devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

the Index types change.

Browse Source 
As discussed on the list (too long to explain here).
This commit is contained in:
Benoit Jacob
2010-05-30 16:00:58 -04:00
parent faa3ff3be6
commit aaaade4b3d
158 changed files with 3137 additions and 2878 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
Eigen/src/Eigenvalues/ComplexEigenSolver.h
View File

@@ -68,6 +68,7 @@ template<typename _MatrixType> class ComplexEigenSolver
/** \brief Scalar type for matrices of type \p _MatrixType. */
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
/** \brief Complex scalar type for \p _MatrixType.
*
@@ -110,7 +111,7 @@ template<typename _MatrixType> class ComplexEigenSolver
* according to the specified problem \a size.
* \sa ComplexEigenSolver()
*/
ComplexEigenSolver(int size)
ComplexEigenSolver(Index size)
: m_eivec(size, size),
m_eivalues(size),
m_schur(size),
@@ -216,7 +217,7 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
{
// this code is inspired from Jampack
assert(matrix.cols() == matrix.rows());
const int n = matrix.cols();
const Index n = matrix.cols();
const RealScalar matrixnorm = matrix.norm();
// Step 1: Do a complex Schur decomposition, A = U T U^*
@@ -227,11 +228,11 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// 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(int k=n-1 ; k>=0 ; k--)
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(int i=k-1 ; i>=0 ; i--)
for(Index i=k-1 ; i>=0 ; i--)
{
m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
if(k-i-1>0)
@@ -250,16 +251,16 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// 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(int k=0 ; k<n ; k++)
for(Index k=0 ; k<n ; k++)
{
m_eivec.col(k).normalize();
}
m_isInitialized = true;
// Step 4: Sort the eigenvalues
for (int i=0; i<n; i++)
for (Index i=0; i<n; i++)
{
int k;
Index k;
m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k);
if (k != 0)
{