1#ifndef STAN_MATH_FWD_FUN_MDIVIDE_LEFT_HPP 
    2#define STAN_MATH_FWD_FUN_MDIVIDE_LEFT_HPP 
   15template <
typename T1, 
typename T2,
 
   16          require_all_eigen_vt<is_fvar, T1, T2>* = 
nullptr,
 
   17          require_vt_same<T1, T2>* = 
nullptr>
 
   18inline Eigen::Matrix<value_type_t<T1>, T1::RowsAtCompileTime,
 
   19                     T2::ColsAtCompileTime>
 
   22  constexpr int S1 = T1::RowsAtCompileTime;
 
   23  constexpr int C2 = T2::ColsAtCompileTime;
 
   31  Eigen::Matrix<T, S1, C2> inv_A_mult_b(A.rows(), b.cols());
 
   32  Eigen::Matrix<T, S1, C2> inv_A_mult_deriv_b(A.rows(), b.cols());
 
   33  Eigen::Matrix<T, S1, S1> inv_A_mult_deriv_A(A.rows(), A.cols());
 
   34  Eigen::Matrix<T, S1, S1> val_A(A.rows(), A.cols());
 
   35  Eigen::Matrix<T, S1, S1> deriv_A(A.rows(), A.cols());
 
   37  const Eigen::Ref<const plain_type_t<T2>>& b_ref = b;
 
   38  const Eigen::Ref<const plain_type_t<T1>>& A_ref = A;
 
   39  for (
int j = 0; j < A.cols(); j++) {
 
   40    for (
int i = 0; i < A.rows(); i++) {
 
   41      val_A(i, j) = A_ref(i, j).val_;
 
   42      deriv_A(i, j) = A_ref(i, j).d_;
 
   50  Eigen::Matrix<T, S1, C2> deriv(A.rows(), b.cols());
 
   51  deriv = inv_A_mult_deriv_b - 
multiply(inv_A_mult_deriv_A, inv_A_mult_b);
 
   53  return to_fvar(inv_A_mult_b, deriv);
 
   56template <
typename T1, 
typename T2,
 
   59inline Eigen::Matrix<value_type_t<T2>, T1::RowsAtCompileTime,
 
   60                     T2::ColsAtCompileTime>
 
   68  const Eigen::Ref<const plain_type_t<T2>>& b_ref = b;
 
   73template <
typename T1, 
typename T2, require_eigen_vt<is_fvar, T1>* = 
nullptr,
 
   74          require_eigen_vt<std::is_arithmetic, T2>* = 
nullptr>
 
   75inline Eigen::Matrix<value_type_t<T1>, T1::RowsAtCompileTime,
 
   76                     T2::ColsAtCompileTime>
 
   79  constexpr int S1 = T1::RowsAtCompileTime;
 
   80  constexpr int C2 = T2::ColsAtCompileTime;
 
   88  Eigen::Matrix<T, S1, C2> inv_A_mult_b(A.rows(), b.cols());
 
   89  Eigen::Matrix<T, S1, S1> inv_A_mult_deriv_A(A.rows(), A.cols());
 
   90  Eigen::Matrix<T, S1, S1> val_A(A.rows(), A.cols());
 
   91  Eigen::Matrix<T, S1, S1> deriv_A(A.rows(), A.cols());
 
   93  const Eigen::Ref<const plain_type_t<T1>>& A_ref = A;
 
   94  for (
int j = 0; j < A.cols(); j++) {
 
   95    for (
int i = 0; i < A.rows(); i++) {
 
   96      val_A(i, j) = A_ref(i, j).val_;
 
   97      deriv_A(i, j) = A_ref(i, j).d_;
 
  104  Eigen::Matrix<T, S1, C2> deriv(A.rows(), b.cols());
 
  105  deriv = -
multiply(inv_A_mult_deriv_A, inv_A_mult_b);
 
  107  return to_fvar(inv_A_mult_b, deriv);
 
require_t< container_type_check_base< is_eigen, value_type_t, TypeCheck, Check... > > require_eigen_vt
Require type satisfies is_eigen.
 
typename value_type< T >::type value_type_t
Helper function for accessing underlying type.
 
void check_square(const char *function, const char *name, const T_y &y)
Check if the specified matrix is square.
 
void check_multiplicable(const char *function, const char *name1, const T1 &y1, const char *name2, const T2 &y2)
Check if the matrices can be multiplied.
 
auto multiply(const Mat1 &m1, const Mat2 &m2)
Return the product of the specified matrices.
 
fvar< T > to_fvar(const T &x)
 
Eigen::Matrix< value_type_t< T1 >, T1::RowsAtCompileTime, T2::ColsAtCompileTime > mdivide_left(const T1 &A, const T2 &b)
 
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...