Automatic Differentiation
 
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mdivide_left_tri_low.hpp
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1#ifndef STAN_MATH_FWD_FUN_MDIVIDE_LEFT_TRI_LOW_HPP
2#define STAN_MATH_FWD_FUN_MDIVIDE_LEFT_TRI_LOW_HPP
3
12
13namespace stan {
14namespace math {
15
16template <typename T1, typename T2,
17 require_all_eigen_vt<is_fvar, T1, T2>* = nullptr,
18 require_vt_same<T1, T2>* = nullptr>
19inline Eigen::Matrix<value_type_t<T1>, T1::RowsAtCompileTime,
20 T2::ColsAtCompileTime>
21mdivide_left_tri_low(const T1& A, const T2& b) {
22 using T = typename value_type_t<T1>::Scalar;
23 constexpr int S1 = T1::RowsAtCompileTime;
24 constexpr int C2 = T2::ColsAtCompileTime;
25
26 check_square("mdivide_left_tri_low", "A", A);
27 check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
28 if (A.size() == 0) {
29 return {0, b.cols()};
30 }
31
32 Eigen::Matrix<T, S1, S1> val_A(A.rows(), A.cols());
33 Eigen::Matrix<T, S1, S1> deriv_A(A.rows(), A.cols());
34 val_A.setZero();
35 deriv_A.setZero();
36
37 const Eigen::Ref<const plain_type_t<T2>>& b_ref = b;
38 const Eigen::Ref<const plain_type_t<T1>>& A_ref = A;
39 for (size_type j = 0; j < A.cols(); j++) {
40 for (size_type i = j; i < A.rows(); i++) {
41 val_A(i, j) = A_ref(i, j).val_;
42 deriv_A(i, j) = A_ref(i, j).d_;
43 }
44 }
45
46 Eigen::Matrix<T, S1, C2> inv_A_mult_b = mdivide_left(val_A, b_ref.val());
47
48 return to_fvar(inv_A_mult_b,
49 mdivide_left(val_A, b_ref.d())
50 - multiply(mdivide_left(val_A, deriv_A), inv_A_mult_b));
51}
52
53template <typename T1, typename T2, require_eigen_t<T1>* = nullptr,
54 require_vt_same<double, T1>* = nullptr,
55 require_eigen_vt<is_fvar, T2>* = nullptr>
56inline Eigen::Matrix<value_type_t<T2>, T1::RowsAtCompileTime,
57 T2::ColsAtCompileTime>
58mdivide_left_tri_low(const T1& A, const T2& b) {
59 constexpr int S1 = T1::RowsAtCompileTime;
60
61 check_square("mdivide_left_tri_low", "A", A);
62 check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
63 if (A.size() == 0) {
64 return {0, b.cols()};
65 }
66
67 Eigen::Matrix<double, S1, S1> val_A(A.rows(), A.cols());
68 val_A.setZero();
69
70 const Eigen::Ref<const plain_type_t<T2>>& b_ref = b;
71 const Eigen::Ref<const plain_type_t<T1>>& A_ref = A;
72 for (size_type j = 0; j < A.cols(); j++) {
73 for (size_type i = j; i < A.rows(); i++) {
74 val_A(i, j) = A_ref(i, j);
75 }
76 }
77
78 return to_fvar(mdivide_left(val_A, b_ref.val()),
79 mdivide_left(val_A, b_ref.d()));
80}
81
82template <typename T1, typename T2, require_eigen_vt<is_fvar, T1>* = nullptr,
83 require_eigen_t<T2>* = nullptr,
84 require_vt_same<double, T2>* = nullptr>
85inline Eigen::Matrix<value_type_t<T1>, T1::RowsAtCompileTime,
86 T2::ColsAtCompileTime>
87mdivide_left_tri_low(const T1& A, const T2& b) {
88 using T = typename value_type_t<T1>::Scalar;
89 constexpr int S1 = T1::RowsAtCompileTime;
90 constexpr int C2 = T2::ColsAtCompileTime;
91
92 check_square("mdivide_left_tri_low", "A", A);
93 check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
94 if (A.size() == 0) {
95 return {0, b.cols()};
96 }
97
98 Eigen::Matrix<T, S1, S1> val_A(A.rows(), A.cols());
99 Eigen::Matrix<T, S1, S1> deriv_A(A.rows(), A.cols());
100 val_A.setZero();
101 deriv_A.setZero();
102
103 const Eigen::Ref<const plain_type_t<T1>>& A_ref = A;
104 for (size_type j = 0; j < A.cols(); j++) {
105 for (size_type i = j; i < A.rows(); i++) {
106 val_A(i, j) = A_ref(i, j).val_;
107 deriv_A(i, j) = A_ref(i, j).d_;
108 }
109 }
110
111 Eigen::Matrix<T, S1, C2> inv_A_mult_b = mdivide_left(val_A, b);
112
113 return to_fvar(inv_A_mult_b,
114 -multiply(mdivide_left(val_A, deriv_A), inv_A_mult_b));
115}
116
117} // namespace math
118} // namespace stan
119#endif
typename value_type< T >::type value_type_t
Helper function for accessing underlying type.
void check_square(const char *function, const char *name, const T_y &y)
Check if the specified matrix is square.
void check_multiplicable(const char *function, const char *name1, const T1 &y1, const char *name2, const T2 &y2)
Check if the matrices can be multiplied.
auto multiply(const Mat1 &m1, const Mat2 &m2)
Return the product of the specified matrices.
Definition multiply.hpp:19
Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic >::Index size_type
Type for sizes and indexes in an Eigen matrix with double elements.
Definition typedefs.hpp:11
fvar< T > to_fvar(const T &x)
Definition to_fvar.hpp:15
Eigen::Matrix< value_type_t< T1 >, T1::RowsAtCompileTime, T2::ColsAtCompileTime > mdivide_left(const T1 &A, const T2 &b)
Eigen::Matrix< value_type_t< T1 >, T1::RowsAtCompileTime, T2::ColsAtCompileTime > mdivide_left_tri_low(const T1 &A, const T2 &b)
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...