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hessian_times_vector.hpp
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1 #ifndef STAN_MATH_MIX_MAT_FUNCTOR_HESSIAN_TIMES_VECTOR_HPP
2 #define STAN_MATH_MIX_MAT_FUNCTOR_HESSIAN_TIMES_VECTOR_HPP
3
4 #include <stan/math/fwd/core.hpp>
5 #include <stan/math/prim/mat/fun/Eigen.hpp>
6 #include <stan/math/rev/core.hpp>
7 #include <stdexcept>
8 #include <vector>
9
10 namespace stan {
11
12 namespace math {
13
14 template <typename F>
15 void
16 hessian_times_vector(const F& f,
17 const Eigen::Matrix<double, Eigen::Dynamic, 1>& x,
18 const Eigen::Matrix<double, Eigen::Dynamic, 1>& v,
19 double& fx,
20 Eigen::Matrix<double, Eigen::Dynamic, 1>& Hv) {
21 using stan::math::fvar;
22 using stan::math::var;
23 using Eigen::Matrix;
24 start_nested();
25 try {
26 Matrix<var, Eigen::Dynamic, 1> x_var(x.size());
27 for (int i = 0; i < x_var.size(); ++i)
28 x_var(i) = x(i);
29 var fx_var;
30 var grad_fx_var_dot_v;
31 gradient_dot_vector(f, x_var, v, fx_var, grad_fx_var_dot_v);
32 fx = fx_var.val();
33 stan::math::grad(grad_fx_var_dot_v.vi_);
34 Hv.resize(x.size());
35 for (int i = 0; i < x.size(); ++i)
36 Hv(i) = x_var(i).adj();
37 } catch (const std::exception& e) {
38 stan::math::recover_memory_nested();
39 throw;
40 }
41 stan::math::recover_memory_nested();
42 }
43 template <typename T, typename F>
44 void
45 hessian_times_vector(const F& f,
46 const Eigen::Matrix<T, Eigen::Dynamic, 1>& x,
47 const Eigen::Matrix<T, Eigen::Dynamic, 1>& v,
48 T& fx,
49 Eigen::Matrix<T, Eigen::Dynamic, 1>& Hv) {
50 using Eigen::Matrix;
51 Matrix<T, Eigen::Dynamic, 1> grad;
52 Matrix<T, Eigen::Dynamic, Eigen::Dynamic> H;
53 hessian(f, x, fx, grad, H);
54 Hv = H * v;
55 }
56
57 } // namespace math
58 } // namespace stan
59 #endif
stan::math::hessian
void hessian(const F &f, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &fx, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad, Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &H)
Calculate the value, the gradient, and the Hessian, of the specified function at the specified argume...
Definition: hessian.hpp:45
core.hpp
core.hpp
stan
Definition: log_sum_exp.hpp:8
stan::math::var
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:31
stan::math::hessian_times_vector
void hessian_times_vector(const F &f, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &v, double &fx, Eigen::Matrix< double, Eigen::Dynamic, 1 > &Hv)
Definition: hessian_times_vector.hpp:16
stan::math::grad
static void grad(vari *vi)
Compute the gradient for all variables starting from the specified root variable implementation.
Definition: grad.hpp:30
stan::math::var::vi_
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:43
Eigen.hpp
stan::math::e
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:95
stan::math::recover_memory_nested
static void recover_memory_nested()
Recover only the memory used for the top nested call.
Definition: recover_memory_nested.hpp:20
stan::math::var::val
double val() const
Return the value of this variable.
Definition: var.hpp:233
stan::math::gradient_dot_vector
void gradient_dot_vector(const F &f, const Eigen::Matrix< T1, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< T2, Eigen::Dynamic, 1 > &v, T1 &fx, T1 &grad_fx_dot_v)
Definition: gradient_dot_vector.hpp:17
stan::math::start_nested
static void start_nested()
Record the current position so that recover_memory_nested() can find it.
Definition: start_nested.hpp:13
stan::math::fvar
Definition: fvar.hpp:13

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