EVPSparse_slepcd.cpp File Reference

Solves a 2x2 generalised eigenvalue problem. More...

#include <SparseLinearEigenSystem.h>
#include <SlepcSession.h>

Go to the source code of this file.

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

Detailed Description

Solves a 2x2 generalised eigenvalue problem.

for the 2 eigenvalues , . As a test case we use the SLEPc library. In this case and are such that the eigenvalues are . The computation requests eigenvalues that satisfy .

Definition in file EVPSparse_slepcd.cpp.

Function Documentation

main()

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

Definition at line 18 of file EVPSparse_slepcd.cpp.

{
 
  SlepcSession::getInstance(argc,argv);
 
  cout << "\n";
  cout << "=== EVP: real eigenvalue problem                 ====\n";
  cout << "\n";
 
  SparseMatrix<double> a( 2, 2 );
  SparseMatrix<double> b( 2, 2 );
  a( 0, 0 ) = 1;
  a( 0, 1 ) = 2;
  a( 1, 0 ) = 4;
  a( 1, 1 ) = 3;
 
  b( 0, 0 ) = 1;
  b( 1, 1 ) = 1;
 
  // a vector for the eigenvalues
  DenseVector<D_complex> lambdas;
  // eigenvalues are: 5,-1
 
  SparseLinearEigenSystem<double> system( &a, &b );
  try
  {
    system.eigensolve();
  }
  catch (const std::runtime_error &error )
  {
    cout << " \033[1;31;48m  * FAILED THROUGH EXCEPTION BEING RAISED \033[0m\n";
    return 1;
  }
 
  //// tag any eigenvalues within a distance of 2 of the point 4
  system.set_shift( 4 );
  system.tag_eigenvalues_disc( +1, 2 );
  // get those tagged eigenvalues
  lambdas = system.get_tagged_eigenvalues();
  const double tol = 1.e-13;
  //lambdas.dump();
 
  if ( std::abs( lambdas[ 0 ] - 5.0 ) < tol )
  {
    cout << "\033[1;32;48m  * PASSED \033[0m\n";
    return 0;
  }
 
  cout << "\033[1;31;48m  * FAILED \033[0m\n";
  cout.precision( 12 );
  cout << "    Final error = " << std::abs( lambdas[ 0 ] - 5.0 ) << "\n";
  lambdas.dump();
  return 1;
}

References CppNoddy::DenseVector< _Type >::dump().