/* ************************************************************************* */
boost::shared_ptr<GaussianFactor>
LinearizedJacobianFactor::linearize(const Values& c) const {
// Create the 'terms' data structure for the Jacobian constructor
std::vector<std::pair<Key, Matrix> > terms;
for(Key key: keys()) {
terms.push_back(std::make_pair(key, this->A(key)));
}
// compute rhs
Vector b = -error_vector(c);
return boost::shared_ptr<GaussianFactor>(new JacobianFactor(terms, b, noiseModel::Unit::Create(dim())));
}
TEUCHOS_UNIT_TEST( MCSA, MCSA_test)
{
int problem_size = 16;
Epetra_SerialComm comm;
Epetra_Map map( problem_size, 0, comm );
std::vector<double> x_vector( problem_size );
Epetra_Vector x( View, map, &x_vector[0] );
std::vector<double> x_aztec_vector( problem_size );
Epetra_Vector x_aztec( View, map, &x_aztec_vector[0] );
std::vector<double> b_vector( problem_size, 0.4 );
Epetra_Vector b( View, map, &b_vector[0] );
Teuchos::RCP<Epetra_CrsMatrix> A =
Teuchos::rcp( new Epetra_CrsMatrix( Copy, map, problem_size ) );
double lower_diag = -0.1;
double diag = 2.4;
double upper_diag = -0.1;
int global_row = 0;
int lower_row = 0;
int upper_row = 0;
for ( int i = 0; i < problem_size; ++i )
{
global_row = A->GRID(i);
lower_row = i-1;
upper_row = i+1;
if ( lower_row > -1 )
{
A->InsertGlobalValues( global_row, 1, &lower_diag, &lower_row );
}
A->InsertGlobalValues( global_row, 1, &diag, &global_row );
if ( upper_row < problem_size )
{
A->InsertGlobalValues( global_row, 1, &upper_diag, &upper_row );
}
}
A->FillComplete();
double spec_rad_A = HMCSA::OperatorTools::spectralRadius( A );
std::cout << std::endl <<
"Operator spectral radius: " << spec_rad_A << std::endl;
Teuchos::RCP<Epetra_LinearProblem> linear_problem = Teuchos::rcp(
new Epetra_LinearProblem( A.getRawPtr(), &x, &b ) );
HMCSA::JacobiPreconditioner preconditioner( linear_problem );
preconditioner.precondition();
HMCSA::MCSA mcsa_solver( linear_problem );
mcsa_solver.iterate( 100, 1.0e-8, 100, 1.0e-8 );
std::cout << "MCSA ITERS: " << mcsa_solver.getNumIters() << std::endl;
Epetra_LinearProblem aztec_linear_problem( A.getRawPtr(), &x_aztec, &b );
AztecOO aztec_solver( aztec_linear_problem );
aztec_solver.SetAztecOption( AZ_solver, AZ_gmres );
aztec_solver.Iterate( 100, 1.0e-8 );
std::vector<double> error_vector( problem_size );
Epetra_Vector error( View, map, &error_vector[0] );
for (int i = 0; i < problem_size; ++i)
{
error[i] = x[i] - x_aztec[i];
}
double error_norm;
error.Norm2( &error_norm );
std::cout << std::endl <<
"Aztec GMRES vs. MCSA absolute error L2 norm: " <<
error_norm << std::endl;
}
/* ************************************************************************* */
double LinearizedJacobianFactor::error(const Values& c) const {
Vector errorVector = error_vector(c);
return 0.5 * errorVector.dot(errorVector);
}
