TraceMinDavidsonUserOpEx.cpp

This is an example of how to use the TraceMinDavidsonSolMgr solver manager to solve a standard eigenvalue problem where the matrix is not explicitly available, using Tpetra data stuctures.

// @HEADER
// *****************************************************************************
//                 Anasazi: Block Eigensolvers Package
//
// Copyright 2004 NTESS and the Anasazi contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
//  This example demonstrates TraceMin-Davidson's ability to converge
//  without the matrix being explicitly stored

// Include autoconfigured header
#include "AnasaziConfigDefs.hpp"

// Include header for TraceMin-Davidson solver
#include "AnasaziTraceMinDavidsonSolMgr.hpp"

// Include header to define basic eigenproblem Ax = \lambda*Bx
#include "AnasaziBasicEigenproblem.hpp"

// Include header to provide Anasazi with Tpetra adapters
#include "AnasaziTpetraAdapter.hpp"
#include "AnasaziOperator.hpp"

// Include headers for Tpetra
#include "Tpetra_Core.hpp"
#include "Tpetra_Version.hpp"
#include "Tpetra_Map.hpp"
#include "Tpetra_MultiVector.hpp"
#include "Tpetra_Operator.hpp"

// Include header for Teuchos serial dense matrix
#include "Teuchos_SerialDenseMatrix.hpp"

// Include header for Teuchos array views
#include "Teuchos_ArrayViewDecl.hpp"

// Include header for Teuchos parameter lists
#include "Teuchos_ParameterList.hpp"



//
// These statements tell the compiler where to look for cout, etc.
//
using Teuchos::rcp;
using Teuchos::RCP;
using std::cout;
using std::endl;

//
// Specify types used in this example.
// Instead of constantly typing Tpetra::MultiVector<Scalar,LocalOrdinal,GlobalOrdinal,Node>,
// we can type TMV.
//
typedef double                                       Scalar;
typedef Tpetra::MultiVector<Scalar>                  TMV;
typedef Tpetra::Operator<Scalar>                     TOP;
typedef Tpetra::Map<>                                Map;
typedef Tpetra::Import<>                             Import;

typedef Teuchos::ScalarTraits<Scalar>::magnitudeType Magnitude;
typedef TMV::local_ordinal_type                       LocalOrdinal;
typedef TMV::global_ordinal_type                      GlobalOrdinal;
typedef TMV::node_type                                Node;

//
// Define a class for our user-defined operator.
// In this case, it is the tridiagonal matrix [-1,2,-1].
// You may define it to be whatever you like.
//
// In general, Trilinos does NOT require the user to deal with MPI
// communication explicitly.  If you want to define your own operator
// though, there's no getting around it.  Fortunately, Trilinos makes
// this relatively straightforward with the use of Map and Import
// objects.  All you have to do is define your initial data
// distribution (which is a block row distribution here), and the data
// distribution you need to perform the operations of your
// matrix-vector multiply.  For instance, when performing a
// matrix-vector multiply with a tridiagonal matrix (with a block row
// distribution), each process needs to know the last element owned by
// the previous process and the first element owned by the next
// process.
//
// If you are only interested in running the code sequentially, you
// may safely ignore everything here regarding Map and Import objects.
//
class MyOp : public virtual TOP {
public:
  //
  // Constructor
  //
  MyOp (const GlobalOrdinal n,
        const Teuchos::RCP<const Teuchos::Comm<int> > comm)
  {
    TEUCHOS_TEST_FOR_EXCEPTION(
      comm.is_null (), std::invalid_argument,
      "MyOp constructor: The input Comm object must be nonnull.");

    //
    // Construct a map for our block row distribution
    //
    const GlobalOrdinal indexBase = 0;
    opMap_ = rcp (new Map (n, indexBase, comm));

    //
    // Get the rank of this process and the number of processes
    // We're going to have to do something special with the first and last processes
    //
    myRank_ = comm->getRank ();
    numProcs_ = comm->getSize ();

    //
    // Get the local number of rows
    //
    LocalOrdinal nlocal = opMap_->getLocalNumElements ();

    //
    // Define the distribution that you need for the matvec.
    // When you define this for your own operator, it is helpful to draw pictures
    // on a sheet of paper to keep track of who needs to receive which elements.
    // Here, each process needs to receive one element from each of its neighbors.
    //

    // All processes but the first will receive one element from the previous process
    if (myRank_ > 0) {
      ++nlocal;
    }
    // All processes but the last will receive one element from the next process
    if (myRank_ < numProcs_ - 1) {
      ++nlocal;
    }
    // Construct a list of columns where this process has nonzero elements
    // For our tridiagonal matrix, this is firstRowItOwns-1:lastRowItOwns+1
    std::vector<GlobalOrdinal> indices;
    indices.reserve (nlocal);
    // The first process is a special case...
    if (myRank_ > 0) {
      indices.push_back (opMap_->getMinGlobalIndex () - 1);
    }
    for (GlobalOrdinal i = opMap_->getMinGlobalIndex (); i <= opMap_->getMaxGlobalIndex (); ++i) {
      indices.push_back (i);
    }
    // So is the last process...
    if (myRank_ < numProcs_ - 1) {
      indices.push_back (opMap_->getMaxGlobalIndex () + 1);
    }

    //
    // Wrap our vector in an array view, which is like a pointer
    //
    Teuchos::ArrayView<const GlobalOrdinal> elementList (indices);

    //
    // Make a map for handling the redistribution.
    //
    // There will be some redundancies (i.e. some of the elements will
    // be owned by multiple processes).  Those redundancies will help
    // express the communication pattern for the sparse mat-vec.
    //
    const GlobalOrdinal numGlobalElements = n + 2*(numProcs_ - 1);
    redistMap_ = rcp (new Map (numGlobalElements, elementList, indexBase, comm));

    //
    // Make an Import object that describes how data will be
    // redistributed.  It takes a Map describing who owns what
    // originally, and a Map that describes who you WANT to own what.
    //
    importer_= rcp (new Import (opMap_, redistMap_));
  };

  //
  // These functions are required since we inherit from Tpetra::Operator
  //

  // Destructor
  virtual ~MyOp() {};

  // Returns the maps
  RCP<const Map> getDomainMap() const { return opMap_; };
  RCP<const Map> getRangeMap() const { return opMap_; };

  //
  // Computes Y = alpha Op X + beta Y
  // TraceMin will never use alpha ~= 1 or beta ~= 0,
  // so we have ignored those options for simplicity.
  //
  void
  apply (const TMV& X,
         TMV& Y,
         Teuchos::ETransp mode = Teuchos::NO_TRANS,
         Scalar alpha = Teuchos::ScalarTraits<Scalar>::one (),
         Scalar beta = Teuchos::ScalarTraits<Scalar>::zero ()) const
  {
    typedef Teuchos::ScalarTraits<Scalar> STS;
    //
    // Let's make sure alpha is 1 and beta is 0...
    // This will throw an exception if that is not the case.
    //
    TEUCHOS_TEST_FOR_EXCEPTION(
      alpha != STS::one() || beta != STS::zero(), std::logic_error,
      "MyOp::apply was given alpha != 1 or beta != 0. "
      "These cases are not implemented.");

    // Get the number of local rows
    const LocalOrdinal nlocRows = X.getLocalLength ();

    // Get the number of vectors
    int numVecs = static_cast<int> (X.getNumVectors ());

    // Make a temporary multivector for holding the redistributed
    // data.  You could also create this in the constructor and reuse
    // it across different apply() calls, but you would need to be
    // careful to reallocate if it has a different number of vectors
    // than X.  The number of vectors in X can vary across different
    // apply() calls.
    RCP<TMV> redistData = rcp (new TMV (redistMap_, numVecs));

    // Redistribute the data.
    // This will do all the necessary communication for you.
    // All processes now own enough data to do the matvec.
    redistData->doImport (X, *importer_, Tpetra::INSERT);

    //
    // Perform the matvec with the data we now locally own
    //
    // For each column...
    for (int c = 0; c < numVecs; ++c) {
      // Get a view of the desired column
      Teuchos::ArrayRCP<Scalar> colView = redistData->getDataNonConst (c);

      LocalOrdinal offset;
      // Y[0,c] = -colView[0] + 2*colView[1] - colView[2] (using local indices)
      if (myRank_ > 0) {
        Y.replaceLocalValue (0, c, -colView[0] + 2*colView[1] - colView[2]);
        offset = 0;
      }
      // Y[0,c] = 2*colView[1] - colView[2] (using local indices)
      else {
        Y.replaceLocalValue (0, c, 2*colView[0] - colView[1]);
        offset = 1;
      }

      // Y[r,c] = -colView[r-offset] + 2*colView[r+1-offset] - colView[r+2-offset]
      for (LocalOrdinal r = 1; r < nlocRows - 1; ++r) {
        const Scalar newVal =
          -colView[r-offset] + 2*colView[r+1-offset] - colView[r+2-offset];
        Y.replaceLocalValue (r, c, newVal);
      }
      // Y[nlocRows-1,c] = -colView[nlocRows-1-offset] + 2*colView[nlocRows-offset]
      //                   - colView[nlocRows+1-offset]
      if (myRank_ < numProcs_ - 1) {
        const Scalar newVal =
          -colView[nlocRows-1-offset] + 2*colView[nlocRows-offset]
          - colView[nlocRows+1-offset];
        Y.replaceLocalValue (nlocRows-1, c, newVal);
      }
      // Y[nlocRows-1,c] = -colView[nlocRows-1-offset] + 2*colView[nlocRows-offset]
      else {
        const Scalar newVal =
          -colView[nlocRows-1-offset] + 2*colView[nlocRows-offset];
        Y.replaceLocalValue (nlocRows-1, c, newVal);
      }
    }
  }

private:
  RCP<const Map> opMap_, redistMap_;
  RCP<const Import> importer_;
  int myRank_, numProcs_;
};



int
main (int argc, char *argv[])
{
  typedef Anasazi::MultiVecTraits<Scalar, TMV> TMVT;
  typedef Anasazi::OperatorTraits<Scalar, TMV, TOP> TOPT;

  //
  // Initialize MPI.
  //
  Tpetra::ScopeGuard tpetraScope (&argc, &argv);

  //
  // Get the default communicator
  //
  RCP<const Teuchos::Comm<int> > comm = Tpetra::getDefaultComm ();
  const int myRank = comm->getRank ();

  //
  // Get parameters from command-line processor
  //
  Scalar tol = 1e-5;
  GlobalOrdinal n = 100;
  int nev = 1;
  int blockSize = 1;
  bool verbose = true;
  std::string whenToShift = "Always";
  Teuchos::CommandLineProcessor cmdp (false, true);
  cmdp.setOption ("n", &n, "Number of rows of our operator.");
  cmdp.setOption ("tolerance", &tol, "Relative residual used for solver.");
  cmdp.setOption ("nev", &nev, "Number of desired eigenpairs.");
  cmdp.setOption ("blocksize", &blockSize, "Number of vectors to add to the "
                  "subspace at each iteration.");
  cmdp.setOption ("verbose","quiet", &verbose,
                  "Whether to print a lot of info or a little bit.");
  cmdp.setOption ("whenToShift", &whenToShift, "When to perform Ritz shifts. "
                  "Options: Never, After Trace Levels, Always.");
  if (cmdp.parse(argc,argv) != Teuchos::CommandLineProcessor::PARSE_SUCCESSFUL) {
    return -1;
  }

  //
  // Construct the operator.
  // Note that the operator does not have to be an explicitly stored matrix.
  // Here, we are using our user-defined operator.
  //
  RCP<MyOp> K = rcp (new MyOp (n, comm));

  //
  // ************************************
  // Start the TraceMin-Davidson iteration
  // ************************************
  //
  // Variables used for the TraceMin-Davidson Method
  //
  int verbosity;
  int numRestartBlocks = 2*nev/blockSize;
  int numBlocks = 10*nev/blockSize;
  if (verbose) {
    verbosity = Anasazi::TimingDetails + Anasazi::IterationDetails +
      Anasazi::Debug + Anasazi::FinalSummary;
  } else {
    verbosity = Anasazi::TimingDetails;
  }
  //
  // Create parameter list to pass into solver
  //
  Teuchos::ParameterList MyPL;
  // How much information should the solver print?
  MyPL.set ("Verbosity", verbosity);
  // Use projected MINRES to solve the saddle point problem
  MyPL.set ("Saddle Solver Type", "Projected Krylov");
  // Add blockSize vectors to the basis per iteration
  MyPL.set ("Block Size", blockSize);
  // How small do the residuals have to be for convergence?
  MyPL.set ("Convergence Tolerance", tol);
  // When we restart, this is how many blocks we keep
  MyPL.set ("Num Restart Blocks", numRestartBlocks);
  // Maximum number of blocks in the subspace
  MyPL.set ("Num Blocks", numBlocks);
  // What triggers a Ritz shift?
  MyPL.set ("When To Shift", whenToShift);

  //
  // Create an initial vector to start the solver.  Note: This needs
  // to have the same number of columns as the block size.  We
  // initialize it with random entries.
  //
  RCP<TMV> ivec = rcp (new TMV (K->getDomainMap (), numRestartBlocks*blockSize));
  TMVT::MvRandom (*ivec);

  // Create the eigenproblem
  RCP<Anasazi::BasicEigenproblem<Scalar,TMV,TOP> > MyProblem =
    rcp (new Anasazi::BasicEigenproblem<Scalar,TMV,TOP> (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
  const bool setProblemCorrectly = MyProblem->setProblem ();
  if (! setProblemCorrectly) {
    if (myRank == 0) {
      cout << "Anasazi::BasicEigenproblem::setProblem() failed to set the "
        "problem correctly." << endl;
    }
    return -1;
  }

  // Initialize the TraceMin-Davidson solver
  typedef Anasazi::Experimental::TraceMinDavidsonSolMgr<Scalar, TMV, TOP> solver_type;
  solver_type MySolverMgr (MyProblem, MyPL);

  //
  // Solve the problem to the specified tolerances
  //
  Anasazi::ReturnType returnCode = MySolverMgr.solve();
  if (myRank == 0) {
    cout << "Anasazi::EigensolverMgr::solve() returned ";
    if (returnCode != Anasazi::Converged) {
      cout << "unconverged.";
    } else {
      cout << "converged.";
    }
    cout << endl;
  }

  //
  // Get the eigenvalues and eigenvectors from the eigenproblem
  //
  Anasazi::Eigensolution<Scalar,TMV> sol = MyProblem->getSolution ();
  std::vector<Anasazi::Value<Scalar> > evals = sol.Evals;
  RCP<TMV> evecs = sol.Evecs;
  int numev = sol.numVecs;

  //
  // Compute the residual, just as a precaution
  //
  if (numev > 0) {
    Teuchos::SerialDenseMatrix<int,Scalar> T (numev, numev);
    TMV tempvec (K->getDomainMap (), TMVT::GetNumberVecs (*evecs));
    std::vector<Scalar> normR (sol.numVecs);
    TMV Kvec (K->getRangeMap (), TMVT::GetNumberVecs (*evecs));

    TOPT::Apply (*K, *evecs, Kvec );
    TMVT::MvTransMv (1.0, Kvec, *evecs, T);
    TMVT::MvTimesMatAddMv (-1.0, *evecs, T, 1.0, Kvec);
    TMVT::MvNorm (Kvec, normR);

    if (myRank == 0) {
      cout.setf (std::ios_base::right, std::ios_base::adjustfield);
      cout << "Actual Eigenvalues (obtained by Rayleigh quotient) : " << endl;
      cout << "------------------------------------------------------" << endl;
      cout << std::setw(16) << "Real Part" << std::setw(16) << "Error" << endl;
      cout<<"------------------------------------------------------" << endl;
      for (int i = 0; i < numev; ++i) {
        cout << std::setw(16) << T(i,i) << std::setw(16) << normR[i]/T(i,i)
             << endl;
      }
      cout << "------------------------------------------------------" << endl;
    }
  }

  return 0;
}