTORY( Pointers )

ADD_SUBDIRECTORY( Matrices )

ADD_SUBDIRECTORY( Solvers )

set( COMMON_EXAMPLES )

add_subdirectory( Linear )

set( COMMON_EXAMPLES

   IterativeLinearSolverExample

   IterativeLinearSolverWithMonitorExample

   IterativeLinearSolverWithTimerExample

   IterativeLinearSolverWithPreconditionerExample

   IterativeLinearSolverWithRuntimeTypesExample

)

if( BUILD_CUDA )

   foreach( target IN ITEMS ${COMMON_EXAMPLES} )

      cuda_add_executable( ${target}-cuda ${target}.cu OPTIONS )

      add_custom_command( COMMAND ${target}-cuda > ${TNL_DOCUMENTATION_OUTPUT_SNIPPETS_PATH}/${target}.out OUTPUT ${target}.out )

      set( CUDA_OUTPUTS ${CUDA_OUTPUTS} ${target}.out )

   endforeach()

else()

   foreach( target IN ITEMS ${COMMON_EXAMPLES} )

      add_executable( ${target} ${target}.cpp )

      add_custom_command( COMMAND ${target} > ${TNL_DOCUMENTATION_OUTPUT_SNIPPETS_PATH}/${target}.out OUTPUT ${target}.out )

      set( HOST_OUTPUTS ${HOST_OUTPUTS} ${target}.out )

   endforeach()

endif()

IF( BUILD_CUDA )

   ADD_CUSTOM_TARGET( RunLinearSolversExamples-cuda ALL DEPENDS ${CUDA_OUTPUTS} )

ELSE()

   ADD_CUSTOM_TARGET( RunLinearSolversExamples ALL DEPENDS ${HOST_OUTPUTS} )

ENDIF()

#include <iostream>

#include <memory>

#include <TNL/Algorithms/ParallelFor.h>

#include <TNL/Matrices/SparseMatrix.h>

#include <TNL/Devices/Host.h>

#include <TNL/Devices/Cuda.h>

#include <TNL/Solvers/Linear/TFQMR.h>

template< typename Device >

void iterativeLinearSolverExample()

{

   /***

    * Set the following matrix (dots represent zero matrix elements):

    *

    *   /  2.5 -1    .    .    .   \

    *   | -1    2.5 -1    .    .   |

    *   |  .   -1    2.5 -1.   .   |

    *   |  .    .   -1    2.5 -1   |

    *   \  .    .    .   -1    2.5 /

    */

   using MatrixType = TNL::Matrices::SparseMatrix< double, Device >;

   using Vector = TNL::Containers::Vector< double, Device >;

   const int size( 5 );

   auto matrix_ptr = std::make_shared< MatrixType >();

   matrix_ptr->setDimensions( size, size );

   matrix_ptr->setRowCapacities( Vector( { 2, 3, 3, 3, 2 } ) );

   auto f = [=] __cuda_callable__ ( typename MatrixType::RowView& row ) mutable {

      const int rowIdx = row.getRowIndex();

      if( rowIdx == 0 )

      {

         row.setElement( 0, rowIdx,    2.5 );    // diagonal element

         row.setElement( 1, rowIdx+1, -1 );      // element above the diagonal

      }

      else if( rowIdx == size - 1 )

      {

         row.setElement( 0, rowIdx-1, -1.0 );    // element below the diagonal

         row.setElement( 1, rowIdx,    2.5 );    // diagonal element

      }

      else

      {

         row.setElement( 0, rowIdx-1, -1.0 );    // element below the diagonal

         row.setElement( 1, rowIdx,    2.5 );    // diagonal element

         row.setElement( 2, rowIdx+1, -1.0 );    // element above the diagonal

      }

   };

   /***

    * Set the matrix elements.

    */

   matrix_ptr->forAllRows( f );

   std::cout << *matrix_ptr << std::endl;

   /***

    * Set the right-hand side vector.

    */

   Vector x( size, 1.0 );

   Vector b( size );

   matrix_ptr->vectorProduct( x, b );

   x = 0.0;

   std::cout << "Vector b = " << b << std::endl;

   /***

    * Solve the linear system.

    */

   using LinearSolver = TNL::Solvers::Linear::TFQMR< MatrixType >;

   LinearSolver solver;

   solver.setMatrix( matrix_ptr );

   solver.solve( b, x );

   std::cout << "Vector x = " << x << std::endl;

}

int main( int argc, char* argv[] )

{

   std::cout << "Solving linear system on host: " << std::endl;

   iterativeLinearSolverExample< TNL::Devices::Sequential >();

#ifdef HAVE_CUDA

   std::cout << "Solving linear system on CUDA device: " << std::endl;

   iterativeLinearSolverExample< TNL::Devices::Cuda >();

#endif

}

IterativeLinearSolverExample.cpp

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