Automatic Differentiation
 
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lambert_w.hpp
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1#ifndef STAN_MATH_FWD_FUN_LAMBERT_W_HPP
2#define STAN_MATH_FWD_FUN_LAMBERT_W_HPP
3
7#include <cmath>
8
9namespace stan {
10namespace math {
11
12template <typename T>
13inline fvar<T> lambert_w0(const fvar<T>& x) {
14 const auto cached_result = lambert_w0(x.val_);
15 return fvar<T>(cached_result, (x.d_ / (x.val_ + exp(cached_result))));
16}
17
18template <typename T>
19inline fvar<T> lambert_wm1(const fvar<T>& x) {
20 const auto cached_result = lambert_wm1(x.val_);
21 return fvar<T>(cached_result, (x.d_ / (x.val_ + exp(cached_result))));
22}
23
24} // namespace math
25} // namespace stan
26#endif
fvar< T > lambert_w0(const fvar< T > &x)
Definition lambert_w.hpp:13
fvar< T > lambert_wm1(const fvar< T > &x)
Definition lambert_w.hpp:19
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:13
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Scalar val_
The value of this variable.
Definition fvar.hpp:49
Scalar d_
The tangent (derivative) of this variable.
Definition fvar.hpp:61
This template class represents scalars used in forward-mode automatic differentiation,...
Definition fvar.hpp:40