Automatic Differentiation
 
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qr_thin_Q.hpp
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1#ifndef STAN_MATH_PRIM_FUN_QR_THIN_Q_HPP
2#define STAN_MATH_PRIM_FUN_QR_THIN_Q_HPP
3
6#include <algorithm>
7
8namespace stan {
9namespace math {
10
18template <typename EigMat, require_eigen_t<EigMat>* = nullptr>
19Eigen::Matrix<value_type_t<EigMat>, Eigen::Dynamic, Eigen::Dynamic> qr_thin_Q(
20 const EigMat& m) {
21 using matrix_t
22 = Eigen::Matrix<value_type_t<EigMat>, Eigen::Dynamic, Eigen::Dynamic>;
23 if (unlikely(m.size() == 0)) {
24 return matrix_t(0, 0);
25 }
26 Eigen::HouseholderQR<matrix_t> qr(m.rows(), m.cols());
27 qr.compute(m);
28 const int min_size = std::min(m.rows(), m.cols());
29 matrix_t Q = qr.householderQ() * matrix_t::Identity(m.rows(), min_size);
30 for (int i = 0; i < min_size; i++) {
31 if (qr.matrixQR().coeff(i, i) < 0) {
32 Q.col(i) *= -1.0;
33 }
34 }
35 return Q;
36}
37
38} // namespace math
39} // namespace stan
40
41#endif
#define unlikely(x)
matrix_cl< double > qr_thin_Q(T_m &&m)
Returns the orthogonal factor of the fat QR decomposition.
Definition qr_thin_Q.hpp:20
std::tuple< Eigen::Matrix< value_type_t< EigMat >, Eigen::Dynamic, Eigen::Dynamic >, Eigen::Matrix< value_type_t< EigMat >, Eigen::Dynamic, Eigen::Dynamic > > qr(const EigMat &m)
Returns the fat QR decomposition.
Definition qr.hpp:25
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9