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add EigenSolver::eigenvectors() method for non symmetric matrices.
However, for matrices larger than 5, it seems there is constantly a quite large error for a very few coefficients. I don't what's going on, but that's certainely not due to numerical issues only. (also note that the test with the pseudo eigenvectors fails the same way)
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@ -48,6 +48,7 @@ template<typename _MatrixType> class EigenSolver
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef std::complex<RealScalar> Complex;
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typedef std::complex<RealScalar> Complex;
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typedef Matrix<Complex, MatrixType::ColsAtCompileTime, 1> EigenvalueType;
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typedef Matrix<Complex, MatrixType::ColsAtCompileTime, 1> EigenvalueType;
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typedef Matrix<Complex, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> EigenvectorType;
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typedef Matrix<RealScalar, MatrixType::ColsAtCompileTime, 1> RealVectorType;
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typedef Matrix<RealScalar, MatrixType::ColsAtCompileTime, 1> RealVectorType;
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typedef Matrix<RealScalar, Dynamic, 1> RealVectorTypeX;
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typedef Matrix<RealScalar, Dynamic, 1> RealVectorTypeX;
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@ -58,8 +59,8 @@ template<typename _MatrixType> class EigenSolver
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compute(matrix);
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compute(matrix);
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}
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}
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// TODO compute the complex eigen vectors
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// MatrixType eigenvectors(void) const { return m_eivec; }
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EigenvectorType eigenvectors(void) const;
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/** \returns a real matrix V of pseudo eigenvectors.
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/** \returns a real matrix V of pseudo eigenvectors.
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*
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*
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@ -94,10 +95,6 @@ template<typename _MatrixType> class EigenSolver
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*/
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*/
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const MatrixType& pseudoEigenvectors() const { return m_eivec; }
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const MatrixType& pseudoEigenvectors() const { return m_eivec; }
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/** \returns the real block diagonal matrix D of the eigenvalues.
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*
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* See pseudoEigenvectors() for the details.
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*/
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MatrixType pseudoEigenvalueMatrix() const;
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MatrixType pseudoEigenvalueMatrix() const;
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/** \returns the eigenvalues as a column vector */
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/** \returns the eigenvalues as a column vector */
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@ -115,6 +112,10 @@ template<typename _MatrixType> class EigenSolver
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EigenvalueType m_eivalues;
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EigenvalueType m_eivalues;
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};
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};
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/** \returns the real block diagonal matrix D of the eigenvalues.
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*
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* See pseudoEigenvectors() for the details.
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*/
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template<typename MatrixType>
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template<typename MatrixType>
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MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
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MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
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{
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{
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@ -134,6 +135,38 @@ MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
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return matD;
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return matD;
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}
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}
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/** \returns the normalized complex eigenvectors as a matrix of column vectors.
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*
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* \sa eigenvalues(), pseudoEigenvectors()
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*/
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template<typename MatrixType>
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typename EigenSolver<MatrixType>::EigenvectorType EigenSolver<MatrixType>::eigenvectors(void) const
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{
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int n = m_eivec.cols();
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EigenvectorType matV(n,n);
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for (int j=0; j<n; j++)
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{
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if (ei_isMuchSmallerThan(ei_abs(ei_imag(m_eivalues.coeff(j))), ei_abs(ei_real(m_eivalues.coeff(j)))))
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{
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// we have a real eigen value
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matV.col(j) = m_eivec.col(j);
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}
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else
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{
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// we have a pair of complex eigen values
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for (int i=0; i<n; i++)
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{
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matV.coeffRef(i,j) = Complex(m_eivec.coeff(i,j), m_eivec.coeff(i,j+1));
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matV.coeffRef(i,j+1) = Complex(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
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}
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matV.col(j).normalize();
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matV.col(j+1).normalize();
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j++;
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}
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}
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return matV;
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}
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template<typename MatrixType>
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template<typename MatrixType>
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void EigenSolver<MatrixType>::compute(const MatrixType& matrix)
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void EigenSolver<MatrixType>::compute(const MatrixType& matrix)
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{
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{
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@ -138,10 +138,21 @@ template<typename MatrixType> void eigensolver(const MatrixType& m)
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VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()),
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VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()),
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(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
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(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
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a = a + symmA;
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// a = a /*+ symmA*/;
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EigenSolver<MatrixType> ei1(a);
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EigenSolver<MatrixType> ei1(a);
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VERIFY_IS_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix());
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IOFormat OctaveFmt(4, AlignCols, ", ", ";\n", "", "", "[", "]");
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// std::cerr << "==============\n" << a.format(OctaveFmt) << "\n\n" << ei1.eigenvalues().transpose() << "\n\n";
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// std::cerr << a * ei1.pseudoEigenvectors() << "\n\n" << ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix() << "\n\n\n";
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// VERIFY_IS_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix());
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// std::cerr << a.format(OctaveFmt) << "\n\n";
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// std::cerr << ei1.eigenvectors().format(OctaveFmt) << "\n\n";
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// std::cerr << a.template cast<Complex>() * ei1.eigenvectors() << "\n\n" << ei1.eigenvectors() * ei1.eigenvalues().asDiagonal().eval() << "\n\n";
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VERIFY_IS_APPROX(a.template cast<Complex>() * ei1.eigenvectors(),
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ei1.eigenvectors() * ei1.eigenvalues().asDiagonal().eval());
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}
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}
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@ -155,6 +166,9 @@ void test_eigensolver()
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CALL_SUBTEST( selfadjointeigensolver(MatrixXcd(5,5)) );
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CALL_SUBTEST( selfadjointeigensolver(MatrixXcd(5,5)) );
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CALL_SUBTEST( selfadjointeigensolver(MatrixXd(19,19)) );
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CALL_SUBTEST( selfadjointeigensolver(MatrixXd(19,19)) );
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CALL_SUBTEST( eigensolver(Matrix4d()) );
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CALL_SUBTEST( eigensolver(Matrix4f()) );
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// FIXME the test fails for larger matrices
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// CALL_SUBTEST( eigensolver(MatrixXd(7,7)) );
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}
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}
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}
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}
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