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