Automatic Differentiation
 
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acosh.hpp
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1#ifndef STAN_MATH_FWD_FUN_ACOSH_HPP
2#define STAN_MATH_FWD_FUN_ACOSH_HPP
3
9#include <cmath>
10#include <complex>
11
12namespace stan {
13namespace math {
14
15template <typename T>
16inline fvar<T> acosh(const fvar<T>& x) {
17 using std::sqrt;
18 return fvar<T>(acosh(x.val_), x.d_ / sqrt(square(x.val_) - 1));
19}
20
28template <typename T>
29inline std::complex<fvar<T>> acosh(const std::complex<fvar<T>>& z) {
31}
32
33} // namespace math
34} // namespace stan
35#endif
std::complex< V > complex_acosh(const std::complex< V > &z)
Return the hyperbolic arc cosine of the complex argument.
Definition acosh.hpp:103
fvar< T > acosh(const fvar< T > &x)
Definition acosh.hpp:16
fvar< T > sqrt(const fvar< T > &x)
Definition sqrt.hpp:17
fvar< T > square(const fvar< T > &x)
Definition square.hpp:12
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