// Copyright (C) 2005, 2006 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Common Public License.
//
// $Id: IpDenseGenMatrix.cpp 759 2006-07-07 03:07:08Z andreasw $
//
// Authors:  Andreas Waechter             IBM    2005-12-24

#include "IpDenseGenMatrix.hpp"
#include "IpBlas.hpp"
#include "IpLapack.hpp"

namespace Ipopt
{

#ifdef IP_DEBUG
  static const Index dbg_verbosity = 0;
#endif

  DenseGenMatrix::DenseGenMatrix(const DenseGenMatrixSpace* owner_space)
      :
      Matrix(owner_space),
      owner_space_(owner_space),
      values_(new Number[NCols()*NRows()]),
      initialized_(false)
  {}

  DenseGenMatrix::~DenseGenMatrix()
  {
    delete [] values_;
  }

  void DenseGenMatrix::ScaleColumns(const DenseVector& scal_vec)
  {
    DBG_ASSERT(scal_vec.Dim() == NCols());
    DBG_ASSERT(initialized_);

    const Number* scal_values = scal_vec.Values();

    for (Index j=0; j<NCols(); j++) {
      IpBlasDscal(NRows(), scal_values[j], &values_[j*NRows()], 1);
    }
    ObjectChanged();
  }

  void DenseGenMatrix::Copy(const DenseGenMatrix& M)
  {
    DBG_ASSERT(NCols()==M.NCols());
    DBG_ASSERT(NRows()==M.NRows());

    IpBlasDcopy(NCols()*NRows(), M.Values(), 1, values_, 1);
    initialized_ = true;
    ObjectChanged();
  }

  void DenseGenMatrix::FillIdentity(Number factor /*=1.*/)
  {
    DBG_ASSERT(NCols()==NRows());

    const Number zero = 0.;
    IpBlasDcopy(NCols()*NRows(), &zero, 0, values_, 1);

    if (factor!=0.) {
      for (Index i=0; i<NRows(); i++) {
        values_[i + i*NRows()] = factor;
      }
    }
    ObjectChanged();
    initialized_ = true;
  }

  void DenseGenMatrix::AddMatrixProduct(Number alpha, const DenseGenMatrix& A,
                                        bool transA, const DenseGenMatrix& B,
                                        bool transB, Number beta)
  {
    Index m = NRows();
    DBG_ASSERT((transA && A.NCols()==m) || (!transA && A.NRows()==m));
    Index n = NCols();
    DBG_ASSERT((transB && B.NRows()==n) || (!transB && B.NCols()==n));
    Index k;
    if (transA) {
      k = A.NRows();
    }
    else {
      k = A.NCols();
    }
    DBG_ASSERT((transB && B.NCols()==k) || (!transB && B.NRows()==k));
    DBG_ASSERT(beta==0. || initialized_);

    IpBlasDgemm(transA, transB, m, n, k, alpha, A.Values(), A.NRows(),
                B.Values(), B.NRows(), beta, values_, NRows());
    initialized_ = true;
    ObjectChanged();
  }

  void DenseGenMatrix::HighRankUpdateTranspose(Number alpha,
      const MultiVectorMatrix& V1,
      const MultiVectorMatrix& V2,
      Number beta)
  {
    DBG_ASSERT(NRows()==V1.NCols());
    DBG_ASSERT(NCols()==V2.NCols());
    DBG_ASSERT(beta==0. || initialized_);

    if (beta==0.) {
      for (Index j=0; j<NCols(); j++) {
        for (Index i=0; i<NRows(); i++) {
          values_[i+j*NRows()] = alpha*V1.GetVector(i)->Dot(*V2.GetVector(j));
        }
      }
    }
    else {
      for (Index j=0; j<NCols(); j++) {
        for (Index i=0; i<NRows(); i++) {
          values_[i+j*NRows()] = alpha*V1.GetVector(i)->Dot(*V2.GetVector(j))
                                 + beta*values_[i+j*NRows()];
        }
      }
    }
    initialized_ = true;
    ObjectChanged();
  }

  bool DenseGenMatrix::ComputeCholeskyFactor(const DenseSymMatrix& M)
  {
    Index dim = M.Dim();
    DBG_ASSERT(dim==NCols());
    DBG_ASSERT(dim==NRows());

    ObjectChanged();

    // First we copy the content of the symmetric matrix into J
    const Number* Mvalues = M.Values();
    for (Index j=0; j<dim; j++) {
      for (Index i=j; i<dim; i++) {
        values_[i+j*dim] = Mvalues[i+j*dim];
      }
    }

    // Now call the lapack subroutine to perform the factorization
    Index info;
    IpLapackDpotrf(dim, values_, dim, info);

    DBG_ASSERT(info>=0);
    if (info!=0) {
      initialized_ = false;
      return false;
    }

    // We set all strictly upper values to zero
    // ToDo: This might not be necessary?!?
    for (Index j=1; j<dim; j++) {
      for (Index i=0; i<j; i++) {
        values_[i+j*dim] = 0.;
      }
    }
    initialized_ = true;
    return true;
  }

  bool DenseGenMatrix::ComputeEigenVectors(const DenseSymMatrix& M,
      DenseVector& Evalues)
  {
    Index dim = M.Dim();
    DBG_ASSERT(Evalues.Dim()==dim);
    DBG_ASSERT(NRows()==dim);
    DBG_ASSERT(NCols()==dim);

    // First we copy the content of the matrix into Q
    const Number* Mvalues = M.Values();
    for (Index j=0; j<dim; j++) {
      for (Index i=j; i<dim; i++) {
        values_[i+j*dim] = Mvalues[i+j*dim];
      }
    }

    bool compute_eigenvectors = true;
    Number* Evals = Evalues.Values();
    Index info;
    IpLapackDsyev(compute_eigenvectors, dim, values_,
                  dim, Evals, info);

    initialized_ = (info==0);
    ObjectChanged();
    return (info==0);
  }

  void DenseGenMatrix::CholeskyBackSolveMatrix(bool trans, Number alpha,
      DenseGenMatrix& B) const
  {
    DBG_ASSERT(NRows()==NCols());
    DBG_ASSERT(B.NRows()==NRows());
    DBG_ASSERT(initialized_);

    Number* Bvalues = B.Values();

    IpBlasDtrsm(trans, NRows(), B.NCols(), alpha,
                values_, NRows(), Bvalues, B.NRows());
  }

  void DenseGenMatrix::CholeskySolveVector(DenseVector& b) const
  {
    DBG_ASSERT(NRows()==NCols());
    DBG_ASSERT(b.Dim()==NRows());
    DBG_ASSERT(initialized_);

    Number* bvalues = b.Values();

    IpLapackDpotrs(NRows(), 1, values_, NRows(), bvalues, b.Dim());
  }

  void DenseGenMatrix::CholeskySolveMatrix(DenseGenMatrix& B) const
  {
    DBG_ASSERT(NRows()==NCols());
    DBG_ASSERT(B.NRows()==NRows());
    DBG_ASSERT(initialized_);

    Number* Bvalues = B.Values();

    IpLapackDpotrs(NRows(), B.NCols(), values_, NRows(), Bvalues, B.NRows());
  }

  void DenseGenMatrix::MultVectorImpl(Number alpha, const Vector &x,
                                      Number beta, Vector &y) const
  {
    //  A few sanity checks
    DBG_ASSERT(NCols()==x.Dim());
    DBG_ASSERT(NRows()==y.Dim());
    DBG_ASSERT(initialized_);

    // See if we can understand the data
    const DenseVector* dense_x = dynamic_cast<const DenseVector*>(&x);
    DBG_ASSERT(dense_x); /* ToDo: Implement others */
    DenseVector* dense_y = dynamic_cast<DenseVector*>(&y);
    DBG_ASSERT(dense_y); /* ToDo: Implement others */

    bool trans = false;
    IpBlasDgemv(trans, NRows(), NCols(), alpha, values_, NRows(),
                dense_x->Values(), 1, beta, dense_y->Values(), 1);
  }

  void DenseGenMatrix::TransMultVectorImpl(Number alpha, const Vector &x,
      Number beta, Vector &y) const
  {
    //  A few sanity checks
    DBG_ASSERT(NCols()==y.Dim());
    DBG_ASSERT(NRows()==x.Dim());
    DBG_ASSERT(initialized_);

    // See if we can understand the data
    const DenseVector* dense_x = dynamic_cast<const DenseVector*>(&x);
    DBG_ASSERT(dense_x); /* ToDo: Implement others */
    DenseVector* dense_y = dynamic_cast<DenseVector*>(&y);
    DBG_ASSERT(dense_y); /* ToDo: Implement others */

    bool trans = true;
    IpBlasDgemv(trans, NRows(), NCols(), alpha, values_, NRows(),
                dense_x->Values(), 1, beta, dense_y->Values(), 1);
  }

  bool DenseGenMatrix::HasValidNumbersImpl() const
  {
    DBG_ASSERT(initialized_);
    Number sum = IpBlasDasum(NRows()*NCols(), values_, 1);
    return IsFiniteNumber(sum);
  }

  void DenseGenMatrix::PrintImpl(const Journalist& jnlst,
                                 EJournalLevel level,
                                 EJournalCategory category,
                                 const std::string& name,
                                 Index indent,
                                 const std::string& prefix) const
  {
    jnlst.Printf(level, category, "\n");
    jnlst.PrintfIndented(level, category, indent,
                         "%sDenseGenMatrix \"%s\" with %d rows and %d columns:\n",
                         prefix.c_str(), name.c_str(), NRows(), NCols());

    if (initialized_) {
      for (Index j=0; j<NCols(); j++) {
        for (Index i=0; i<NRows(); i++) {
          jnlst.PrintfIndented(level, category, indent,
                               "%s%s[%5d,%5d]=%23.16e\n",
                               prefix.c_str(), name.c_str(), i, j, values_[i+NRows()*j]);
        }
      }
    }
    else {
      jnlst.PrintfIndented(level, category, indent,
                           "The matrix has not yet been initialized!\n");
    }
  }

  DenseGenMatrixSpace::DenseGenMatrixSpace(Index nRows, Index nCols)
      :
      MatrixSpace(nRows, nCols)
  {}

} // namespace Ipopt
