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log_determinant_spd.hpp
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1 #ifndef STAN_MATH_REV_MAT_FUN_LOG_DETERMINANT_SPD_HPP
2 #define STAN_MATH_REV_MAT_FUN_LOG_DETERMINANT_SPD_HPP
3
4 #include <stan/math/prim/scal/err/domain_error.hpp>
5 #include <stan/math/prim/scal/err/check_finite.hpp>
6 #include <stan/math/prim/mat/err/check_square.hpp>
7 #include <stan/math/prim/mat/fun/Eigen.hpp>
8 #include <stan/math/rev/core.hpp>
9
10 namespace stan {
11 namespace math {
12
13 template <int R, int C>
14 inline var log_determinant_spd(const Eigen::Matrix<var, R, C>& m) {
15 using Eigen::Matrix;
16
17 check_square("log_determinant_spd", "m", m);
18
19 Matrix<double, R, C> m_d(m.rows(), m.cols());
20 for (int i = 0; i < m.size(); ++i)
21 m_d(i) = m(i).val();
22
23 Eigen::LDLT<Matrix<double, R, C> > ldlt(m_d);
24 if (ldlt.info() != Eigen::Success) {
25 double y = 0;
26 domain_error("log_determinant_spd",
27 "matrix argument", y,
28 "failed LDLT factorization");
29 }
30
31 // compute the inverse of A (needed for the derivative)
32 m_d.setIdentity(m.rows(), m.cols());
33 ldlt.solveInPlace(m_d);
34
35 if (ldlt.isNegative() || (ldlt.vectorD().array() <= 1e-16).any()) {
36 double y = 0;
37 domain_error("log_determinant_spd",
38 "matrix argument", y,
39 "matrix is negative definite");
40 }
41
42 double val = ldlt.vectorD().array().log().sum();
43
44 check_finite("log_determinant_spd",
45 "log determininant of the matrix argument", val);
46
47 vari** operands = ChainableStack::memalloc_
48 .alloc_array<vari*>(m.size());
49 for (int i = 0; i < m.size(); ++i)
50 operands[i] = m(i).vi_;
51
52 double* gradients = ChainableStack::memalloc_
53 .alloc_array<double>(m.size());
54 for (int i = 0; i < m.size(); ++i)
55 gradients[i] = m_d(i);
56
57 return var(new precomputed_gradients_vari(val, m.size(),
58 operands, gradients));
59 }
60
61 }
62
63 }
64 #endif
core.hpp
stan
Definition: log_sum_exp.hpp:8
stan::math::AutodiffStackStorage::memalloc_
static stack_alloc memalloc_
Definition: autodiffstackstorage.hpp:16
stan::math::vari
The variable implementation base class.
Definition: vari.hpp:30
stan::math::var
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:30
stan::math::precomputed_gradients_vari
A variable implementation taking a sequence of operands and partial derivatives with respect to the o...
Definition: precomputed_gradients.hpp:20
stan::math::domain_error
void domain_error(const char *function, const char *name, const T &y, const char *msg1, const char *msg2)
Throw a domain error with a consistently formatted message.
Definition: domain_error.hpp:30
Eigen.hpp
stan::math::stack_alloc::alloc_array
T * alloc_array(size_t n)
Allocate an array on the arena of the specified size to hold values of the specified template paramet...
Definition: stack_alloc.hpp:182
stan::math::e
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:94
stan::math::check_finite
bool check_finite(const char *function, const char *name, const T_y &y)
Return true if y is finite.
Definition: check_finite.hpp:60
check_finite.hpp
domain_error.hpp
stan::math::check_square
bool check_square(const char *function, const char *name, const Eigen::Matrix< T_y, Eigen::Dynamic, Eigen::Dynamic > &y)
Return true if the specified matrix is square.
Definition: check_square.hpp:28
stan::math::log_determinant_spd
T log_determinant_spd(const Eigen::Matrix< T, R, C > &m)
Returns the log absolute determinant of the specified square matrix.
Definition: log_determinant_spd.hpp:19
check_square.hpp

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