Automatic Differentiation
 
Loading...
Searching...
No Matches
qr.hpp
Go to the documentation of this file.
1#ifndef STAN_MATH_PRIM_FUN_QR_HPP
2#define STAN_MATH_PRIM_FUN_QR_HPP
3
7#include <algorithm>
8#include <tuple>
9
10namespace stan {
11namespace math {
12
22template <typename EigMat, require_eigen_t<EigMat>* = nullptr>
23inline std::tuple<
24 Eigen::Matrix<value_type_t<EigMat>, Eigen::Dynamic, Eigen::Dynamic>,
25 Eigen::Matrix<value_type_t<EigMat>, Eigen::Dynamic, Eigen::Dynamic>>
26qr(const EigMat& m) {
27 using matrix_t
28 = Eigen::Matrix<value_type_t<EigMat>, Eigen::Dynamic, Eigen::Dynamic>;
29 if (unlikely(m.size() == 0)) {
30 return std::make_tuple(matrix_t(0, 0), matrix_t(0, 0));
31 }
32
33 Eigen::HouseholderQR<matrix_t> qr(m.rows(), m.cols());
34 qr.compute(m);
35 matrix_t Q = qr.householderQ();
36 const int min_size = std::min(m.rows(), m.cols());
37 for (int i = 0; i < min_size; i++) {
38 if (qr.matrixQR().coeff(i, i) < 0) {
39 Q.col(i) *= -1.0;
40 }
41 }
42 matrix_t R = qr.matrixQR();
43 if (m.rows() > m.cols()) {
44 R.bottomRows(m.rows() - m.cols()).setZero();
45 }
46 for (int i = 0; i < min_size; i++) {
47 for (int j = 0; j < i; j++) {
48 R.coeffRef(i, j) = 0.0;
49 }
50 if (R(i, i) < 0) {
51 R.row(i) *= -1.0;
52 }
53 }
54 return std::make_tuple(std::move(Q), std::move(R));
55}
56
57} // namespace math
58} // namespace stan
59
60#endif
#define unlikely(x)
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:26
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...