BelosProjectedLeastSquaresSolver.hpp

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#ifndef __Belos_ProjectedLeastSquaresSolver_hpp
#define __Belos_ProjectedLeastSquaresSolver_hpp
 
#include "BelosConfigDefs.hpp"
#include "BelosTypes.hpp"
#include "Teuchos_Array.hpp"
#include "Teuchos_BLAS.hpp"
#include "Teuchos_LAPACK.hpp"
#include "Teuchos_oblackholestream.hpp"
#include "Teuchos_ScalarTraits.hpp"
#include "Teuchos_SerialDenseMatrix.hpp"
#include "Teuchos_StandardParameterEntryValidators.hpp"
 
 
namespace Belos {
 
  namespace details {
 
    // Anonymous namespace restricts contents to file scope.
    namespace {
      template<class Scalar>
      void
      printMatrix (std::ostream& out,
                   const std::string& name,
                   const Teuchos::SerialDenseMatrix<int, Scalar>& A)
      {
        using std::endl;
 
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        out << name << " = " << endl << '[';
        if (numCols == 1) {
          // Compact form for column vectors; valid Matlab.
          for (int i = 0; i < numRows; ++i) {
            out << A(i,0);
            if (i < numRows-1) {
              out << "; ";
            }
          }
        } else {
          for (int i = 0; i < numRows; ++i) {
            for (int j = 0; j < numCols; ++j) {
              out << A(i,j);
              if (j < numCols-1) {
                out << ", ";
              } else if (i < numRows-1) {
                out << ';' << endl;
              }
            }
          }
        }
        out << ']' << endl;
      }
 
      template<class Scalar>
      void
      print (std::ostream& out,
             const Teuchos::SerialDenseMatrix<int, Scalar>& A,
             const std::string& linePrefix)
      {
        using std::endl;
 
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        out << linePrefix << '[';
        if (numCols == 1) {
          // Compact form for column vectors; valid Matlab.
          for (int i = 0; i < numRows; ++i) {
            out << A(i,0);
            if (i < numRows-1) {
              out << "; ";
            }
          }
        } else {
          for (int i = 0; i < numRows; ++i) {
            for (int j = 0; j < numCols; ++j) {
              if (numRows > 1) {
                out << linePrefix << "  ";
              }
              out << A(i,j);
              if (j < numCols-1) {
                out << ", ";
              } else if (i < numRows-1) {
                out << ';' << endl;
              }
            }
          }
        }
        out << linePrefix << ']' << endl;
      }
    } // namespace (anonymous)
 
    template<class Scalar>
    class ProjectedLeastSquaresProblem {
    public:
      typedef Scalar scalar_type;
      typedef typename Teuchos::ScalarTraits<Scalar>::magnitudeType magnitude_type;
 
      Teuchos::SerialDenseMatrix<int,Scalar> H;
 
      Teuchos::SerialDenseMatrix<int,Scalar> R;
 
      Teuchos::SerialDenseMatrix<int,Scalar> y;
 
      Teuchos::SerialDenseMatrix<int,Scalar> z;
 
      Teuchos::Array<Scalar> theCosines;
 
      Teuchos::Array<Scalar> theSines;
 
      ProjectedLeastSquaresProblem (const int maxNumIterations) :
        H (maxNumIterations+1, maxNumIterations),
        R (maxNumIterations+1, maxNumIterations),
        y (maxNumIterations+1, 1),
        z (maxNumIterations+1, 1),
        theCosines (maxNumIterations+1),
        theSines (maxNumIterations+1)
      {}
 
      void
      reset (const typename Teuchos::ScalarTraits<Scalar>::magnitudeType beta)
      {
        typedef Teuchos::ScalarTraits<Scalar> STS;
 
        // Zero out the right-hand side of the least-squares problem.
        z.putScalar (STS::zero());
 
        // Promote the initial residual norm from a magnitude type to
        // a scalar type, so we can assign it to the first entry of z.
        const Scalar initialResidualNorm (beta);
        z(0,0) = initialResidualNorm;
      }
 
      void
      reallocateAndReset (const typename Teuchos::ScalarTraits<Scalar>::magnitudeType beta,
                          const int maxNumIterations)
      {
        typedef Teuchos::ScalarTraits<Scalar> STS;
        typedef Teuchos::ScalarTraits<magnitude_type> STM;
 
        TEUCHOS_TEST_FOR_EXCEPTION(beta < STM::zero(), std::invalid_argument,
                           "ProjectedLeastSquaresProblem::reset: initial "
                           "residual beta = " << beta << " < 0.");
        TEUCHOS_TEST_FOR_EXCEPTION(maxNumIterations <= 0, std::invalid_argument,
                           "ProjectedLeastSquaresProblem::reset: maximum number "
                           "of iterations " << maxNumIterations << " <= 0.");
 
        if (H.numRows() < maxNumIterations+1 || H.numCols() < maxNumIterations) {
          const int errcode = H.reshape (maxNumIterations+1, maxNumIterations);
          TEUCHOS_TEST_FOR_EXCEPTION(errcode != 0, std::runtime_error,
                             "Failed to reshape H into a " << (maxNumIterations+1)
                             << " x " << maxNumIterations << " matrix.");
        }
        (void) H.putScalar (STS::zero());
 
        if (R.numRows() < maxNumIterations+1 || R.numCols() < maxNumIterations) {
          const int errcode = R.reshape (maxNumIterations+1, maxNumIterations);
          TEUCHOS_TEST_FOR_EXCEPTION(errcode != 0, std::runtime_error,
                             "Failed to reshape R into a " << (maxNumIterations+1)
                             << " x " << maxNumIterations << " matrix.");
        }
        (void) R.putScalar (STS::zero());
 
        if (y.numRows() < maxNumIterations+1 || y.numCols() < 1) {
          const int errcode = y.reshape (maxNumIterations+1, 1);
          TEUCHOS_TEST_FOR_EXCEPTION(errcode != 0, std::runtime_error,
                             "Failed to reshape y into a " << (maxNumIterations+1)
                             << " x " << 1 << " matrix.");
        }
        (void) y.putScalar (STS::zero());
 
        if (z.numRows() < maxNumIterations+1 || z.numCols() < 1) {
          const int errcode = z.reshape (maxNumIterations+1, 1);
          TEUCHOS_TEST_FOR_EXCEPTION(errcode != 0, std::runtime_error,
                             "Failed to reshape z into a " << (maxNumIterations+1)
                             << " x " << 1 << " matrix.");
        }
        reset (beta);
      }
 
    };
 
 
    template<class Scalar>
    class LocalDenseMatrixOps {
    public:
      typedef Scalar scalar_type;
      typedef typename Teuchos::ScalarTraits<Scalar>::magnitudeType magnitude_type;
      typedef Teuchos::SerialDenseMatrix<int,Scalar> mat_type;
 
    private:
      typedef Teuchos::ScalarTraits<scalar_type> STS;
      typedef Teuchos::ScalarTraits<magnitude_type> STM;
      typedef Teuchos::BLAS<int, scalar_type> blas_type;
      typedef Teuchos::LAPACK<int, scalar_type> lapack_type;
 
    public:
      void
      conjugateTranspose (mat_type& A_star, const mat_type& A) const
      {
        for (int i = 0; i < A.numRows(); ++i) {
          for (int j = 0; j < A.numCols(); ++j) {
            A_star(j,i) = STS::conjugate (A(i,j));
          }
        }
      }
 
      void
      conjugateTransposeOfUpperTriangular (mat_type& L, const mat_type& R) const
      {
        const int N = R.numCols();
 
        for (int j = 0; j < N; ++j) {
          for (int i = 0; i <= j; ++i) {
            L(j,i) = STS::conjugate (R(i,j));
          }
        }
      }
 
      void
      zeroOutStrictLowerTriangle (mat_type& A) const
      {
        const int N = std::min (A.numRows(), A.numCols());
 
        for (int j = 0; j < N; ++j) {
          for (int i = j+1; i < A.numRows(); ++i) {
            A(i,j) = STS::zero();
          }
        }
      }
 
      void
      partition (Teuchos::RCP<mat_type>& A_11,
                 Teuchos::RCP<mat_type>& A_21,
                 Teuchos::RCP<mat_type>& A_12,
                 Teuchos::RCP<mat_type>& A_22,
                 mat_type& A,
                 const int numRows1,
                 const int numRows2,
                 const int numCols1,
                 const int numCols2)
      {
        using Teuchos::rcp;
        using Teuchos::View;
 
        A_11 = rcp (new mat_type (View, A, numRows1, numCols1, 0, 0));
        A_21 = rcp (new mat_type (View, A, numRows2, numCols1, numRows1, 0));
        A_12 = rcp (new mat_type (View, A, numRows1, numCols2, 0, numCols1));
        A_22 = rcp (new mat_type (View, A, numRows2, numCols2, numRows1, numCols1));
      }
 
      void
      matScale (mat_type& A, const scalar_type& alpha) const
      {
        // const int LDA = A.stride(); // unused
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        if (numRows == 0 || numCols == 0) {
          return;
        } else {
          for (int j = 0; j < numCols; ++j) {
            scalar_type* const A_j = &A(0,j);
 
            for (int i = 0; i < numRows; ++i) {
              A_j[i] *= alpha;
            }
          }
        }
      }
 
      void
      axpy (mat_type& Y,
            const scalar_type& alpha,
            const mat_type& X) const
      {
        const int numRows = Y.numRows();
        const int numCols = Y.numCols();
 
        TEUCHOS_TEST_FOR_EXCEPTION(numRows != X.numRows() || numCols != X.numCols(),
                           std::invalid_argument, "Dimensions of X and Y don't "
                           "match.  X is " << X.numRows() << " x " << X.numCols()
                           << ", and Y is " << numRows << " x " << numCols << ".");
        for (int j = 0; j < numCols; ++j) {
          for (int i = 0; i < numRows; ++i) {
            Y(i,j) += alpha * X(i,j);
          }
        }
      }
 
      void
      matAdd (mat_type& A, const mat_type& B) const
      {
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        TEUCHOS_TEST_FOR_EXCEPTION(
          B.numRows() != numRows || B.numCols() != numCols,
          std::invalid_argument,
          "matAdd: The input matrices A and B have incompatible dimensions.  "
          "A is " << numRows << " x " << numCols << ", but B is " <<
          B.numRows () << " x " << B.numCols () << ".");
        if (numRows == 0 || numCols == 0) {
          return;
        } else {
          for (int j = 0; j < numCols; ++j) {
            scalar_type* const A_j = &A(0,j);
            const scalar_type* const B_j = &B(0,j);
 
            for (int i = 0; i < numRows; ++i) {
              A_j[i] += B_j[i];
            }
          }
        }
      }
 
      void
      matSub (mat_type& A, const mat_type& B) const
      {
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        TEUCHOS_TEST_FOR_EXCEPTION(
          B.numRows() != numRows || B.numCols() != numCols,
          std::invalid_argument,
          "matSub: The input matrices A and B have incompatible dimensions.  "
          "A is " << numRows << " x " << numCols << ", but B is " <<
          B.numRows () << " x " << B.numCols () << ".");
        if (numRows == 0 || numCols == 0) {
          return;
        } else {
          for (int j = 0; j < numCols; ++j) {
            scalar_type* const A_j = &A(0,j);
            const scalar_type* const B_j = &B(0,j);
 
            for (int i = 0; i < numRows; ++i) {
              A_j[i] -= B_j[i];
            }
          }
        }
      }
 
      void
      rightUpperTriSolve (mat_type& B,
                          const mat_type& R) const
      {
        TEUCHOS_TEST_FOR_EXCEPTION(B.numCols() != R.numRows(),
                           std::invalid_argument,
                           "rightUpperTriSolve: R and B have incompatible "
                           "dimensions.  B has " << B.numCols() << " columns, "
                           "but R has " << R.numRows() << " rows.");
        blas_type blas;
        blas.TRSM (Teuchos::RIGHT_SIDE, Teuchos::UPPER_TRI,
                   Teuchos::NO_TRANS, Teuchos::NON_UNIT_DIAG,
                   R.numCols(), B.numCols(),
                   STS::one(), R.values(), R.stride(),
                   B.values(), B.stride());
      }
 
      void
      matMatMult (const scalar_type& beta,
                  mat_type& C,
                  const scalar_type& alpha,
                  const mat_type& A,
                  const mat_type& B) const
      {
        using Teuchos::NO_TRANS;
 
        TEUCHOS_TEST_FOR_EXCEPTION(A.numCols() != B.numRows(),
                           std::invalid_argument,
                           "matMatMult: The input matrices A and B have "
                           "incompatible dimensions.  A is " << A.numRows()
                           << " x " << A.numCols() << ", but B is "
                           << B.numRows() << " x " << B.numCols() << ".");
        TEUCHOS_TEST_FOR_EXCEPTION(A.numRows() != C.numRows(),
                           std::invalid_argument,
                           "matMatMult: The input matrix A and the output "
                           "matrix C have incompatible dimensions.  A has "
                           << A.numRows() << " rows, but C has " << C.numRows()
                           << " rows.");
        TEUCHOS_TEST_FOR_EXCEPTION(B.numCols() != C.numCols(),
                           std::invalid_argument,
                           "matMatMult: The input matrix B and the output "
                           "matrix C have incompatible dimensions.  B has "
                           << B.numCols() << " columns, but C has "
                           << C.numCols() << " columns.");
        blas_type blas;
        blas.GEMM (NO_TRANS, NO_TRANS, C.numRows(), C.numCols(), A.numCols(),
                   alpha, A.values(), A.stride(), B.values(), B.stride(),
                   beta, C.values(), C.stride());
      }
 
      int
      infNaNCount (const mat_type& A, const bool upperTriangular=false) const
      {
        int count = 0;
        for (int j = 0; j < A.numCols(); ++j) {
          if (upperTriangular) {
            for (int i = 0; i <= j && i < A.numRows(); ++i) {
              if (STS::isnaninf (A(i,j))) {
                ++count;
              }
            }
          } else {
            for (int i = 0; i < A.numRows(); ++i) {
              if (STS::isnaninf (A(i,j))) {
                ++count;
              }
            }
          }
        }
        return count;
      }
 
      std::pair<bool, std::pair<magnitude_type, magnitude_type> >
      isUpperTriangular (const mat_type& A) const
      {
        magnitude_type lowerTri = STM::zero();
        magnitude_type upperTri = STM::zero();
        int count = 0;
 
        for (int j = 0; j < A.numCols(); ++j) {
          // Compute the Frobenius norm of the upper triangle /
          // trapezoid of A.  The second clause of the loop upper
          // bound is for matrices with fewer rows than columns.
          for (int i = 0; i <= j && i < A.numRows(); ++i) {
            const magnitude_type A_ij_mag = STS::magnitude (A(i,j));
            upperTri += A_ij_mag * A_ij_mag;
          }
          // Scan the strict lower triangle / trapezoid of A.
          for (int i = j+1; i < A.numRows(); ++i) {
            const magnitude_type A_ij_mag = STS::magnitude (A(i,j));
            lowerTri += A_ij_mag * A_ij_mag;
            if (A_ij_mag != STM::zero()) {
              ++count;
            }
          }
        }
        return std::make_pair (count == 0, std::make_pair (lowerTri, upperTri));
      }
 
 
      std::pair<bool, std::pair<magnitude_type, magnitude_type> >
      isUpperHessenberg (const mat_type& A) const
      {
        magnitude_type lower = STM::zero();
        magnitude_type upper = STM::zero();
        int count = 0;
 
        for (int j = 0; j < A.numCols(); ++j) {
          // Compute the Frobenius norm of the upper Hessenberg part
          // of A.  The second clause of the loop upper bound is for
          // matrices with fewer rows than columns.
          for (int i = 0; i <= j+1 && i < A.numRows(); ++i) {
            const magnitude_type A_ij_mag = STS::magnitude (A(i,j));
            upper += A_ij_mag * A_ij_mag;
          }
          // Scan the strict lower part of A.
          for (int i = j+2; i < A.numRows(); ++i) {
            const magnitude_type A_ij_mag = STS::magnitude (A(i,j));
            lower += A_ij_mag * A_ij_mag;
            if (A_ij_mag != STM::zero()) {
              ++count;
            }
          }
        }
        return std::make_pair (count == 0, std::make_pair (lower, upper));
      }
 
      void
      ensureUpperTriangular (const mat_type& A,
                             const char* const matrixName) const
      {
        std::pair<bool, std::pair<magnitude_type, magnitude_type> > result =
          isUpperTriangular (A);
 
        TEUCHOS_TEST_FOR_EXCEPTION(! result.first, std::invalid_argument,
                           "The " << A.numRows() << " x " << A.numCols()
                           << " matrix " << matrixName << " is not upper "
                           "triangular.  ||tril(A)||_F = "
                           << result.second.first << " and ||A||_F = "
                           << result.second.second << ".");
      }
 
      void
      ensureUpperHessenberg (const mat_type& A,
                             const char* const matrixName) const
      {
        std::pair<bool, std::pair<magnitude_type, magnitude_type> > result =
          isUpperHessenberg (A);
 
        TEUCHOS_TEST_FOR_EXCEPTION(! result.first, std::invalid_argument,
                           "The " << A.numRows() << " x " << A.numCols()
                           << " matrix " << matrixName << " is not upper "
                           "triangular.  ||tril(A(2:end, :))||_F = "
                           << result.second.first << " and ||A||_F = "
                           << result.second.second << ".");
      }
 
      void
      ensureUpperHessenberg (const mat_type& A,
                             const char* const matrixName,
                             const magnitude_type relativeTolerance) const
      {
        std::pair<bool, std::pair<magnitude_type, magnitude_type> > result =
          isUpperHessenberg (A);
 
        if (result.first) {
          // Mollified relative departure from upper Hessenberg.
          const magnitude_type err = (result.second.second == STM::zero() ?
                                      result.second.first :
                                      result.second.first / result.second.second);
          TEUCHOS_TEST_FOR_EXCEPTION(err > relativeTolerance, std::invalid_argument,
                             "The " << A.numRows() << " x " << A.numCols()
                             << " matrix " << matrixName << " is not upper "
                             "triangular.  ||tril(A(2:end, :))||_F "
                             << (result.second.second == STM::zero() ? "" : " / ||A||_F")
                             << " = " << err << " > " << relativeTolerance << ".");
        }
      }
 
      void
      ensureMinimumDimensions (const mat_type& A,
                               const char* const matrixName,
                               const int minNumRows,
                               const int minNumCols) const
      {
        TEUCHOS_TEST_FOR_EXCEPTION(A.numRows() < minNumRows || A.numCols() < minNumCols,
                           std::invalid_argument,
                           "The matrix " << matrixName << " is " << A.numRows()
                           << " x " << A.numCols() << ", and therefore does not "
                           "satisfy the minimum dimensions " << minNumRows
                           << " x " << minNumCols << ".");
      }
 
      void
      ensureEqualDimensions (const mat_type& A,
                             const char* const matrixName,
                             const int numRows,
                             const int numCols) const
      {
        TEUCHOS_TEST_FOR_EXCEPTION(A.numRows() != numRows || A.numCols() != numCols,
                           std::invalid_argument,
                           "The matrix " << matrixName << " is supposed to be "
                           << numRows << " x " << numCols << ", but is "
                           << A.numRows() << " x " << A.numCols() << " instead.");
      }
 
    };
 
    enum ERobustness {
      ROBUSTNESS_NONE,
      ROBUSTNESS_SOME,
      ROBUSTNESS_LOTS,
      ROBUSTNESS_INVALID
    };
 
    inline std::string
    robustnessEnumToString (const ERobustness x)
    {
      const char* strings[] = {"None", "Some", "Lots"};
      TEUCHOS_TEST_FOR_EXCEPTION(x < ROBUSTNESS_NONE || x >= ROBUSTNESS_INVALID,
                         std::invalid_argument,
                         "Invalid enum value " << x << ".");
      return std::string (strings[x]);
    }
 
    ERobustness
    inline robustnessStringToEnum (const std::string& x)
    {
      const char* strings[] = {"None", "Some", "Lots"};
      for (int r = 0; r < static_cast<int> (ROBUSTNESS_INVALID); ++r) {
        if (x == strings[r]) {
          return static_cast<ERobustness> (r);
        }
      }
      TEUCHOS_TEST_FOR_EXCEPTION(true, std::invalid_argument,
                         "Invalid robustness string " << x << ".");
    }
 
    inline Teuchos::RCP<Teuchos::ParameterEntryValidator>
    robustnessValidator ()
    {
      using Teuchos::stringToIntegralParameterEntryValidator;
 
      Teuchos::Array<std::string> strs (3);
      strs[0] = robustnessEnumToString (ROBUSTNESS_NONE);
      strs[1] = robustnessEnumToString (ROBUSTNESS_SOME);
      strs[2] = robustnessEnumToString (ROBUSTNESS_LOTS);
      Teuchos::Array<std::string> docs (3);
      docs[0] = "Use the BLAS' triangular solve.  This may result in Inf or "
        "NaN output if the triangular matrix is rank deficient.";
      docs[1] = "Robustness somewhere between \"None\" and \"Lots\".";
      docs[2] = "Solve the triangular system in a least-squares sense, using "
        "an SVD-based algorithm.  This will always succeed, though the "
        "solution may not make sense for GMRES.";
      Teuchos::Array<ERobustness> ints (3);
      ints[0] = ROBUSTNESS_NONE;
      ints[1] = ROBUSTNESS_SOME;
      ints[2] = ROBUSTNESS_LOTS;
      const std::string pname ("Robustness of Projected Least-Squares Solve");
 
      return stringToIntegralParameterEntryValidator<ERobustness> (strs, docs,
                                                                   ints, pname);
    }
 
    template<class Scalar>
    class ProjectedLeastSquaresSolver {
    public:
      typedef Scalar scalar_type;
      typedef typename Teuchos::ScalarTraits<Scalar>::magnitudeType magnitude_type;
      typedef Teuchos::SerialDenseMatrix<int,Scalar> mat_type;
 
    private:
      typedef Teuchos::ScalarTraits<scalar_type> STS;
      typedef Teuchos::ScalarTraits<magnitude_type> STM;
      typedef Teuchos::BLAS<int, scalar_type> blas_type;
      typedef Teuchos::LAPACK<int, scalar_type> lapack_type;
 
    public:
      ProjectedLeastSquaresSolver (std::ostream& warnStream,
                                   const ERobustness defaultRobustness=ROBUSTNESS_NONE) :
        warn_ (warnStream),
        defaultRobustness_ (defaultRobustness)
      {}
 
      magnitude_type
      updateColumn (ProjectedLeastSquaresProblem<Scalar>& problem,
                    const int curCol)
      {
        return updateColumnGivens (problem.H, problem.R, problem.y, problem.z,
                                   problem.theCosines, problem.theSines, curCol);
      }
 
      magnitude_type
      updateColumns (ProjectedLeastSquaresProblem<Scalar>& problem,
                     const int startCol,
                     const int endCol)
      {
        return updateColumnsGivens (problem.H, problem.R, problem.y, problem.z,
                                    problem.theCosines, problem.theSines,
                                    startCol, endCol);
      }
 
      void
      solve (ProjectedLeastSquaresProblem<Scalar>& problem,
             const int curCol)
      {
        solveGivens (problem.y, problem.R, problem.z, curCol);
      }
 
      std::pair<int, bool>
      solveUpperTriangularSystem (Teuchos::ESide side,
                                  mat_type& X,
                                  const mat_type& R,
                                  const mat_type& B)
      {
        return solveUpperTriangularSystem (side, X, R, B, defaultRobustness_);
      }
 
      std::pair<int, bool>
      solveUpperTriangularSystem (Teuchos::ESide side,
                                  mat_type& X,
                                  const mat_type& R,
                                  const mat_type& B,
                                  const ERobustness robustness)
      {
        TEUCHOS_TEST_FOR_EXCEPTION(X.numRows() != B.numRows(), std::invalid_argument,
                           "The output X and right-hand side B have different "
                           "numbers of rows.  X has " << X.numRows() << " rows"
                           ", and B has " << B.numRows() << " rows.");
        // If B has more columns than X, we ignore the remaining
        // columns of B when solving the upper triangular system.  If
        // B has _fewer_ columns than X, we can't solve for all the
        // columns of X, so we throw an exception.
        TEUCHOS_TEST_FOR_EXCEPTION(X.numCols() > B.numCols(), std::invalid_argument,
                           "The output X has more columns than the "
                           "right-hand side B.  X has " << X.numCols()
                           << " columns and B has " << B.numCols()
                           << " columns.");
        // See above explaining the number of columns in B_view.
        mat_type B_view (Teuchos::View, B, B.numRows(), X.numCols());
 
        // Both the BLAS' _TRSM and LAPACK's _LATRS overwrite the
        // right-hand side with the solution, so first copy B_view
        // into X.
        X.assign (B_view);
 
        // Solve the upper triangular system.
        return solveUpperTriangularSystemInPlace (side, X, R, robustness);
      }
 
      std::pair<int, bool>
      solveUpperTriangularSystemInPlace (Teuchos::ESide side,
                                         mat_type& X,
                                         const mat_type& R)
      {
        return solveUpperTriangularSystemInPlace (side, X, R, defaultRobustness_);
      }
 
      std::pair<int, bool>
      solveUpperTriangularSystemInPlace (Teuchos::ESide side,
                                         mat_type& X,
                                         const mat_type& R,
                                         const ERobustness robustness)
      {
        using Teuchos::Array;
        using Teuchos::Copy;
        using Teuchos::LEFT_SIDE;
        using Teuchos::RIGHT_SIDE;
        LocalDenseMatrixOps<Scalar> ops;
 
        const int M = R.numRows();
        const int N = R.numCols();
        TEUCHOS_TEST_FOR_EXCEPTION(M < N, std::invalid_argument,
                           "The input matrix R has fewer columns than rows.  "
                           "R is " << M << " x " << N << ".");
        // Ignore any additional rows of R by working with a square view.
        mat_type R_view (Teuchos::View, R, N, N);
 
        if (side == LEFT_SIDE) {
          TEUCHOS_TEST_FOR_EXCEPTION(X.numRows() < N, std::invalid_argument,
                             "The input/output matrix X has only "
                             << X.numRows() << " rows, but needs at least "
                             << N << " rows to match the matrix for a "
                             "left-side solve R \\ X.");
        } else if (side == RIGHT_SIDE) {
          TEUCHOS_TEST_FOR_EXCEPTION(X.numCols() < N, std::invalid_argument,
                             "The input/output matrix X has only "
                             << X.numCols() << " columns, but needs at least "
                             << N << " columns to match the matrix for a "
                             "right-side solve X / R.");
        }
        TEUCHOS_TEST_FOR_EXCEPTION(robustness < ROBUSTNESS_NONE ||
                           robustness >= ROBUSTNESS_INVALID,
                           std::invalid_argument,
                           "Invalid robustness value " << robustness << ".");
 
        // In robust mode, scan the matrix and right-hand side(s) for
        // Infs and NaNs.  Only look at the upper triangle of the
        // matrix.
        if (robustness > ROBUSTNESS_NONE) {
          int count = ops.infNaNCount (R_view, true);
          TEUCHOS_TEST_FOR_EXCEPTION(count > 0, std::runtime_error,
                             "There " << (count != 1 ? "are" : "is")
                             << " " << count << " Inf or NaN entr"
                             << (count != 1 ? "ies" : "y")
                             << " in the upper triangle of R.");
          count = ops.infNaNCount (X, false);
          TEUCHOS_TEST_FOR_EXCEPTION(count > 0, std::runtime_error,
                             "There " << (count != 1 ? "are" : "is")
                             << " " << count << " Inf or NaN entr"
                             << (count != 1 ? "ies" : "y") << " in the "
                             "right-hand side(s) X.");
        }
 
        // Pair of values to return from this method.
        int rank = N;
        bool foundRankDeficiency = false;
 
        // Solve for X.
        blas_type blas;
 
        if (robustness == ROBUSTNESS_NONE) {
          // Fast triangular solve using the BLAS' _TRSM.  This does
          // no checking for rank deficiency.
          blas.TRSM(side, Teuchos::UPPER_TRI, Teuchos::NO_TRANS,
                    Teuchos::NON_UNIT_DIAG, X.numRows(), X.numCols(),
                    STS::one(), R.values(), R.stride(),
                    X.values(), X.stride());
        } else if (robustness < ROBUSTNESS_INVALID) {
          // Save a copy of X, since X contains the right-hand side on
          // input.
          mat_type B (Copy, X, X.numRows(), X.numCols());
 
          // Fast triangular solve using the BLAS' _TRSM.  This does
          // no checking for rank deficiency.
          blas.TRSM(side, Teuchos::UPPER_TRI, Teuchos::NO_TRANS,
                    Teuchos::NON_UNIT_DIAG, X.numRows(), X.numCols(),
                    STS::one(), R.values(), R.stride(),
                    X.values(), X.stride());
 
          // Check for Infs or NaNs in X.  If there are any, then
          // assume that TRSM failed, and use a more robust algorithm.
          if (ops.infNaNCount (X, false) != 0) {
 
            warn_ << "Upper triangular solve: Found Infs and/or NaNs in the "
              "solution after using the fast algorithm.  Retrying using a more "
              "robust algorithm." << std::endl;
 
            // Restore X from the copy.
            X.assign (B);
 
            // Find the minimum-norm solution to the least-squares
            // problem $\min_x \|RX - B\|_2$, using the singular value
            // decomposition (SVD).
            LocalDenseMatrixOps<Scalar> ops;
            if (side == LEFT_SIDE) {
              // _GELSS overwrites its matrix input, so make a copy.
              mat_type R_copy (Teuchos::Copy, R_view, N, N);
 
              // Zero out the lower triangle of R_copy, since the
              // mat_type constructor copies all the entries, not just
              // the upper triangle.  _GELSS will read all the entries
              // of the input matrix.
              ops.zeroOutStrictLowerTriangle (R_copy);
 
              // Solve the least-squares problem.
              rank = solveLeastSquaresUsingSVD (R_copy, X);
            } else {
              // If solving with R on the right-hand side, the interface
              // requires that instead of solving $\min \|XR - B\|_2$,
              // we have to solve $\min \|R^* X^* - B^*\|_2$.  We
              // compute (conjugate) transposes in newly allocated
              // temporary matrices X_star resp. R_star.  (B is already
              // in X and _GELSS overwrites its input vector X with the
              // solution.)
              mat_type X_star (X.numCols(), X.numRows());
              ops.conjugateTranspose (X_star, X);
              mat_type R_star (N, N); // Filled with zeros automatically.
              ops.conjugateTransposeOfUpperTriangular (R_star, R);
 
              // Solve the least-squares problem.
              rank = solveLeastSquaresUsingSVD (R_star, X_star);
 
              // Copy the transpose of X_star back into X.
              ops.conjugateTranspose (X, X_star);
            }
            if (rank < N) {
              foundRankDeficiency = true;
            }
          }
        } else {
          TEUCHOS_TEST_FOR_EXCEPTION(true, std::logic_error,
                             "Should never get here!  Invalid robustness value "
                             << robustness << ".  Please report this bug to the "
                             "Belos developers.");
        }
        return std::make_pair (rank, foundRankDeficiency);
      }
 
 
    public:
      bool
      testGivensRotations (std::ostream& out)
      {
        using std::endl;
 
        out << "Testing Givens rotations:" << endl;
        Scalar x = STS::random();
        Scalar y = STS::random();
        out << "  x = " << x << ", y = " << y << endl;
 
        Scalar theCosine, theSine, result;
        blas_type blas;
        computeGivensRotation (x, y, theCosine, theSine, result);
        out << "-- After computing rotation:" << endl;
        out << "---- cos,sin = " << theCosine << "," << theSine << endl;
        out << "---- x = " << x << ", y = " << y
            << ", result = " << result << endl;
 
        blas.ROT (1, &x, 1, &y, 1, &theCosine, &theSine);
        out << "-- After applying rotation:" << endl;
        out << "---- cos,sin = " << theCosine << "," << theSine << endl;
        out << "---- x = " << x << ", y = " << y << endl;
 
        // Allow only a tiny bit of wiggle room for zeroing-out of y.
        if (STS::magnitude(y) > 2*STS::eps())
          return false;
        else
          return true;
      }
 
      bool
      testUpdateColumn (std::ostream& out,
                        const int numCols,
                        const bool testBlockGivens=false,
                        const bool extraVerbose=false)
      {
        using Teuchos::Array;
        using std::endl;
 
        TEUCHOS_TEST_FOR_EXCEPTION(numCols <= 0, std::invalid_argument,
                           "numCols = " << numCols << " <= 0.");
        const int numRows = numCols + 1;
 
        mat_type H (numRows, numCols);
        mat_type z (numRows, 1);
 
        mat_type R_givens (numRows, numCols);
        mat_type y_givens (numRows, 1);
        mat_type z_givens (numRows, 1);
        Array<Scalar> theCosines (numCols);
        Array<Scalar> theSines (numCols);
 
        mat_type R_blockGivens (numRows, numCols);
        mat_type y_blockGivens (numRows, 1);
        mat_type z_blockGivens (numRows, 1);
        Array<Scalar> blockCosines (numCols);
        Array<Scalar> blockSines (numCols);
        const int panelWidth = std::min (3, numCols);
 
        mat_type R_lapack (numRows, numCols);
        mat_type y_lapack (numRows, 1);
        mat_type z_lapack (numRows, 1);
 
        // Make a random least-squares problem.
        makeRandomProblem (H, z);
        if (extraVerbose) {
          printMatrix<Scalar> (out, "H", H);
          printMatrix<Scalar> (out, "z", z);
        }
 
        // Set up the right-hand side copies for each of the methods.
        // Each method is free to overwrite its given right-hand side.
        z_givens.assign (z);
        if (testBlockGivens) {
          z_blockGivens.assign (z);
        }
        z_lapack.assign (z);
 
        //
        // Imitate how one would update the least-squares problem in a
        // typical GMRES implementation, for each updating method.
        //
        // Update using Givens rotations, one at a time.
        magnitude_type residualNormGivens = STM::zero();
        for (int curCol = 0; curCol < numCols; ++curCol) {
          residualNormGivens = updateColumnGivens (H, R_givens, y_givens, z_givens,
                                                   theCosines, theSines, curCol);
        }
        solveGivens (y_givens, R_givens, z_givens, numCols-1);
 
        // Update using the "panel left-looking" Givens approach, with
        // the given panel width.
        magnitude_type residualNormBlockGivens = STM::zero();
        if (testBlockGivens) {
          const bool testBlocksAtATime = true;
          if (testBlocksAtATime) {
            // Blocks of columns at a time.
            for (int startCol = 0; startCol < numCols; startCol += panelWidth) {
              int endCol = std::min (startCol + panelWidth - 1, numCols - 1);
              residualNormBlockGivens =
                updateColumnsGivens (H, R_blockGivens, y_blockGivens, z_blockGivens,
                                     blockCosines, blockSines, startCol, endCol);
            }
          } else {
            // One column at a time.  This is good as a sanity check
            // to make sure updateColumnsGivens() with a single column
            // does the same thing as updateColumnGivens().
            for (int startCol = 0; startCol < numCols; ++startCol) {
              residualNormBlockGivens =
                updateColumnsGivens (H, R_blockGivens, y_blockGivens, z_blockGivens,
                                     blockCosines, blockSines, startCol, startCol);
            }
          }
          // The panel version of Givens should compute the same
          // cosines and sines as the non-panel version, and should
          // update the right-hand side z in the same way.  Thus, we
          // should be able to use the same triangular solver.
          solveGivens (y_blockGivens, R_blockGivens, z_blockGivens, numCols-1);
        }
 
        // Solve using LAPACK's least-squares solver.
        const magnitude_type residualNormLapack =
          solveLapack (H, R_lapack, y_lapack, z_lapack, numCols-1);
 
        // Compute the condition number of the least-squares problem.
        // This requires a residual, so use the residual from the
        // LAPACK method.  All that the method needs for an accurate
        // residual norm is forward stability.
        const magnitude_type leastSquaresCondNum =
          leastSquaresConditionNumber (H, z, residualNormLapack);
 
        // Compute the relative least-squares solution error for both
        // Givens methods.  We assume that the LAPACK solution is
        // "exact" and compare against the Givens rotations solution.
        // This is taking liberties with the definition of condition
        // number, but it's the best we can do, since we don't know
        // the exact solution and don't have an extended-precision
        // solver.
 
        // The solution lives only in y[0 .. numCols-1].
        mat_type y_givens_view (Teuchos::View, y_givens, numCols, 1);
        mat_type y_blockGivens_view (Teuchos::View, y_blockGivens, numCols, 1);
        mat_type y_lapack_view (Teuchos::View, y_lapack, numCols, 1);
 
        const magnitude_type givensSolutionError =
          solutionError (y_givens_view, y_lapack_view);
        const magnitude_type blockGivensSolutionError = testBlockGivens ?
          solutionError (y_blockGivens_view, y_lapack_view) :
          STM::zero();
 
        // If printing out the matrices, copy out the upper triangular
        // factors for printing.  (Both methods are free to leave data
        // below the lower triangle.)
        if (extraVerbose) {
          mat_type R_factorFromGivens (numCols, numCols);
          mat_type R_factorFromBlockGivens (numCols, numCols);
          mat_type R_factorFromLapack (numCols, numCols);
 
          for (int j = 0; j < numCols; ++j) {
            for (int i = 0; i <= j; ++i) {
              R_factorFromGivens(i,j) = R_givens(i,j);
              if (testBlockGivens) {
                R_factorFromBlockGivens(i,j) = R_blockGivens(i,j);
              }
              R_factorFromLapack(i,j) = R_lapack(i,j);
            }
          }
 
          printMatrix<Scalar> (out, "R_givens", R_factorFromGivens);
          printMatrix<Scalar> (out, "y_givens", y_givens_view);
          printMatrix<Scalar> (out, "z_givens", z_givens);
 
          if (testBlockGivens) {
            printMatrix<Scalar> (out, "R_blockGivens", R_factorFromBlockGivens);
            printMatrix<Scalar> (out, "y_blockGivens", y_blockGivens_view);
            printMatrix<Scalar> (out, "z_blockGivens", z_blockGivens);
          }
 
          printMatrix<Scalar> (out, "R_lapack", R_factorFromLapack);
          printMatrix<Scalar> (out, "y_lapack", y_lapack_view);
          printMatrix<Scalar> (out, "z_lapack", z_lapack);
        }
 
        // Compute the (Frobenius) norm of the original matrix H.
        const magnitude_type H_norm = H.normFrobenius();
 
        out << "||H||_F = " << H_norm << endl;
 
        out << "||H y_givens - z||_2 / ||H||_F = "
            << leastSquaresResidualNorm (H, y_givens_view, z) / H_norm << endl;
        if (testBlockGivens) {
          out << "||H y_blockGivens - z||_2 / ||H||_F = "
              << leastSquaresResidualNorm (H, y_blockGivens_view, z) / H_norm << endl;
        }
        out << "||H y_lapack - z||_2 / ||H||_F = "
            << leastSquaresResidualNorm (H, y_lapack_view, z) / H_norm << endl;
 
        out << "||y_givens - y_lapack||_2 / ||y_lapack||_2 = "
            << givensSolutionError << endl;
        if (testBlockGivens) {
          out << "||y_blockGivens - y_lapack||_2 / ||y_lapack||_2 = "
              << blockGivensSolutionError << endl;
        }
 
        out << "Least-squares condition number = "
            << leastSquaresCondNum << endl;
 
        // Now for the controversial part of the test: judging whether
        // we succeeded.  This includes the problem's condition
        // number, which is a measure of the maximum perturbation in
        // the solution for which we can still say that the solution
        // is valid.  We include a little wiggle room by including a
        // factor proportional to the square root of the number of
        // floating-point operations that influence the last entry
        // (the conventional Wilkinsonian heuristic), times 10 for
        // good measure.
        //
        // (The square root looks like it has something to do with an
        // average-case probabilistic argument, but doesn't really.
        // What's an "average problem"?)
        const magnitude_type wiggleFactor =
          10 * STM::squareroot( numRows*numCols );
        const magnitude_type solutionErrorBoundFactor =
          wiggleFactor * leastSquaresCondNum;
        const magnitude_type solutionErrorBound =
          solutionErrorBoundFactor * STS::eps();
        out << "Solution error bound: " << solutionErrorBoundFactor
            << " * eps = " << solutionErrorBound << endl;
 
        // Remember that NaN is not greater than, not less than, and
        // not equal to any other number, including itself.  Some
        // compilers will rudely optimize away the "x != x" test.
        if (STM::isnaninf (solutionErrorBound)) {
          // Hm, the solution error bound is Inf or NaN.  This
          // probably means that the test problem was generated
          // incorrectly.  We should return false in this case.
          return false;
        } else { // solution error bound is finite.
          if (STM::isnaninf (givensSolutionError)) {
            return false;
          } else if (givensSolutionError > solutionErrorBound) {
            return false;
          } else if (testBlockGivens) {
            if (STM::isnaninf (blockGivensSolutionError)) {
              return false;
            } else if (blockGivensSolutionError > solutionErrorBound) {
              return false;
            } else { // Givens and Block Givens tests succeeded.
              return true;
            }
          } else { // Not testing block Givens; Givens test succeeded.
            return true;
          }
        }
      }
 
      bool
      testTriangularSolves (std::ostream& out,
                            const int testProblemSize,
                            const ERobustness robustness,
                            const bool verbose=false)
      {
        using Teuchos::LEFT_SIDE;
        using Teuchos::RIGHT_SIDE;
        using std::endl;
        typedef Teuchos::SerialDenseMatrix<int, scalar_type> mat_type;
 
        Teuchos::oblackholestream blackHole;
        std::ostream& verboseOut = verbose ? out : blackHole;
 
        verboseOut << "Testing upper triangular solves" << endl;
        //
        // Construct an upper triangular linear system to solve.
        //
        verboseOut << "-- Generating test matrix" << endl;
        const int N = testProblemSize;
        mat_type R (N, N);
        // Fill the upper triangle of R with random numbers.
        for (int j = 0; j < N; ++j) {
          for (int i = 0; i <= j; ++i) {
            R(i,j) = STS::random ();
          }
        }
        // Compute the Frobenius norm of R for later use.
        const magnitude_type R_norm = R.normFrobenius ();
        // Fill the right-hand side B with random numbers.
        mat_type B (N, 1);
        B.random ();
        // Compute the Frobenius norm of B for later use.
        const magnitude_type B_norm = B.normFrobenius ();
 
        // Save a copy of the original upper triangular system.
        mat_type R_copy (Teuchos::Copy, R, N, N);
        mat_type B_copy (Teuchos::Copy, B, N, 1);
 
        // Solution vector.
        mat_type X (N, 1);
 
        // Solve RX = B.
        verboseOut << "-- Solving RX=B" << endl;
        // We're ignoring the return values for now.
        (void) solveUpperTriangularSystem (LEFT_SIDE, X, R, B, robustness);
        // Test the residual error.
        mat_type Resid (N, 1);
        Resid.assign (B_copy);
        Belos::details::LocalDenseMatrixOps<scalar_type> ops;
        ops.matMatMult (STS::one(), Resid, -STS::one(), R_copy, X);
        verboseOut << "---- ||R*X - B||_F = " << Resid.normFrobenius() << endl;
        verboseOut << "---- ||R||_F ||X||_F + ||B||_F = "
                   << (R_norm * X.normFrobenius() + B_norm)
                   << endl;
 
        // Restore R and B.
        R.assign (R_copy);
        B.assign (B_copy);
 
        //
        // Set up a right-side test problem: YR = B^*.
        //
        mat_type Y (1, N);
        mat_type B_star (1, N);
        ops.conjugateTranspose (B_star, B);
        mat_type B_star_copy (1, N);
        B_star_copy.assign (B_star);
        // Solve YR = B^*.
        verboseOut << "-- Solving YR=B^*" << endl;
        // We're ignoring the return values for now.
        (void) solveUpperTriangularSystem (RIGHT_SIDE, Y, R, B_star, robustness);
        // Test the residual error.
        mat_type Resid2 (1, N);
        Resid2.assign (B_star_copy);
        ops.matMatMult (STS::one(), Resid2, -STS::one(), Y, R_copy);
        verboseOut << "---- ||Y*R - B^*||_F = " << Resid2.normFrobenius() << endl;
        verboseOut << "---- ||Y||_F ||R||_F + ||B^*||_F = "
                   << (Y.normFrobenius() * R_norm + B_norm)
                   << endl;
 
        // FIXME (mfh 14 Oct 2011) The test always "passes" for now;
        // you have to inspect the output in order to see whether it
        // succeeded.  We really should fix the above to use the
        // infinity-norm bounds in Higham's book for triangular
        // solves.  That would automate the test.
        return true;
      }
 
    private:
      std::ostream& warn_;
 
      ERobustness defaultRobustness_;
 
    private:
      int
      solveLeastSquaresUsingSVD (mat_type& A, mat_type& X)
      {
        using Teuchos::Array;
        LocalDenseMatrixOps<Scalar> ops;
 
        if (defaultRobustness_ > ROBUSTNESS_SOME) {
          int count = ops.infNaNCount (A);
          TEUCHOS_TEST_FOR_EXCEPTION(count != 0, std::invalid_argument,
                             "solveLeastSquaresUsingSVD: The input matrix A "
                             "contains " << count << "Inf and/or NaN entr"
                             << (count != 1 ? "ies" : "y") << ".");
          count = ops.infNaNCount (X);
          TEUCHOS_TEST_FOR_EXCEPTION(count != 0, std::invalid_argument,
                             "solveLeastSquaresUsingSVD: The input matrix X "
                             "contains " << count << "Inf and/or NaN entr"
                             << (count != 1 ? "ies" : "y") << ".");
        }
        const int N = std::min (A.numRows(), A.numCols());
        lapack_type lapack;
 
        // Rank of A; to be computed by _GELSS and returned.
        int rank = N;
 
        // Use Scalar's machine precision for the rank tolerance,
        // not magnitude_type's machine precision.
        const magnitude_type rankTolerance = STS::eps();
 
        // Array of singular values.
        Array<magnitude_type> singularValues (N);
 
        // Extra workspace.  This is only used by _GELSS if Scalar is
        // complex.  Teuchos::LAPACK presents a unified interface to
        // _GELSS that always includes the RWORK argument, even though
        // LAPACK's SGELSS and DGELSS don't have the RWORK argument.
        // We always allocate at least one entry so that &rwork[0]
        // makes sense.
        Array<magnitude_type> rwork (1);
        if (STS::isComplex) {
          rwork.resize (std::max (1, 5 * N));
        }
        //
        // Workspace query
        //
        Scalar lworkScalar = STS::one(); // To be set by workspace query
        int info = 0;
        lapack.GELSS (A.numRows(), A.numCols(), X.numCols(),
                      A.values(), A.stride(), X.values(), X.stride(),
                      &singularValues[0], rankTolerance, &rank,
                      &lworkScalar, -1, &rwork[0], &info);
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::logic_error,
                           "_GELSS workspace query returned INFO = "
                           << info << " != 0.");
        const int lwork = static_cast<int> (STS::real (lworkScalar));
        TEUCHOS_TEST_FOR_EXCEPTION(lwork < 0, std::logic_error,
                           "_GELSS workspace query returned LWORK = "
                           << lwork << " < 0.");
        // Allocate workspace.  Size > 0 means &work[0] makes sense.
        Array<Scalar> work (std::max (1, lwork));
        // Solve the least-squares problem.
        lapack.GELSS (A.numRows(), A.numCols(), X.numCols(),
                      A.values(), A.stride(), X.values(), X.stride(),
                      &singularValues[0], rankTolerance, &rank,
                      &work[0], lwork, &rwork[0], &info);
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::runtime_error,
                           "_GELSS returned INFO = " << info << " != 0.");
        return rank;
      }
 
      void
      solveGivens (mat_type& y,
                   mat_type& R,
                   const mat_type& z,
                   const int curCol)
      {
        const int numRows = curCol + 2;
 
        // Now that we have the updated R factor of H, and the updated
        // right-hand side z, solve the least-squares problem by
        // solving the upper triangular linear system Ry=z for y.
        const mat_type R_view (Teuchos::View, R, numRows-1, numRows-1);
        const mat_type z_view (Teuchos::View, z, numRows-1, z.numCols());
        mat_type y_view (Teuchos::View, y, numRows-1, y.numCols());
 
        (void) solveUpperTriangularSystem (Teuchos::LEFT_SIDE, y_view,
                                           R_view, z_view, defaultRobustness_);
      }
 
      void
      makeRandomProblem (mat_type& H, mat_type& z)
      {
        // In GMRES, z always starts out with only the first entry
        // being nonzero.  That entry always has nonnegative real part
        // and zero imaginary part, since it is the initial residual
        // norm.
        H.random ();
        // Zero out the entries below the subdiagonal of H, so that it
        // is upper Hessenberg.
        for (int j = 0; j < H.numCols(); ++j) {
          for (int i = j+2; i < H.numRows(); ++i) {
            H(i,j) = STS::zero();
          }
        }
        // Initialize z, the right-hand side of the least-squares
        // problem.  Make the first entry of z nonzero.
        {
          // It's still possible that a random number will come up
          // zero after 1000 trials, but unlikely.  Nevertheless, it's
          // still important not to allow an infinite loop, for
          // example if the pseudorandom number generator is broken
          // and always returns zero.
          const int numTrials = 1000;
          magnitude_type z_init = STM::zero();
          for (int trial = 0; trial < numTrials && z_init == STM::zero(); ++trial) {
            z_init = STM::random();
          }
          TEUCHOS_TEST_FOR_EXCEPTION(z_init == STM::zero(), std::runtime_error,
                             "After " << numTrials << " trial"
                             << (numTrials != 1 ? "s" : "")
                             << ", we were unable to generate a nonzero pseudo"
                             "random real number.  This most likely indicates a "
                             "broken pseudorandom number generator.");
          const magnitude_type z_first = (z_init < 0) ? -z_init : z_init;
 
          // NOTE I'm assuming here that "scalar_type = magnitude_type"
          // assignments make sense.
          z(0,0) = z_first;
        }
      }
 
      void
      computeGivensRotation (const Scalar& x,
                             const Scalar& y,
                             Scalar& theCosine,
                             Scalar& theSine,
                             Scalar& result)
      {
        // _LARTG, an LAPACK aux routine, is slower but more accurate
        // than the BLAS' _ROTG.
        const bool useLartg = false;
 
        if (useLartg) {
          lapack_type lapack;
          // _LARTG doesn't clobber its input arguments x and y.
          lapack.LARTG (x, y, &theCosine, &theSine, &result);
        } else {
          // _ROTG clobbers its first two arguments.  x is overwritten
          // with the result of applying the Givens rotation: [x; y] ->
          // [x (on output); 0].  y is overwritten with the "fast"
          // Givens transform (see Golub and Van Loan, 3rd ed.).
          Scalar x_temp = x;
          Scalar y_temp = y;
          blas_type blas;
          blas.ROTG (&x_temp, &y_temp, &theCosine, &theSine);
          result = x_temp;
        }
      }
 
      void
      singularValues (const mat_type& A,
                      Teuchos::ArrayView<magnitude_type> sigmas)
      {
        using Teuchos::Array;
        using Teuchos::ArrayView;
 
        const int numRows = A.numRows();
        const int numCols = A.numCols();
        TEUCHOS_TEST_FOR_EXCEPTION(sigmas.size() < std::min (numRows, numCols),
                           std::invalid_argument,
                           "The sigmas array is only of length " << sigmas.size()
                           << ", but must be of length at least "
                           << std::min (numRows, numCols)
                           << " in order to hold all the singular values of the "
                           "matrix A.");
 
        // Compute the condition number of the matrix A, using a singular
        // value decomposition (SVD).  LAPACK's SVD routine overwrites the
        // input matrix, so make a copy.
        mat_type A_copy (numRows, numCols);
        A_copy.assign (A);
 
        // Workspace query.
        lapack_type lapack;
        int info = 0;
        Scalar lworkScalar = STS::zero();
        Array<magnitude_type> rwork (std::max (std::min (numRows, numCols) - 1, 1));
        lapack.GESVD ('N', 'N', numRows, numCols,
                      A_copy.values(), A_copy.stride(), &sigmas[0],
                      (Scalar*) NULL, 1, (Scalar*) NULL, 1,
                      &lworkScalar, -1, &rwork[0], &info);
 
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::logic_error,
                           "LAPACK _GESVD workspace query failed with INFO = "
                           << info << ".");
        const int lwork = static_cast<int> (STS::real (lworkScalar));
        TEUCHOS_TEST_FOR_EXCEPTION(lwork < 0, std::logic_error,
                           "LAPACK _GESVD workspace query returned LWORK = "
                           << lwork << " < 0.");
        // Make sure that the workspace array always has positive
        // length, so that &work[0] makes sense.
        Teuchos::Array<Scalar> work (std::max (1, lwork));
 
        // Compute the singular values of A.
        lapack.GESVD ('N', 'N', numRows, numCols,
                      A_copy.values(), A_copy.stride(), &sigmas[0],
                      (Scalar*) NULL, 1, (Scalar*) NULL, 1,
                      &work[0], lwork, &rwork[0], &info);
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::logic_error,
                           "LAPACK _GESVD failed with INFO = " << info << ".");
      }
 
      std::pair<magnitude_type, magnitude_type>
      extremeSingularValues (const mat_type& A)
      {
        using Teuchos::Array;
 
        const int numRows = A.numRows();
        const int numCols = A.numCols();
 
        Array<magnitude_type> sigmas (std::min (numRows, numCols));
        singularValues (A, sigmas);
        return std::make_pair (sigmas[0], sigmas[std::min(numRows, numCols) - 1]);
      }
 
      magnitude_type
      leastSquaresConditionNumber (const mat_type& A,
                                   const mat_type& b,
                                   const magnitude_type& residualNorm)
      {
        // Extreme singular values of A.
        const std::pair<magnitude_type, magnitude_type> sigmaMaxMin =
          extremeSingularValues (A);
 
        // Our solvers currently assume that H has full rank.  If the
        // test matrix doesn't have full rank, we stop right away.
        TEUCHOS_TEST_FOR_EXCEPTION(sigmaMaxMin.second == STM::zero(), std::runtime_error,
                           "The test matrix is rank deficient; LAPACK's _GESVD "
                           "routine reports that its smallest singular value is "
                           "zero.");
        // 2-norm condition number of A.  We checked above that the
        // denominator is nonzero.
        const magnitude_type A_cond = sigmaMaxMin.first / sigmaMaxMin.second;
 
        // "Theta" in the variable names below refers to the angle between
        // the vectors b and A*x, where x is the computed solution.  It
        // measures whether the residual norm is large (near ||b||) or
        // small (near 0).
        const magnitude_type sinTheta = residualNorm / b.normFrobenius();
 
        // \sin^2 \theta + \cos^2 \theta = 1.
        //
        // The range of sine is [-1,1], so squaring it won't overflow.
        // We still have to check whether sinTheta > 1, though.  This
        // is impossible in exact arithmetic, assuming that the
        // least-squares solver worked (b-A*0 = b and x minimizes
        // ||b-A*x||_2, so ||b-A*0||_2 >= ||b-A*x||_2).  However, it
        // might just be possible in floating-point arithmetic.  We're
        // just looking for an estimate, so if sinTheta > 1, we cap it
        // at 1.
        const magnitude_type cosTheta = (sinTheta > STM::one()) ?
          STM::zero() : STM::squareroot (1 - sinTheta * sinTheta);
 
        // This may result in Inf, if cosTheta is zero.  That's OK; in
        // that case, the condition number of the (full-rank)
        // least-squares problem is rightfully infinite.
        const magnitude_type tanTheta = sinTheta / cosTheta;
 
        // Condition number for the full-rank least-squares problem.
        return 2 * A_cond / cosTheta + tanTheta * A_cond * A_cond;
      }
 
      magnitude_type
      leastSquaresResidualNorm (const mat_type& A,
                                const mat_type& x,
                                const mat_type& b)
      {
        mat_type r (b.numRows(), b.numCols());
 
        // r := b - A*x
        r.assign (b);
        LocalDenseMatrixOps<Scalar> ops;
        ops.matMatMult (STS::one(), r, -STS::one(), A, x);
        return r.normFrobenius ();
      }
 
      magnitude_type
      solutionError (const mat_type& x_approx,
                     const mat_type& x_exact)
      {
        const int numRows = x_exact.numRows();
        const int numCols = x_exact.numCols();
 
        mat_type x_diff (numRows, numCols);
        for (int j = 0; j < numCols; ++j) {
          for (int i = 0; i < numRows; ++i) {
            x_diff(i,j) = x_exact(i,j) - x_approx(i,j);
          }
        }
        const magnitude_type scalingFactor = x_exact.normFrobenius();
 
        // If x_exact has zero norm, just use the absolute difference.
        return x_diff.normFrobenius() /
          (scalingFactor == STM::zero() ? STM::one() : scalingFactor);
      }
 
      magnitude_type
      updateColumnGivens (const mat_type& H,
                          mat_type& R,
                          mat_type& y,
                          mat_type& z,
                          Teuchos::ArrayView<scalar_type> theCosines,
                          Teuchos::ArrayView<scalar_type> theSines,
                          const int curCol)
      {
        using std::cerr;
        using std::endl;
 
        const int numRows = curCol + 2; // curCol is zero-based
        const int LDR = R.stride();
        const bool extraDebug = false;
 
        if (extraDebug) {
          cerr << "updateColumnGivens: curCol = " << curCol << endl;
        }
 
        // View of H( 1:curCol+1, curCol ) (in Matlab notation, if
        // curCol were a one-based index, as it would be in Matlab but
        // is not here).
        const mat_type H_col (Teuchos::View, H, numRows, 1, 0, curCol);
 
        // View of R( 1:curCol+1, curCol ) (again, in Matlab notation,
        // if curCol were a one-based index).
        mat_type R_col (Teuchos::View, R, numRows, 1, 0, curCol);
 
        // 1. Copy the current column from H into R, where it will be
        //    modified.
        R_col.assign (H_col);
 
        if (extraDebug) {
          printMatrix<Scalar> (cerr, "R_col before ", R_col);
        }
 
        // 2. Apply all the previous Givens rotations, if any, to the
        //    current column of the matrix.
        blas_type blas;
        for (int j = 0; j < curCol; ++j) {
          // BLAS::ROT really should take "const Scalar*" for these
          // arguments, but it wants a "Scalar*" instead, alas.
          Scalar theCosine = theCosines[j];
          Scalar theSine = theSines[j];
 
          if (extraDebug) {
            cerr << "  j = " << j << ": (cos,sin) = "
                 << theCosines[j] << "," << theSines[j] << endl;
          }
          blas.ROT (1, &R_col(j,0), LDR, &R_col(j+1,0), LDR,
                    &theCosine, &theSine);
        }
        if (extraDebug && curCol > 0) {
          printMatrix<Scalar> (cerr, "R_col after applying previous "
                               "Givens rotations", R_col);
        }
 
        // 3. Calculate new Givens rotation for R(curCol, curCol),
        //    R(curCol+1, curCol).
        Scalar theCosine, theSine, result;
        computeGivensRotation (R_col(curCol,0), R_col(curCol+1,0),
                               theCosine, theSine, result);
        theCosines[curCol] = theCosine;
        theSines[curCol] = theSine;
 
        if (extraDebug) {
          cerr << "  New cos,sin = " << theCosine << "," << theSine << endl;
        }
 
        // 4. _Apply_ the new Givens rotation.  We don't need to
        //    invoke _ROT here, because computeGivensRotation()
        //    already gives us the result: [x; y] -> [result; 0].
        R_col(curCol, 0) = result;
        R_col(curCol+1, 0) = STS::zero();
 
        if (extraDebug) {
          printMatrix<Scalar> (cerr, "R_col after applying current "
                               "Givens rotation", R_col);
        }
 
        // 5. Apply the resulting Givens rotation to z (the right-hand
        //    side of the projected least-squares problem).
        //
        // We prefer overgeneralization to undergeneralization by assuming
        // here that z may have more than one column.
        const int LDZ = z.stride();
        blas.ROT (z.numCols(),
                  &z(curCol,0), LDZ, &z(curCol+1,0), LDZ,
                  &theCosine, &theSine);
 
        if (extraDebug) {
          //mat_type R_after (Teuchos::View, R, numRows, numRows-1);
          //printMatrix<Scalar> (cerr, "R_after", R_after);
          //mat_type z_after (Teuchos::View, z, numRows, z.numCols());
          printMatrix<Scalar> (cerr, "z_after", z);
        }
 
        // The last entry of z is the nonzero part of the residual of the
        // least-squares problem.  Its magnitude gives the residual 2-norm
        // of the least-squares problem.
        return STS::magnitude( z(numRows-1, 0) );
      }
 
      magnitude_type
      solveLapack (const mat_type& H,
                   mat_type& R,
                   mat_type& y,
                   mat_type& z,
                   const int curCol)
      {
        const int numRows = curCol + 2;
        const int numCols = curCol + 1;
        const int LDR = R.stride();
 
        // Copy H( 1:curCol+1, 1:curCol ) into R( 1:curCol+1, 1:curCol ).
        const mat_type H_view (Teuchos::View, H, numRows, numCols);
        mat_type R_view (Teuchos::View, R, numRows, numCols);
        R_view.assign (H_view);
 
        // The LAPACK least-squares solver overwrites the right-hand side
        // vector with the solution, so first copy z into y.
        mat_type y_view (Teuchos::View, y, numRows, y.numCols());
        mat_type z_view (Teuchos::View, z, numRows, y.numCols());
        y_view.assign (z_view);
 
        // Workspace query for the least-squares routine.
        int info = 0;
        Scalar lworkScalar = STS::zero();
        lapack_type lapack;
        lapack.GELS ('N', numRows, numCols, y_view.numCols(),
                     NULL, LDR, NULL, y_view.stride(),
                     &lworkScalar, -1, &info);
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::logic_error,
                           "LAPACK _GELS workspace query failed with INFO = "
                           << info << ", for a " << numRows << " x " << numCols
                           << " matrix with " << y_view.numCols()
                           << " right hand side"
                           << ((y_view.numCols() != 1) ? "s" : "") << ".");
        TEUCHOS_TEST_FOR_EXCEPTION(STS::real(lworkScalar) < STM::zero(),
                           std::logic_error,
                           "LAPACK _GELS workspace query returned an LWORK with "
                           "negative real part: LWORK = " << lworkScalar
                           << ".  That should never happen.  Please report this "
                           "to the Belos developers.");
        TEUCHOS_TEST_FOR_EXCEPTION(STS::isComplex && STS::imag(lworkScalar) != STM::zero(),
                           std::logic_error,
                           "LAPACK _GELS workspace query returned an LWORK with "
                           "nonzero imaginary part: LWORK = " << lworkScalar
                           << ".  That should never happen.  Please report this "
                           "to the Belos developers.");
        // Cast workspace from Scalar to int.  Scalar may be complex,
        // hence the request for the real part.  Don't ask for the
        // magnitude, since computing the magnitude may overflow due
        // to squaring and square root to int.  Hopefully LAPACK
        // doesn't ever overflow int this way.
        const int lwork = std::max (1, static_cast<int> (STS::real (lworkScalar)));
 
        // Allocate workspace for solving the least-squares problem.
        Teuchos::Array<Scalar> work (lwork);
 
        // Solve the least-squares problem.  The ?: operator prevents
        // accessing the first element of the work array, if it has
        // length zero.
        lapack.GELS ('N', numRows, numCols, y_view.numCols(),
                     R_view.values(), R_view.stride(),
                     y_view.values(), y_view.stride(),
                     (lwork > 0 ? &work[0] : (Scalar*) NULL),
                     lwork, &info);
 
        TEUCHOS_TEST_FOR_EXCEPTION(info != 0, std::logic_error,
                           "Solving projected least-squares problem with LAPACK "
                           "_GELS failed with INFO = " << info << ", for a "
                           << numRows << " x " << numCols << " matrix with "
                           << y_view.numCols() << " right hand side"
                           << (y_view.numCols() != 1 ? "s" : "") << ".");
        // Extract the projected least-squares problem's residual error.
        // It's the magnitude of the last entry of y_view on output from
        // LAPACK's least-squares solver.
        return STS::magnitude( y_view(numRows-1, 0) );
      }
 
      magnitude_type
      updateColumnsGivens (const mat_type& H,
                           mat_type& R,
                           mat_type& y,
                           mat_type& z,
                           Teuchos::ArrayView<scalar_type> theCosines,
                           Teuchos::ArrayView<scalar_type> theSines,
                           const int startCol,
                           const int endCol)
      {
        TEUCHOS_TEST_FOR_EXCEPTION(startCol > endCol, std::invalid_argument,
                           "updateColumnGivens: startCol = " << startCol
                           << " > endCol = " << endCol << ".");
        magnitude_type lastResult = STM::zero();
        // [startCol, endCol] is an inclusive range.
        for (int curCol = startCol; curCol <= endCol; ++curCol) {
          lastResult = updateColumnGivens (H, R, y, z, theCosines, theSines, curCol);
        }
        return lastResult;
      }
 
      magnitude_type
      updateColumnsGivensBlock (const mat_type& H,
                                mat_type& R,
                                mat_type& y,
                                mat_type& z,
                                Teuchos::ArrayView<scalar_type> theCosines,
                                Teuchos::ArrayView<scalar_type> theSines,
                                const int startCol,
                                const int endCol)
      {
        const int numRows = endCol + 2;
        const int numColsToUpdate = endCol - startCol + 1;
        const int LDR = R.stride();
 
        // 1. Copy columns [startCol, endCol] from H into R, where they
        //    will be modified.
        {
          const mat_type H_view (Teuchos::View, H, numRows, numColsToUpdate, 0, startCol);
          mat_type R_view (Teuchos::View, R, numRows, numColsToUpdate, 0, startCol);
          R_view.assign (H_view);
        }
 
        // 2. Apply all the previous Givens rotations, if any, to
        //    columns [startCol, endCol] of the matrix.  (Remember
        //    that we're using a left-looking QR factorization
        //    approach; we haven't yet touched those columns.)
        blas_type blas;
        for (int j = 0; j < startCol; ++j) {
          blas.ROT (numColsToUpdate,
                    &R(j, startCol), LDR, &R(j+1, startCol), LDR,
                    &theCosines[j], &theSines[j]);
        }
 
        // 3. Update each column in turn of columns [startCol, endCol].
        for (int curCol = startCol; curCol < endCol; ++curCol) {
          // a. Apply the Givens rotations computed in previous
          //    iterations of this loop to the current column of R.
          for (int j = startCol; j < curCol; ++j) {
            blas.ROT (1, &R(j, curCol), LDR, &R(j+1, curCol), LDR,
                      &theCosines[j], &theSines[j]);
          }
          // b. Calculate new Givens rotation for R(curCol, curCol),
          //    R(curCol+1, curCol).
          Scalar theCosine, theSine, result;
          computeGivensRotation (R(curCol, curCol), R(curCol+1, curCol),
                                 theCosine, theSine, result);
          theCosines[curCol] = theCosine;
          theSines[curCol] = theSine;
 
          // c. _Apply_ the new Givens rotation.  We don't need to
          //    invoke _ROT here, because computeGivensRotation()
          //    already gives us the result: [x; y] -> [result; 0].
          R(curCol+1, curCol) = result;
          R(curCol+1, curCol) = STS::zero();
 
          // d. Apply the resulting Givens rotation to z (the right-hand
          //    side of the projected least-squares problem).
          //
          // We prefer overgeneralization to undergeneralization by
          // assuming here that z may have more than one column.
          const int LDZ = z.stride();
          blas.ROT (z.numCols(),
                    &z(curCol,0), LDZ, &z(curCol+1,0), LDZ,
                    &theCosine, &theSine);
        }
 
        // The last entry of z is the nonzero part of the residual of the
        // least-squares problem.  Its magnitude gives the residual 2-norm
        // of the least-squares problem.
        return STS::magnitude( z(numRows-1, 0) );
      }
    }; // class ProjectedLeastSquaresSolver
  } // namespace details
} // namespace Belos
 
#endif // __Belos_ProjectedLeastSquaresSolver_hpp