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Added a Hessenberg decomposition class for both real and complex matrices.

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This is the first step towards a non-selfadjoint eigen solver.
Notes:
 - We might consider merging Tridiagonalization and Hessenberg toghether ?
 - Or we could factorize some code into a Householder class (could also be shared with QR)
This commit is contained in:
Gael Guennebaud
2008-06-08 15:03:23 +00:00
parent 4dd57b585d
commit e3fac69f19
6 changed files with 287 additions and 5 deletions
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20
test/qr.cpp
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@@ -39,17 +39,31 @@ template<typename MatrixType> void qr(const MatrixType& m)
MatrixType a = MatrixType::random(rows,cols);
QR<MatrixType> qrOfA(a);
VERIFY_IS_APPROX(a, qrOfA.matrixQ() * qrOfA.matrixR());
VERIFY_IS_NOT_APPROX(a+MatrixType::identity(rows, cols), qrOfA.matrixQ() * qrOfA.matrixR());
SquareMatrixType b = a.adjoint() * a;
// check tridiagonalization
Tridiagonalization<SquareMatrixType> tridiag(b);
VERIFY_IS_APPROX(b, tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint());
// check hessenberg decomposition
HessenbergDecomposition<SquareMatrixType> hess(b);
VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
VERIFY_IS_APPROX(tridiag.matrixT(), hess.matrixH());
b = SquareMatrixType::random(cols,cols);
hess.compute(b);
VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
}
void test_qr()
{
for(int i = 0; i < 1; i++) {
CALL_SUBTEST( qr(Matrix2f()) );
CALL_SUBTEST( qr(Matrix3d()) );
CALL_SUBTEST( qr(Matrix4d()) );
CALL_SUBTEST( qr(MatrixXf(12,8)) );
// CALL_SUBTEST( qr(MatrixXcd(17,7)) ); // complex numbers are not supported yet
CALL_SUBTEST( qr(MatrixXcd(5,5)) );
CALL_SUBTEST( qr(MatrixXcd(7,3)) );
}
}