Stan Math Library  2.15.0
reverse mode automatic differentiation
log_determinant.hpp
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1 #ifndef STAN_MATH_REV_MAT_FUN_LOG_DETERMINANT_HPP
2 #define STAN_MATH_REV_MAT_FUN_LOG_DETERMINANT_HPP
3 
6 #include <stan/math/rev/core.hpp>
7 
8 namespace stan {
9  namespace math {
10 
11  template <int R, int C>
12  inline var log_determinant(const Eigen::Matrix<var, R, C>& m) {
13  using Eigen::Matrix;
14 
15  math::check_square("log_determinant", "m", m);
16 
17  Matrix<double, R, C> m_d(m.rows(), m.cols());
18  for (int i = 0; i < m.size(); ++i)
19  m_d(i) = m(i).val();
20 
21  Eigen::FullPivHouseholderQR<Matrix<double, R, C> > hh
22  = m_d.fullPivHouseholderQr();
23 
24  double val = hh.logAbsDeterminant();
25 
26  vari** varis
28  for (int i = 0; i < m.size(); ++i)
29  varis[i] = m(i).vi_;
30 
31  Matrix<double, R, C> m_inv_transpose = hh.inverse().transpose();
32  double* gradients
33  = ChainableStack::memalloc_.alloc_array<double>(m.size());
34  for (int i = 0; i < m.size(); ++i)
35  gradients[i] = m_inv_transpose(i);
36 
37  return var(new precomputed_gradients_vari(val, m.size(),
38  varis, gradients));
39  }
40 
41  }
42 }
43 #endif
The variable implementation base class.
Definition: vari.hpp:30
fvar< T > log_determinant(const Eigen::Matrix< fvar< T >, R, C > &m)
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:30
A variable implementation taking a sequence of operands and partial derivatives with respect to the o...
void check_square(const char *function, const char *name, const Eigen::Matrix< T_y, Eigen::Dynamic, Eigen::Dynamic > &y)
Check if the specified matrix is square.
T * alloc_array(size_t n)
Allocate an array on the arena of the specified size to hold values of the specified template paramet...

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