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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 <boost/math/special_functions/fpclassify.hpp>
5 #include <stan/math/prim/scal/err/domain_error.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
12 namespace math {
13
14 template <int R, int C>
15 inline var log_determinant_spd(const Eigen::Matrix<var, R, C>& m) {
16 using stan::math::domain_error;
17 using Eigen::Matrix;
18
19 math::check_square("log_determinant_spd", "m", m);
20
21 Matrix<double, R, C> m_d(m.rows(), m.cols());
22 for (int i = 0; i < m.size(); ++i)
23 m_d(i) = m(i).val();
24
25 Eigen::LDLT<Matrix<double, R, C> > ldlt(m_d);
26 if (ldlt.info() != Eigen::Success) {
27 double y = 0;
28 domain_error("log_determinant_spd",
29 "matrix argument", y,
30 "failed LDLT factorization");
31 }
32
33 // compute the inverse of A (needed for the derivative)
34 m_d.setIdentity(m.rows(), m.cols());
35 ldlt.solveInPlace(m_d);
36
37 if (ldlt.isNegative() || (ldlt.vectorD().array() <= 1e-16).any()) {
38 double y = 0;
39 domain_error("log_determinant_spd",
40 "matrix argument", y,
41 "matrix is negative definite");
42 }
43
44 double val = ldlt.vectorD().array().log().sum();
45
46 if (!boost::math::isfinite(val)) {
47 double y = 0;
48 domain_error("log_determinant_spd",
49 "matrix argument", y,
50 "log determininant is infinite");
51 }
52
53 vari** operands = ChainableStack::memalloc_
54 .alloc_array<vari*>(m.size());
55 for (int i = 0; i < m.size(); ++i)
56 operands[i] = m(i).vi_;
57
58 double* gradients = ChainableStack::memalloc_
59 .alloc_array<double>(m.size());
60 for (int i = 0; i < m.size(); ++i)
61 gradients[i] = m_d(i);
62
63 return var(new precomputed_gradients_vari(val, m.size(),
64 operands, gradients));
65 }
66
67
68 }
69
70 }
71 #endif
core.hpp
boost::math::isfinite
bool isfinite(const stan::math::var &v)
Checks if the given number has finite value.
Definition: boost_isfinite.hpp:22
stan::math::AutodiffStackStorage::memalloc_
static stack_alloc memalloc_
Definition: autodiffstackstorage.hpp:16
stan::math::vari
The variable implementation base class.
Definition: vari.hpp:28
stan::math::var
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:32
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:21
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:32
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:190
stan::math::e
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:95
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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