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hessian.hpp
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1 #ifndef STAN_MATH_MIX_MAT_FUNCTOR_HESSIAN_HPP
2 #define STAN_MATH_MIX_MAT_FUNCTOR_HESSIAN_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 namespace math {
12
42 template <typename F>
43 void
44 hessian(const F& f,
45 const Eigen::Matrix<double, Eigen::Dynamic, 1>& x,
46 double& fx,
47 Eigen::Matrix<double, Eigen::Dynamic, 1>& grad,
48 Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>& H) {
49 H.resize(x.size(), x.size());
50 grad.resize(x.size());
51 try {
52 for (int i = 0; i < x.size(); ++i) {
53 start_nested();
54 Eigen::Matrix<fvar<var>, Eigen::Dynamic, 1> x_fvar(x.size());
55 for (int j = 0; j < x.size(); ++j)
56 x_fvar(j) = fvar<var>(x(j), i == j);
57 fvar<var> fx_fvar = f(x_fvar);
58 grad(i) = fx_fvar.d_.val();
59 if (i == 0) fx = fx_fvar.val_.val();
60 stan::math::grad(fx_fvar.d_.vi_);
61 for (int j = 0; j < x.size(); ++j)
62 H(i, j) = x_fvar(j).val_.adj();
63 recover_memory_nested();
64 }
65 } catch (const std::exception& e) {
66 recover_memory_nested();
67 throw;
68 }
69 }
70 // time O(N^3); space O(N^2)
71 template <typename T, typename F>
72 void
73 hessian(const F& f,
74 const Eigen::Matrix<T, Eigen::Dynamic, 1>& x,
75 T& fx,
76 Eigen::Matrix<T, Eigen::Dynamic, 1>& grad,
77 Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>& H) {
78 H.resize(x.size(), x.size());
79 grad.resize(x.size());
80 Eigen::Matrix<fvar<fvar<T> >, Eigen::Dynamic, 1> x_fvar(x.size());
81 for (int i = 0; i < x.size(); ++i) {
82 for (int j = i; j < x.size(); ++j) {
83 for (int k = 0; k < x.size(); ++k)
84 x_fvar(k) = fvar<fvar<T> >(fvar<T>(x(k), j == k),
85 fvar<T>(i == k, 0));
86 fvar<fvar<T> > fx_fvar = f(x_fvar);
87 if (j == 0)
88 fx = fx_fvar.val_.val_;
89 if (i == j)
90 grad(i) = fx_fvar.d_.val_;
91 H(i, j) = fx_fvar.d_.d_;
92 H(j, i) = H(i, j);
93 }
94 }
95 }
96
97 }
98 }
99 #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:44
core.hpp
core.hpp
stan::math::fvar::d_
T d_
Definition: fvar.hpp:14
stan
Definition: log_sum_exp.hpp:8
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::fvar::val_
T val_
Definition: fvar.hpp:13
stan::math::var::vi_
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:42
Eigen.hpp
stan::math::e
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:94
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:229
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:12

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