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Refactor compute() by splitting off two smaller private methods.

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This commit is contained in:
Jitse Niesen 2010-06-03 22:29:11 +01:00
parent e64460d5d0
commit ed73a195e0
1 changed files with 52 additions and 39 deletions
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91
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@ -227,6 +227,10 @@ template<typename _MatrixType> class ComplexEigenSolver
bool m_isInitialized;
bool m_eigenvectorsOk;
EigenvectorType m_matX;
private:
void doComputeEigenvectors(RealScalar matrixnorm);
void sortEigenvalues(bool computeEigenvectors);
};
@ -235,52 +239,64 @@ ComplexEigenSolver<MatrixType>& ComplexEigenSolver<MatrixType>::compute(const Ma
{
// this code is inspired from Jampack
assert(matrix.cols() == matrix.rows());
const Index n = matrix.cols();
const RealScalar matrixnorm = matrix.norm();
// Step 1: Do a complex Schur decomposition, A = U T U^*
// Do a complex Schur decomposition, A = U T U^*
// The eigenvalues are on the diagonal of T.
m_schur.compute(matrix, computeEigenvectors);
m_eivalues = m_schur.matrixT().diagonal();
if(computeEigenvectors)
{
// Step 2: Compute X such that T = X D X^(-1), where D is the diagonal of T.
// The matrix X is unit triangular.
m_matX = EigenvectorType::Zero(n, n);
for(Index k=n-1 ; k>=0 ; k--)
{
m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
// Compute X(i,k) using the (i,k) entry of the equation X T = D X
for(Index i=k-1 ; i>=0 ; i--)
{
m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
if(k-i-1>0)
m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
if(z==ComplexScalar(0))
{
// If the i-th and k-th eigenvalue are equal, then z equals 0.
// Use a small value instead, to prevent division by zero.
ei_real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
}
m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
}
}
// Step 3: Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
m_eivec.noalias() = m_schur.matrixU() * m_matX;
// .. and normalize the eigenvectors
for(Index k=0 ; k<n ; k++)
{
m_eivec.col(k).normalize();
}
}
doComputeEigenvectors(matrix.norm());
sortEigenvalues(computeEigenvectors);
m_isInitialized = true;
m_eigenvectorsOk = computeEigenvectors;
return *this;
}
// Step 4: Sort the eigenvalues
template<typename MatrixType>
void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm)
{
const Index n = m_eivalues.size();
// Compute X such that T = X D X^(-1), where D is the diagonal of T.
// The matrix X is unit triangular.
m_matX = EigenvectorType::Zero(n, n);
for(Index k=n-1 ; k>=0 ; k--)
{
m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
// Compute X(i,k) using the (i,k) entry of the equation X T = D X
for(Index i=k-1 ; i>=0 ; i--)
{
m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
if(k-i-1>0)
m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
if(z==ComplexScalar(0))
{
// If the i-th and k-th eigenvalue are equal, then z equals 0.
// Use a small value instead, to prevent division by zero.
ei_real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
}
m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
}
}
// Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
m_eivec.noalias() = m_schur.matrixU() * m_matX;
// .. and normalize the eigenvectors
for(Index k=0 ; k<n ; k++)
{
m_eivec.col(k).normalize();
}
}
template<typename MatrixType>
void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors)
{
const Index n = m_eivalues.size();
for (Index i=0; i<n; i++)
{
Index k;
@ -293,10 +309,7 @@ ComplexEigenSolver<MatrixType>& ComplexEigenSolver<MatrixType>::compute(const Ma
m_eivec.col(i).swap(m_eivec.col(k));
}
}
return *this;
}
#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H