1 #ifndef STAN_MATH_REV_SCAL_FUN_POW_HPP 2 #define STAN_MATH_REV_SCAL_FUN_POW_HPP 25 avi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
26 bvi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
42 avi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
57 bvi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
128 if (exponent == -2.0)
130 if (exponent == -1.0)
132 if (exponent == -0.5)
fvar< T > inv_sqrt(const fvar< T > &x)
fvar< T > sqrt(const fvar< T > &x)
fvar< T > log(const fvar< T > &x)
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
The variable implementation base class.
bool is_any_nan(const T &x)
Returns true if the input is NaN and false otherwise.
Independent (input) and dependent (output) variables for gradients.
const double val_
The value of this variable.
fvar< T > square(const fvar< T > &x)
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
pow_dv_vari(double a, vari *bvi)
vari * vi_
Pointer to the implementation of this variable.
pow_vd_vari(vari *avi, double b)
fvar< T > inv_square(const fvar< T > &x)
double adj_
The adjoint of this variable, which is the partial derivative of this variable with respect to the ro...
fvar< T > pow(const fvar< T > &x1, const fvar< T > &x2)
pow_vv_vari(vari *avi, vari *bvi)
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
fvar< T > inv(const fvar< T > &x)