Automatic Differentiation
 
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eigenvalues.hpp
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1#ifndef STAN_MATH_PRIM_FUN_EIGENVALUES_HPP
2#define STAN_MATH_PRIM_FUN_EIGENVALUES_HPP
3
6
7namespace stan {
8namespace math {
9
18template <typename EigMat, require_eigen_matrix_dynamic_t<EigMat>* = nullptr,
19 require_not_vt_complex<EigMat>* = nullptr>
20inline Eigen::Matrix<complex_return_t<value_type_t<EigMat>>, -1, 1> eigenvalues(
21 const EigMat& m) {
22 if (unlikely(m.size() == 0)) {
23 return Eigen::Matrix<complex_return_t<value_type_t<EigMat>>, -1, 1>(0, 1);
24 }
25 check_square("eigenvalues", "m", m);
26 using PlainMat = plain_type_t<EigMat>;
27 const PlainMat& m_eval = m;
28
29 Eigen::EigenSolver<PlainMat> solver(m_eval, false);
30 return solver.eigenvalues();
31}
32
41template <typename EigCplxMat,
43inline Eigen::Matrix<complex_return_t<value_type_t<EigCplxMat>>, -1, 1>
44eigenvalues(const EigCplxMat& m) {
45 if (unlikely(m.size() == 0)) {
46 return Eigen::Matrix<complex_return_t<value_type_t<EigCplxMat>>, -1, 1>(0,
47 1);
48 }
49 check_square("eigenvalues", "m", m);
50 using PlainMat = Eigen::Matrix<scalar_type_t<EigCplxMat>, -1, -1>;
51 const PlainMat& m_eval = m;
52
53 Eigen::ComplexEigenSolver<PlainMat> solver(m_eval, false);
54
55 return solver.eigenvalues();
56}
57
58} // namespace math
59} // namespace stan
60#endif
#define unlikely(x)
require_t< container_type_check_base< is_eigen_matrix_dynamic, value_type_t, TypeCheck, Check... > > require_eigen_matrix_dynamic_vt
Require type satisfies is_eigen_matrix_dynamic.
void check_square(const char *function, const char *name, const T_y &y)
Check if the specified matrix is square.
Eigen::Matrix< complex_return_t< value_type_t< EigMat > >, -1, 1 > eigenvalues(const EigMat &m)
Return the eigenvalues of a (real-valued) matrix.
typename plain_type< T >::type plain_type_t
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9