int main(int argc, char *argv[]) {
//
bool haveM = false;
#ifdef EPETRA_MPI
// Initialize MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm( MPI_COMM_WORLD );
#else
Epetra_SerialComm Comm;
#endif
int MyPID = Comm.MyPID();
int nev = 5;
int blockSize = 5;
int maxIterations = 1000;
double tol = 1.0e-8;
bool verbose=false, locking=false, fullOrtho=true;
std::string k_filename = "";
std::string m_filename = "";
std::string which = "SM";
Teuchos::CommandLineProcessor cmdp(false,true);
cmdp.setOption("nev",&nev,"Number of eigenvalues to compute.");
cmdp.setOption("blocksize",&blockSize,"Block size used in LOBPCG.");
cmdp.setOption("maxiters",&maxIterations,"Maximum number of iterations used in LOBPCG.");
cmdp.setOption("tol",&tol,"Convergence tolerance requested for computed eigenvalues.");
cmdp.setOption("verbose","quiet",&verbose,"Print messages and results.");
cmdp.setOption("locking","nolocking",&locking,"Use locking of converged eigenvalues.");
cmdp.setOption("fullortho","nofullortho",&fullOrtho,"Use full orthogonalization.");
cmdp.setOption("sort",&which,"Targetted eigenvalues (SM,LM,SR,or LR).");
cmdp.setOption("K-filename",&k_filename,"Filename and path of the stiffness matrix.");
cmdp.setOption("M-filename",&m_filename,"Filename and path of the mass matrix.");
if (cmdp.parse(argc,argv) != Teuchos::CommandLineProcessor::PARSE_SUCCESSFUL) {
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return -1;
}
if (k_filename=="") {
cout << "The matrix K must be supplied through an input file!!!" << endl;
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return -1;
}
if (m_filename!="") {
haveM = true;
}
//
//**********************************************************************
//******************Set up the problem to be solved*********************
//**********************************************************************
//
// *****Read in matrix from file******
//
Teuchos::RCP<Epetra_Map> Map;
Teuchos::RCP<Epetra_CrsMatrix> K, M;
EpetraExt::readEpetraLinearSystem( k_filename, Comm, &K, &Map );
if (haveM) {
EpetraExt::readEpetraLinearSystem( m_filename, Comm, &M, &Map );
}
//
//************************************
// Start the block Davidson iteration
//***********************************
//
// Variables used for the LOBPCG Method
//
// Set verbosity level
int verbosity = Anasazi::Errors + Anasazi::Warnings;
if (verbose) {
verbosity += Anasazi::FinalSummary + Anasazi::TimingDetails;
}
//
// Create parameter list to pass into solver
//
Teuchos::ParameterList MyPL;
MyPL.set( "Verbosity", verbosity );
MyPL.set( "Which", which );
MyPL.set( "Block Size", blockSize );
MyPL.set( "Maximum Iterations", maxIterations );
MyPL.set( "Convergence Tolerance", tol );
MyPL.set( "Use Locking", locking );
MyPL.set( "Locking Tolerance", tol/10 );
MyPL.set( "Full Ortho", fullOrtho );
typedef Epetra_MultiVector MV;
typedef Epetra_Operator OP;
typedef Anasazi::MultiVecTraits<double, MV> MVT;
typedef Anasazi::OperatorTraits<double, MV, OP> OPT;
//
// Create the eigenproblem to be solved.
//
Teuchos::RCP<Epetra_MultiVector> ivec = Teuchos::rcp( new Epetra_MultiVector(*Map, blockSize) );
ivec->Random();
Teuchos::RCP<Anasazi::BasicEigenproblem<double, MV, OP> > MyProblem;
if (haveM) {
MyProblem = Teuchos::rcp( new Anasazi::BasicEigenproblem<double, MV, OP>( K, M, ivec ) );
}
else {
MyProblem = Teuchos::rcp( new Anasazi::BasicEigenproblem<double, MV, OP>( K, ivec ) );
}
// Inform the eigenproblem that (K,M) is Hermitian
MyProblem->setHermitian(true);
// Set the number of eigenvalues requested
MyProblem->setNEV( nev );
// Inform the eigenproblem that you are finished passing it information
bool boolret = MyProblem->setProblem();
if (boolret != true) {
if (verbose && MyPID == 0) {
cout << "Anasazi::BasicEigenproblem::setProblem() returned with error." << endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return -1;
}
// Initialize the LOBPCG solver
Anasazi::LOBPCGSolMgr<double, MV, OP> MySolverMgr(MyProblem, MyPL);
// Solve the problem to the specified tolerances or length
Anasazi::ReturnType returnCode = MySolverMgr.solve();
if (returnCode != Anasazi::Converged && MyPID==0 && verbose) {
cout << "Anasazi::EigensolverMgr::solve() returned unconverged." << endl;
}
// Get the eigenvalues and eigenvectors from the eigenproblem
Anasazi::Eigensolution<double,MV> sol = MyProblem->getSolution();
std::vector<Anasazi::Value<double> > evals = sol.Evals;
Teuchos::RCP<MV> evecs = sol.Evecs;
std::vector<int> index = sol.index;
int numev = sol.numVecs;
if (numev > 0) {
// Compute residuals.
Teuchos::LAPACK<int,double> lapack;
std::vector<double> normEV(numev);
// Get storage
Teuchos::RCP<Epetra_MultiVector> Kevecs, Mevecs;
Teuchos::SerialDenseMatrix<int,double> B(numev,numev);
B.putScalar(0.0);
for (int i=0; i<numev; i++) {B(i,i) = evals[i].realpart;}
// Compute K*evecs
Kevecs = Teuchos::rcp(new Epetra_MultiVector(*Map,numev) );
OPT::Apply( *K, *evecs, *Kevecs );
// Compute M*evecs
if (haveM) {
Mevecs = Teuchos::rcp(new Epetra_MultiVector(*Map,numev) );
OPT::Apply( *M, *evecs, *Mevecs );
}
else {
Mevecs = evecs;
}
// Compute K*evecs - lambda*M*evecs and its norm
MVT::MvTimesMatAddMv( -1.0, *Mevecs, B, 1.0, *Kevecs );
MVT::MvNorm( *Kevecs, normEV );
// Scale the norms by the eigenvalue
for (int i=0; i<numev; i++) {
normEV[i] /= Teuchos::ScalarTraits<double>::magnitude( evals[i].realpart );
}
// Output computed eigenvalues and their direct residuals
if (verbose && MyPID==0) {
cout.setf(std::ios_base::right, std::ios_base::adjustfield);
cout<<endl<< "Actual Residuals"<<endl;
cout<< std::setw(16) << "Real Part"
<< std::setw(20) << "Direct Residual"<< endl;
cout<<"-----------------------------------------------------------"<<endl;
for (int i=0; i<numev; i++) {
cout<< std::setw(16) << evals[i].realpart
<< std::setw(20) << normEV[i] << endl;
}
cout<<"-----------------------------------------------------------"<<endl;
}
}
#ifdef EPETRA_MPI
MPI_Finalize() ;
#endif
return 0;
} // end LOBPCGEpetraExFile.cpp
int main(int argc, char *argv[]) {
#ifdef EPETRA_MPI
// Initialize MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
bool testFailed;
bool boolret;
int MyPID = Comm.MyPID();
bool verbose = true;
bool debug = false;
std::string which("SM");
Teuchos::CommandLineProcessor cmdp(false,true);
cmdp.setOption("verbose","quiet",&verbose,"Print messages and results.");
cmdp.setOption("debug","nodebug",&debug,"Print debugging information.");
cmdp.setOption("sort",&which,"Targetted eigenvalues (SM,LM,SR,LR,SI,or LI).");
if (cmdp.parse(argc,argv) != Teuchos::CommandLineProcessor::PARSE_SUCCESSFUL) {
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return -1;
}
typedef double ScalarType;
typedef Teuchos::ScalarTraits<ScalarType> ScalarTypeTraits;
typedef ScalarTypeTraits::magnitudeType MagnitudeType;
typedef Epetra_MultiVector MV;
typedef Epetra_Operator OP;
typedef Anasazi::MultiVecTraits<ScalarType,MV> MVTraits;
typedef Anasazi::OperatorTraits<ScalarType,MV,OP> OpTraits;
// Dimension of the matrix
int nx = 10; // Discretization points in any one direction.
int NumGlobalElements = nx*nx; // Size of matrix nx*nx
// Construct a Map that puts approximately the same number of
// equations on each processor.
Epetra_Map Map(NumGlobalElements, 0, Comm);
// Get update list and number of local equations from newly created Map.
int NumMyElements = Map.NumMyElements();
std::vector<int> MyGlobalElements(NumMyElements);
Map.MyGlobalElements(&MyGlobalElements[0]);
// Create an integer vector NumNz that is used to build the Petra Matrix.
// NumNz[i] is the Number of OFF-DIAGONAL term for the ith global equation
// on this processor
std::vector<int> NumNz(NumMyElements);
/* We are building a matrix of block structure:
| T -I |
|-I T -I |
| -I T |
| ... -I|
| -I T|
where each block is dimension nx by nx and the matrix is on the order of
nx*nx. The block T is a tridiagonal matrix.
*/
for (int i=0; i<NumMyElements; i++) {
if (MyGlobalElements[i] == 0 || MyGlobalElements[i] == NumGlobalElements-1 ||
MyGlobalElements[i] == nx-1 || MyGlobalElements[i] == nx*(nx-1) ) {
NumNz[i] = 3;
}
else if (MyGlobalElements[i] < nx || MyGlobalElements[i] > nx*(nx-1) ||
MyGlobalElements[i]%nx == 0 || (MyGlobalElements[i]+1)%nx == 0) {
NumNz[i] = 4;
}
else {
NumNz[i] = 5;
}
}
// Create an Epetra_Matrix
Teuchos::RCP<Epetra_CrsMatrix> A = Teuchos::rcp( new Epetra_CrsMatrix(Copy, Map, &NumNz[0]) );
// Diffusion coefficient, can be set by user.
// When rho*h/2 <= 1, the discrete convection-diffusion operator has real eigenvalues.
// When rho*h/2 > 1, the operator has complex eigenvalues.
double rho = 2*(nx+1);
// Compute coefficients for discrete convection-diffution operator
const double one = 1.0;
std::vector<double> Values(4);
std::vector<int> Indices(4);
double h = one /(nx+1);
double h2 = h*h;
double c = 5.0e-01*rho/ h;
Values[0] = -one/h2 - c; Values[1] = -one/h2 + c; Values[2] = -one/h2; Values[3]= -one/h2;
double diag = 4.0 / h2;
int NumEntries, info;
for (int i=0; i<NumMyElements; i++)
{
if (MyGlobalElements[i]==0)
{
Indices[0] = 1;
Indices[1] = nx;
NumEntries = 2;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[1], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i] == nx*(nx-1))
{
Indices[0] = nx*(nx-1)+1;
Indices[1] = nx*(nx-2);
NumEntries = 2;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[1], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i] == nx-1)
{
Indices[0] = nx-2;
NumEntries = 1;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
Indices[0] = 2*nx-1;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[2], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i] == NumGlobalElements-1)
{
Indices[0] = NumGlobalElements-2;
NumEntries = 1;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
Indices[0] = nx*(nx-1)-1;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[2], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i] < nx)
{
Indices[0] = MyGlobalElements[i]-1;
Indices[1] = MyGlobalElements[i]+1;
Indices[2] = MyGlobalElements[i]+nx;
NumEntries = 3;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i] > nx*(nx-1))
{
Indices[0] = MyGlobalElements[i]-1;
Indices[1] = MyGlobalElements[i]+1;
Indices[2] = MyGlobalElements[i]-nx;
NumEntries = 3;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
}
else if (MyGlobalElements[i]%nx == 0)
{
Indices[0] = MyGlobalElements[i]+1;
Indices[1] = MyGlobalElements[i]-nx;
Indices[2] = MyGlobalElements[i]+nx;
NumEntries = 3;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[1], &Indices[0]);
assert( info==0 );
}
else if ((MyGlobalElements[i]+1)%nx == 0)
{
Indices[0] = MyGlobalElements[i]-nx;
Indices[1] = MyGlobalElements[i]+nx;
NumEntries = 2;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[2], &Indices[0]);
assert( info==0 );
Indices[0] = MyGlobalElements[i]-1;
NumEntries = 1;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
}
else
{
Indices[0] = MyGlobalElements[i]-1;
Indices[1] = MyGlobalElements[i]+1;
Indices[2] = MyGlobalElements[i]-nx;
Indices[3] = MyGlobalElements[i]+nx;
NumEntries = 4;
info = A->InsertGlobalValues(MyGlobalElements[i], NumEntries, &Values[0], &Indices[0]);
assert( info==0 );
}
// Put in the diagonal entry
info = A->InsertGlobalValues(MyGlobalElements[i], 1, &diag, &MyGlobalElements[i]);
assert( info==0 );
}
// Finish up
info = A->FillComplete();
assert( info==0 );
A->SetTracebackMode(1); // Shutdown Epetra Warning tracebacks
//************************************
// Start the block Davidson iteration
//***********************************
//
// Variables used for the Generalized Davidson Method
//
int nev = 4;
int blockSize = 1;
int maxDim = 50;
int restartDim = 10;
int maxRestarts = 500;
double tol = 1e-10;
// Set verbosity level
int verbosity = Anasazi::Errors + Anasazi::Warnings;
if (verbose) {
verbosity += Anasazi::FinalSummary + Anasazi::TimingDetails;
}
if (debug) {
verbosity += Anasazi::Debug;
}
//
// Create parameter list to pass into solver manager
//
Teuchos::ParameterList MyPL;
MyPL.set( "Verbosity", verbosity );
MyPL.set( "Which", which );
MyPL.set( "Block Size", blockSize );
MyPL.set( "Maximum Subspace Dimension", maxDim);
MyPL.set( "Restart Dimension", restartDim);
MyPL.set( "Maximum Restarts", maxRestarts );
MyPL.set( "Convergence Tolerance", tol );
MyPL.set( "Relative Convergence Tolerance", true );
MyPL.set( "Initial Guess", "User" );
// Create an Epetra_MultiVector for an initial vector to start the solver.
// Note: This needs to have the same number of columns as the blocksize.
Teuchos::RCP<Epetra_MultiVector> ivec = Teuchos::rcp( new Epetra_MultiVector(Map, blockSize) );
ivec->Random();
// Create the eigenproblem.
Teuchos::RCP<Anasazi::BasicEigenproblem<double, MV, OP> > MyProblem = Teuchos::rcp(
new Anasazi::BasicEigenproblem<double,MV,OP>() );
MyProblem->setA(A);
MyProblem->setInitVec(ivec);
// Inform the eigenproblem that the operator A is non-Hermitian
MyProblem->setHermitian(false);
// Set the number of eigenvalues requested
MyProblem->setNEV( nev );
// Inform the eigenproblem that you are finishing passing it information
boolret = MyProblem->setProblem();
if (boolret != true) {
if (verbose && MyPID == 0) {
std::cout << "Anasazi::BasicEigenproblem::setProblem() returned with error." << std::endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return -1;
}
// Initialize the Block Arnoldi solver
Anasazi::GeneralizedDavidsonSolMgr<double, MV, OP> MySolverMgr(MyProblem, MyPL);
// Solve the problem to the specified tolerances or length
Anasazi::ReturnType returnCode = MySolverMgr.solve();
testFailed = false;
if (returnCode != Anasazi::Converged && MyPID==0 && verbose) {
testFailed = true;
}
// Get the eigenvalues and eigenvectors from the eigenproblem
Anasazi::Eigensolution<ScalarType,MV> sol = MyProblem->getSolution();
std::vector<Anasazi::Value<ScalarType> > evals = sol.Evals;
Teuchos::RCP<MV> evecs = sol.Evecs;
std::vector<int> index = sol.index;
int numev = sol.numVecs;
// Output computed eigenvalues and their direct residuals
if (verbose && MyPID==0) {
int numritz = (int)evals.size();
std::cout.setf(std::ios_base::right, std::ios_base::adjustfield);
std::cout<<std::endl<< "Computed Ritz Values"<< std::endl;
std::cout<< std::setw(16) << "Real Part"
<< std::setw(16) << "Imag Part"
<< std::endl;
std::cout<<"-----------------------------------------------------------"<<std::endl;
for (int i=0; i<numritz; i++) {
std::cout<< std::setw(16) << evals[i].realpart
<< std::setw(16) << evals[i].imagpart
<< std::endl;
}
std::cout<<"-----------------------------------------------------------"<<std::endl;
}
if (numev > 0) {
// Compute residuals.
Teuchos::LAPACK<int,double> lapack;
std::vector<double> normA(numev);
// The problem is non-Hermitian.
int i=0;
std::vector<int> curind(1);
std::vector<double> resnorm(1), tempnrm(1);
Teuchos::RCP<MV> tempAevec;
Teuchos::RCP<const MV> evecr, eveci;
Epetra_MultiVector Aevec(Map,numev);
// Compute A*evecs
OpTraits::Apply( *A, *evecs, Aevec );
Teuchos::SerialDenseMatrix<int,double> Breal(1,1), Bimag(1,1);
while (i<numev) {
if (index[i]==0) {
// Get a view of the current eigenvector (evecr)
curind[0] = i;
evecr = MVTraits::CloneView( *evecs, curind );
// Get a copy of A*evecr
tempAevec = MVTraits::CloneCopy( Aevec, curind );
// Compute A*evecr - lambda*evecr
Breal(0,0) = evals[i].realpart;
MVTraits::MvTimesMatAddMv( -1.0, *evecr, Breal, 1.0, *tempAevec );
// Compute the norm of the residual and increment counter
MVTraits::MvNorm( *tempAevec, resnorm );
normA[i] = resnorm[0] / Teuchos::ScalarTraits<MagnitudeType>::magnitude( evals[i].realpart );
i++;
} else {
// Get a view of the real part of the eigenvector (evecr)
curind[0] = i;
evecr = MVTraits::CloneView( *evecs, curind );
// Get a copy of A*evecr
tempAevec = MVTraits::CloneCopy( Aevec, curind );
// Get a view of the imaginary part of the eigenvector (eveci)
curind[0] = i+1;
eveci = MVTraits::CloneView( *evecs, curind );
// Set the eigenvalue into Breal and Bimag
Breal(0,0) = evals[i].realpart;
Bimag(0,0) = evals[i].imagpart;
// Compute A*evecr - evecr*lambdar + eveci*lambdai
MVTraits::MvTimesMatAddMv( -1.0, *evecr, Breal, 1.0, *tempAevec );
MVTraits::MvTimesMatAddMv( 1.0, *eveci, Bimag, 1.0, *tempAevec );
MVTraits::MvNorm( *tempAevec, tempnrm );
// Get a copy of A*eveci
tempAevec = MVTraits::CloneCopy( Aevec, curind );
// Compute A*eveci - eveci*lambdar - evecr*lambdai
MVTraits::MvTimesMatAddMv( -1.0, *evecr, Bimag, 1.0, *tempAevec );
MVTraits::MvTimesMatAddMv( -1.0, *eveci, Breal, 1.0, *tempAevec );
MVTraits::MvNorm( *tempAevec, resnorm );
// Compute the norms and scale by magnitude of eigenvalue
normA[i] = lapack.LAPY2( tempnrm[0], resnorm[0] ) /
lapack.LAPY2( evals[i].realpart, evals[i].imagpart );
normA[i+1] = normA[i];
i=i+2;
}
}
// Output computed eigenvalues and their direct residuals
if (verbose && MyPID==0) {
std::cout.setf(std::ios_base::right, std::ios_base::adjustfield);
std::cout<<std::endl<< "Actual Residuals"<<std::endl;
std::cout<< std::setw(16) << "Real Part"
<< std::setw(16) << "Imag Part"
<< std::setw(20) << "Direct Residual"<< std::endl;
std::cout<<"-----------------------------------------------------------"<<std::endl;
for (int j=0; j<numev; j++) {
std::cout<< std::setw(16) << evals[j].realpart
<< std::setw(16) << evals[j].imagpart
<< std::setw(20) << normA[j] << std::endl;
if ( normA[j] > tol ) {
testFailed = true;
}
}
std::cout<<"-----------------------------------------------------------"<<std::endl;
}
}
#ifdef EPETRA_MPI
MPI_Finalize();
#endif
if (testFailed) {
if (verbose && MyPID==0) {
std::cout << "End Result: TEST FAILED" << std::endl;
}
return -1;
}
//
// Default return value
//
if (verbose && MyPID==0) {
std::cout << "End Result: TEST PASSED" << std::endl;
}
return 0;
}
int main(int argc, char *argv[]) {
int i;
#ifdef EPETRA_MPI
// Initialize MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
int MyPID = Comm.MyPID();
// Number of dimension of the domain
int space_dim = 2;
// Size of each of the dimensions of the domain
std::vector<double> brick_dim( space_dim );
brick_dim[0] = 1.0;
brick_dim[1] = 1.0;
// Number of elements in each of the dimensions of the domain
std::vector<int> elements( space_dim );
elements[0] = 10;
elements[1] = 10;
// Create problem
Teuchos::RCP<ModalProblem> testCase = Teuchos::rcp( new ModeLaplace2DQ2(Comm, brick_dim[0], elements[0], brick_dim[1], elements[1]) );
// Get the stiffness and mass matrices
Teuchos::RCP<Epetra_CrsMatrix> K = Teuchos::rcp( const_cast<Epetra_CrsMatrix *>(testCase->getStiffness()), false );
Teuchos::RCP<Epetra_CrsMatrix> M = Teuchos::rcp( const_cast<Epetra_CrsMatrix *>(testCase->getMass()), false );
//
// *******************************************************
// Set up Amesos direct solver for inner iteration
// *******************************************************
//
// Create the shifted system K - sigma * M.
// For the buckling transformation, this shift must be nonzero.
double sigma = 1.0;
Epetra_CrsMatrix Kcopy( *K );
int addErr = EpetraExt::MatrixMatrix::Add( *M, false, -sigma, Kcopy, 1.0 );
if (addErr != 0) {
if (MyPID == 0) {
std::cout << "EpetraExt::MatrixMatrix::Add returned with error: " << addErr << std::endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return -1;
}
// Create Epetra linear problem class to solve "x = b"
Epetra_LinearProblem AmesosProblem;
AmesosProblem.SetOperator(&Kcopy);
// Create Amesos factory and solver for solving "(K - sigma*M)x = b" using a direct factorization
Amesos amesosFactory;
Teuchos::RCP<Amesos_BaseSolver> AmesosSolver =
Teuchos::rcp( amesosFactory.Create( "Klu", AmesosProblem ) );
// The AmesosBucklingOp class assumes that the symbolic/numeric factorizations have already
// been performed on the linear problem.
AmesosSolver->SymbolicFactorization();
AmesosSolver->NumericFactorization();
//
// ************************************
// Start the block Arnoldi iteration
// ************************************
//
// Variables used for the Block Arnoldi Method
//
int nev = 10;
int blockSize = 3;
int numBlocks = 3*nev/blockSize;
int maxRestarts = 5;
//int step = 5;
double tol = 1.0e-8;
std::string which = "LM";
int verbosity = Anasazi::Errors + Anasazi::Warnings + Anasazi::FinalSummary;
//
// Create parameter list to pass into solver
//
Teuchos::ParameterList MyPL;
MyPL.set( "Verbosity", verbosity );
MyPL.set( "Which", which );
MyPL.set( "Block Size", blockSize );
MyPL.set( "Num Blocks", numBlocks );
MyPL.set( "Maximum Restarts", maxRestarts );
MyPL.set( "Convergence Tolerance", tol );
//MyPL.set( "Step Size", step );
typedef Epetra_MultiVector MV;
typedef Epetra_Operator OP;
typedef Anasazi::MultiVecTraits<double, MV> MVT;
typedef Anasazi::OperatorTraits<double, MV, OP> OPT;
// Create an Epetra_MultiVector for an initial vector to start the solver.
// Note: This needs to have the same number of columns as the blocksize.
Teuchos::RCP<Epetra_MultiVector> ivec = Teuchos::rcp( new Epetra_MultiVector(K->Map(), blockSize) );
MVT::MvRandom( *ivec );
// Create the Epetra_Operator for the buckling transformation using the Amesos direct solver.
Teuchos::RCP<AmesosBucklingOp> BucklingOp
= Teuchos::rcp( new AmesosBucklingOp(AmesosProblem, AmesosSolver, K) );
Teuchos::RCP<Anasazi::BasicEigenproblem<double,MV,OP> > MyProblem =
Teuchos::rcp( new Anasazi::BasicEigenproblem<double,MV,OP>(BucklingOp, K, ivec) );
// Inform the eigenproblem that the matrix pencil (K,M) is symmetric
MyProblem->setHermitian(true);
// Set the number of eigenvalues requested
MyProblem->setNEV( nev );
// Inform the eigenproblem that you are finished passing it information
bool boolret = MyProblem->setProblem();
if (boolret != true) {
if (MyPID == 0) {
std::cout << "Anasazi::BasicEigenproblem::setProblem() returned with error." << std::endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return -1;
}
// Initialize the Block Arnoldi solver
Anasazi::BlockKrylovSchurSolMgr<double, MV, OP> MySolverMgr(MyProblem, MyPL);
// Solve the problem to the specified tolerances or length
Anasazi::ReturnType returnCode = MySolverMgr.solve();
if (returnCode != Anasazi::Converged && MyPID==0) {
std::cout << "Anasazi::EigensolverMgr::solve() returned unconverged." << std::endl;
}
// Get the eigenvalues and eigenvectors from the eigenproblem
Anasazi::Eigensolution<double,MV> sol = MyProblem->getSolution();
std::vector<Anasazi::Value<double> > evals = sol.Evals;
Teuchos::RCP<MV> evecs = sol.Evecs;
int numev = sol.numVecs;
if (numev > 0) {
// Undo buckling transformation; computed eigenvalues are real
std::vector<double> compEvals(numev);
for (i=0; i<numev; ++i) {
compEvals[i] = sigma*evals[i].realpart/(evals[i].realpart-1.0);
}
// Remember, eigenvectors are constructed K-orthogonal to preserve symmetry,
// so numerator of the Rayleigh quotient is 1.0.
Teuchos::SerialDenseMatrix<int,double> dmatr(numev,numev);
Epetra_MultiVector tempvec(M->Map(), MVT::GetNumberVecs( *evecs ));
OPT::Apply( *M, *evecs, tempvec );
MVT::MvTransMv( 1.0, tempvec, *evecs, dmatr );
if (MyPID==0) {
double rq_eval = 0.0;
std::cout.setf(std::ios_base::right, std::ios_base::adjustfield);
std::cout<<"Actual Eigenvalues (obtained by Rayleigh quotient) : "<<std::endl;
std::cout<<"------------------------------------------------------"<<std::endl;
std::cout<<std::setw(16)<<"Real Part"
<<std::setw(16)<<"Rayleigh Error"<<std::endl;
std::cout<<"------------------------------------------------------"<<std::endl;
for (i=0; i<numev; i++) {
rq_eval = 1.0 / dmatr(i,i);
std::cout<<std::setw(16)<<rq_eval
<<std::setw(16)<<Teuchos::ScalarTraits<double>::magnitude(rq_eval-compEvals[i])
<<std::endl;
}
std::cout<<"------------------------------------------------------"<<std::endl;
}
}
#ifdef EPETRA_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char *argv[]) {
int i, j, info;
const double one = 1.0;
const double zero = 0.0;
Teuchos::LAPACK<int,double> lapack;
#ifdef EPETRA_MPI
// Initialize MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
int MyPID = Comm.MyPID();
// Dimension of the matrix
int m = 500;
int n = 100;
// Construct a Map that puts approximately the same number of
// equations on each processor.
Epetra_Map RowMap(m, 0, Comm);
Epetra_Map ColMap(n, 0, Comm);
// Get update list and number of local equations from newly created Map.
int NumMyRowElements = RowMap.NumMyElements();
std::vector<int> MyGlobalRowElements(NumMyRowElements);
RowMap.MyGlobalElements(&MyGlobalRowElements[0]);
/* We are building an m by n matrix with entries
A(i,j) = k*(si)*(tj - 1) if i <= j
= k*(tj)*(si - 1) if i > j
where si = i/(m+1) and tj = j/(n+1) and k = 1/(n+1).
*/
// Create an Epetra_Matrix
Teuchos::RCP<Epetra_CrsMatrix> A = Teuchos::rcp( new Epetra_CrsMatrix(Copy, RowMap, n) );
// Compute coefficients for discrete integral operator
std::vector<double> Values(n);
std::vector<int> Indices(n);
double inv_mp1 = one/(m+1);
double inv_np1 = one/(n+1);
for (i=0; i<n; i++) { Indices[i] = i; }
for (i=0; i<NumMyRowElements; i++) {
//
for (j=0; j<n; j++) {
//
if ( MyGlobalRowElements[i] <= j ) {
Values[j] = inv_np1 * ( (MyGlobalRowElements[i]+one)*inv_mp1 ) * ( (j+one)*inv_np1 - one ); // k*(si)*(tj-1)
}
else {
Values[j] = inv_np1 * ( (j+one)*inv_np1 ) * ( (MyGlobalRowElements[i]+one)*inv_mp1 - one ); // k*(tj)*(si-1)
}
}
info = A->InsertGlobalValues(MyGlobalRowElements[i], n, &Values[0], &Indices[0]);
assert( info==0 );
}
// Finish up
info = A->FillComplete(ColMap, RowMap);
assert( info==0 );
info = A->OptimizeStorage();
assert( info==0 );
A->SetTracebackMode(1); // Shutdown Epetra Warning tracebacks
//************************************
// Start the block Arnoldi iteration
//***********************************
//
// Variables used for the Block Arnoldi Method
//
int nev = 4;
int blockSize = 1;
int numBlocks = 10;
int maxRestarts = 20;
int verbosity = Anasazi::Errors + Anasazi::Warnings + Anasazi::FinalSummary;
double tol = lapack.LAMCH('E');
std::string which = "LM";
//
// Create parameter list to pass into solver
//
Teuchos::ParameterList MyPL;
MyPL.set( "Verbosity", verbosity );
MyPL.set( "Which", which );
MyPL.set( "Block Size", blockSize );
MyPL.set( "Num Blocks", numBlocks );
MyPL.set( "Maximum Restarts", maxRestarts );
MyPL.set( "Convergence Tolerance", tol );
typedef Anasazi::MultiVec<double> MV;
typedef Anasazi::Operator<double> OP;
// Create an Anasazi::EpetraMultiVec for an initial vector to start the solver.
// Note: This needs to have the same number of columns as the blocksize.
Teuchos::RCP<Anasazi::EpetraMultiVec> ivec = Teuchos::rcp( new Anasazi::EpetraMultiVec(ColMap, blockSize) );
ivec->MvRandom();
// Call the constructor for the (A^T*A) operator
Teuchos::RCP<Anasazi::EpetraSymOp> Amat = Teuchos::rcp( new Anasazi::EpetraSymOp(A) );
Teuchos::RCP<Anasazi::BasicEigenproblem<double, MV, OP> > MyProblem =
Teuchos::rcp( new Anasazi::BasicEigenproblem<double, MV, OP>(Amat, ivec) );
// Inform the eigenproblem that the matrix A is symmetric
MyProblem->setHermitian(true);
// Set the number of eigenvalues requested and the blocksize the solver should use
MyProblem->setNEV( nev );
// Inform the eigenproblem that you are finished passing it information
bool boolret = MyProblem->setProblem();
if (boolret != true) {
if (MyPID == 0) {
cout << "Anasazi::BasicEigenproblem::setProblem() returned with error." << endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return -1;
}
// Initialize the Block Arnoldi solver
Anasazi::BlockKrylovSchurSolMgr<double, MV, OP> MySolverMgr(MyProblem, MyPL);
// Solve the problem to the specified tolerances or length
Anasazi::ReturnType returnCode = MySolverMgr.solve();
if (returnCode != Anasazi::Converged && MyPID==0) {
cout << "Anasazi::EigensolverMgr::solve() returned unconverged." << endl;
}
// Get the eigenvalues and eigenvectors from the eigenproblem
Anasazi::Eigensolution<double,MV> sol = MyProblem->getSolution();
std::vector<Anasazi::Value<double> > evals = sol.Evals;
int numev = sol.numVecs;
if (numev > 0) {
// Compute singular values/vectors and direct residuals.
//
// Compute singular values which are the square root of the eigenvalues
if (MyPID==0) {
cout<<"------------------------------------------------------"<<endl;
cout<<"Computed Singular Values: "<<endl;
cout<<"------------------------------------------------------"<<endl;
}
for (i=0; i<numev; i++) { evals[i].realpart = Teuchos::ScalarTraits<double>::squareroot( evals[i].realpart ); }
//
// Compute left singular vectors : u = Av/sigma
//
std::vector<double> tempnrm(numev), directnrm(numev);
std::vector<int> index(numev);
for (i=0; i<numev; i++) { index[i] = i; }
Anasazi::EpetraMultiVec Av(RowMap,numev), u(RowMap,numev);
Anasazi::EpetraMultiVec* evecs = dynamic_cast<Anasazi::EpetraMultiVec* >(sol.Evecs->CloneViewNonConst( index ));
Teuchos::SerialDenseMatrix<int,double> S(numev,numev);
A->Apply( *evecs, Av );
Av.MvNorm( tempnrm );
for (i=0; i<numev; i++) { S(i,i) = one/tempnrm[i]; };
u.MvTimesMatAddMv( one, Av, S, zero );
//
// Compute direct residuals : || Av - sigma*u ||
//
for (i=0; i<numev; i++) { S(i,i) = evals[i].realpart; }
Av.MvTimesMatAddMv( -one, u, S, one );
Av.MvNorm( directnrm );
if (MyPID==0) {
cout.setf(std::ios_base::right, std::ios_base::adjustfield);
cout<<std::setw(16)<<"Singular Value"
<<std::setw(20)<<"Direct Residual"
<<endl;
cout<<"------------------------------------------------------"<<endl;
for (i=0; i<numev; i++) {
cout<<std::setw(16)<<evals[i].realpart
<<std::setw(20)<<directnrm[i]
<<endl;
}
cout<<"------------------------------------------------------"<<endl;
}
}
#ifdef EPETRA_MPI
MPI_Finalize() ;
#endif
return 0;
}