mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-15 07:10:37 +08:00
33 lines
1.3 KiB
C++
33 lines
1.3 KiB
C++
#include<Eigen/Core>
|
|
|
|
using namespace Eigen;
|
|
|
|
/** From Golub & van Loan Algorithm 5.1.1 page 210
|
|
*/
|
|
template<typename InputVector, typename OutputVector>
|
|
void ei_compute_householder(const InputVector& x, OutputVector *v, typename OutputVector::RealScalar *beta)
|
|
{
|
|
EIGEN_STATIC_ASSERT(ei_is_same_type<typename InputVector::Scalar, typename OutputVector::Scalar>::ret,
|
|
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
|
|
EIGEN_STATIC_ASSERT((InputVector::SizeAtCompileTime == OutputVector::SizeAtCompileTime+1)
|
|
|| InputVector::SizeAtCompileTime == Dynamic
|
|
|| OutputVector::SizeAtCompileTime == Dynamic,
|
|
YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
|
|
typedef typename OutputVector::RealScalar RealScalar;
|
|
ei_assert(x.size() == v->size()+1);
|
|
int n = x.size();
|
|
RealScalar sigma = x.end(n-1).squaredNorm();
|
|
*v = x.end(n-1);
|
|
// the big assumption in this code is that ei_abs2(x->coeff(0)) is not much smaller than sigma.
|
|
if(ei_isMuchSmallerThan(sigma, ei_abs2(x.coeff(0))))
|
|
{
|
|
// in this case x is approx colinear to (1,0,....,0)
|
|
// fixme, implement this trivial case
|
|
}
|
|
else
|
|
{
|
|
RealScalar mu = ei_sqrt(ei_abs2(x.coeff(0)) + sigma);
|
|
RealScalar kappa = -sigma/(x.coeff(0)+mu);
|
|
*beta =
|
|
}
|
|
} |