1 #ifndef STAN_MATH_FWD_MAT_FUN_MDIVIDE_RIGHT_TRI_LOW_HPP 2 #define STAN_MATH_FWD_MAT_FUN_MDIVIDE_RIGHT_TRI_LOW_HPP 15 template <
typename T,
int R1,
int C1,
int R2,
int C2>
17 const Eigen::Matrix<
fvar<T>, R1, C1> &A,
18 const Eigen::Matrix<
fvar<T>, R2, C2> &b) {
22 Eigen::Matrix<T, R1, C2> A_mult_inv_b(A.rows(), b.cols());
23 Eigen::Matrix<T, R1, C2> deriv_A_mult_inv_b(A.rows(), b.cols());
24 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
25 Eigen::Matrix<T, R1, C1> val_A(A.rows(), A.cols());
26 Eigen::Matrix<T, R1, C1> deriv_A(A.rows(), A.cols());
27 Eigen::Matrix<T, R2, C2> val_b(b.rows(), b.cols());
28 Eigen::Matrix<T, R2, C2> deriv_b(b.rows(), b.cols());
32 for (
size_type j = 0; j < A.cols(); j++) {
33 for (
size_type i = 0; i < A.rows(); i++) {
34 val_A(i, j) = A(i, j).val_;
35 deriv_A(i, j) = A(i, j).d_;
39 for (
size_type j = 0; j < b.cols(); j++) {
40 for (
size_type i = j; i < b.rows(); i++) {
41 val_b(i, j) = b(i, j).val_;
42 deriv_b(i, j) = b(i, j).d_;
50 Eigen::Matrix<T, R1, C2> deriv(A.rows(), b.cols());
51 deriv = deriv_A_mult_inv_b -
multiply(A_mult_inv_b, deriv_b_mult_inv_b);
53 return to_fvar(A_mult_inv_b, deriv);
56 template <
typename T,
int R1,
int C1,
int R2,
int C2>
58 const Eigen::Matrix<
fvar<T>, R1, C1> &A,
59 const Eigen::Matrix<double, R2, C2> &b) {
63 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
64 Eigen::Matrix<T, R1, C1> val_A(A.rows(), A.cols());
65 Eigen::Matrix<T, R1, C1> deriv_A(A.rows(), A.cols());
66 Eigen::Matrix<T, R2, C2> val_b(b.rows(), b.cols());
69 for (
int j = 0; j < A.cols(); j++) {
70 for (
int i = 0; i < A.rows(); i++) {
71 val_A(i, j) = A(i, j).val_;
72 deriv_A(i, j) = A(i, j).d_;
76 for (
size_type j = 0; j < b.cols(); j++) {
77 for (
size_type i = j; i < b.rows(); i++) {
78 val_b(i, j) = b(i, j);
85 template <
typename T,
int R1,
int C1,
int R2,
int C2>
87 const Eigen::Matrix<double, R1, C1> &A,
88 const Eigen::Matrix<
fvar<T>, R2, C2> &b) {
92 Eigen::Matrix<T, R1, C2> A_mult_inv_b(A.rows(), b.cols());
93 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
94 Eigen::Matrix<T, R2, C2> val_b(b.rows(), b.cols());
95 Eigen::Matrix<T, R2, C2> deriv_b(b.rows(), b.cols());
99 for (
int j = 0; j < b.cols(); j++) {
100 for (
int i = j; i < b.rows(); i++) {
101 val_b(i, j) = b(i, j).val_;
102 deriv_b(i, j) = b(i, j).d_;
109 Eigen::Matrix<T, R1, C2> deriv(A.rows(), b.cols());
110 deriv = -
multiply(A_mult_inv_b, deriv_b_mult_inv_b);
112 return to_fvar(A_mult_inv_b, deriv);
void check_square(const char *function, const char *name, const matrix_cl &y)
Check if the matrix_cl is square.
Eigen::Matrix< fvar< T >, R1, C1 > multiply(const Eigen::Matrix< fvar< T >, R1, C1 > &m, const fvar< T > &c)
std::vector< fvar< T > > to_fvar(const std::vector< T > &v)
Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic >::Index size_type
Type for sizes and indexes in an Eigen matrix with double e.
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.
Eigen::Matrix< fvar< T >, R1, C2 > mdivide_right(const Eigen::Matrix< fvar< T >, R1, C1 > &A, const Eigen::Matrix< fvar< T >, R2, C2 > &b)
This template class represents scalars used in forward-mode automatic differentiation, which consist of values and directional derivatives of the specified template type.
Eigen::Matrix< fvar< T >, R1, C1 > mdivide_right_tri_low(const Eigen::Matrix< fvar< T >, R1, C1 > &A, const Eigen::Matrix< fvar< T >, R2, C2 > &b)