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Add info() method which can be queried to check whether iteration converged.

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This commit is contained in:
Jitse Niesen
2010-06-03 22:59:57 +01:00
parent ed73a195e0
commit 9178e2bd54
11 changed files with 224 additions and 57 deletions
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42
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@@ -26,6 +26,9 @@
#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H
#define EIGEN_SELFADJOINTEIGENSOLVER_H
#include "./EigenvaluesCommon.h"
#include "./Tridiagonalization.h"
/** \eigenvalues_module \ingroup Eigenvalues_Module
* \nonstableyet
*
@@ -360,6 +363,16 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
}
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
*/
ComputationInfo info() const
{
ei_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
return m_info;
}
/** \brief Maximum number of iterations.
*
* Maximum number of iterations allowed for an eigenvalue to converge.
@@ -371,6 +384,7 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
RealVectorType m_eivalues;
TridiagonalizationType m_tridiag;
typename TridiagonalizationType::SubDiagonalType m_subdiag;
ComputationInfo m_info;
bool m_isInitialized;
bool m_eigenvectorsOk;
};
@@ -410,6 +424,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
m_eivalues.coeffRef(0,0) = ei_real(matrix.coeff(0,0));
if(computeEigenvectors)
m_eivec.setOnes();
m_info = Success;
m_isInitialized = true;
m_eigenvectorsOk = computeEigenvectors;
return *this;
@@ -443,7 +458,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
// if we spent too many iterations on the current element, we give up
iter++;
if(iter >= m_maxIterations) break;
if(iter > m_maxIterations) break;
start = end - 1;
while (start>0 && m_subdiag[start-1]!=0)
@@ -452,23 +467,26 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
ei_tridiagonal_qr_step(diag.data(), m_subdiag.data(), start, end, computeEigenvectors ? m_eivec.data() : (Scalar*)0, n);
}
if(iter >= m_maxIterations)
{
return *this;
}
if (iter <= m_maxIterations)
m_info = Success;
else
m_info = NoConvergence;
// Sort eigenvalues and corresponding vectors.
// TODO make the sort optional ?
// TODO use a better sort algorithm !!
for (Index i = 0; i < n-1; ++i)
if (m_info == Success)
{
Index k;
m_eivalues.segment(i,n-i).minCoeff(&k);
if (k > 0)
for (Index i = 0; i < n-1; ++i)
{
std::swap(m_eivalues[i], m_eivalues[k+i]);
if(computeEigenvectors)
m_eivec.col(i).swap(m_eivec.col(k+i));
Index k;
m_eivalues.segment(i,n-i).minCoeff(&k);
if (k > 0)
{
std::swap(m_eivalues[i], m_eivalues[k+i]);
if(computeEigenvectors)
m_eivec.col(i).swap(m_eivec.col(k+i));
}
}
}