1 #ifndef STAN_MATH_FWD_MAT_FUN_MDIVIDE_RIGHT_HPP 2 #define STAN_MATH_FWD_MAT_FUN_MDIVIDE_RIGHT_HPP 18 template <
typename T,
int R1,
int C1,
int R2,
int C2>
20 const Eigen::Matrix<
fvar<T>, R1, C1> &A,
21 const Eigen::Matrix<
fvar<T>, R2, C2> &b) {
25 Eigen::Matrix<T, R1, C2> A_mult_inv_b(A.rows(), b.cols());
26 Eigen::Matrix<T, R1, C2> deriv_A_mult_inv_b(A.rows(), b.cols());
27 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
28 Eigen::Matrix<T, R1, C1> val_A(A.rows(), A.cols());
29 Eigen::Matrix<T, R1, C1> deriv_A(A.rows(), A.cols());
30 Eigen::Matrix<T, R2, C2> val_b(b.rows(), b.cols());
31 Eigen::Matrix<T, R2, C2> deriv_b(b.rows(), b.cols());
33 for (
int j = 0; j < A.cols(); j++) {
34 for (
int i = 0; i < A.rows(); i++) {
35 val_A(i, j) = A(i, j).val_;
36 deriv_A(i, j) = A(i, j).d_;
40 for (
int j = 0; j < b.cols(); j++) {
41 for (
int i = 0; i < b.rows(); i++) {
42 val_b(i, j) = b(i, j).val_;
43 deriv_b(i, j) = b(i, j).d_;
51 Eigen::Matrix<T, R1, C2> deriv(A.rows(), b.cols());
52 deriv = deriv_A_mult_inv_b -
multiply(A_mult_inv_b, deriv_b_mult_inv_b);
54 return to_fvar(A_mult_inv_b, deriv);
57 template <
typename T,
int R1,
int C1,
int R2,
int C2>
59 const Eigen::Matrix<
fvar<T>, R1, C1> &A,
60 const Eigen::Matrix<double, R2, C2> &b) {
64 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
65 Eigen::Matrix<T, R1, C1> val_A(A.rows(), A.cols());
66 Eigen::Matrix<T, R1, C1> deriv_A(A.rows(), A.cols());
68 for (
int j = 0; j < A.cols(); j++) {
69 for (
int i = 0; i < A.rows(); i++) {
70 val_A(i, j) = A(i, j).val_;
71 deriv_A(i, j) = A(i, j).d_;
78 template <
typename T,
int R1,
int C1,
int R2,
int C2>
80 const Eigen::Matrix<double, R1, C1> &A,
81 const Eigen::Matrix<
fvar<T>, R2, C2> &b) {
84 Eigen::Matrix<T, R1, C2> A_mult_inv_b(A.rows(), b.cols());
85 Eigen::Matrix<T, R2, C2> deriv_b_mult_inv_b(b.rows(), b.cols());
86 Eigen::Matrix<T, R2, C2> val_b(b.rows(), b.cols());
87 Eigen::Matrix<T, R2, C2> deriv_b(b.rows(), b.cols());
89 for (
int j = 0; j < b.cols(); j++) {
90 for (
int i = 0; i < b.rows(); i++) {
91 val_b(i, j) = b(i, j).val_;
92 deriv_b(i, j) = b(i, j).d_;
99 Eigen::Matrix<T, R1, C2> deriv(A.rows(), b.cols());
100 deriv = -
multiply(A_mult_inv_b, deriv_b_mult_inv_b);
102 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)
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.