read_cov_L.hpp

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#ifndef STAN_MATH_REV_FUN_READ_COV_L_HPP
#define STAN_MATH_REV_FUN_READ_COV_L_HPP
 
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/math/rev/core.hpp>
#include <stan/math/prim/fun/log.hpp>
#include <stan/math/prim/fun/read_cov_L.hpp>
#include <stan/math/rev/fun/read_corr_L.hpp>
#include <stan/math/prim/fun/sum.hpp>
#include <stan/math/prim/fun/constants.hpp>
 
namespace stan {
namespace math {
 
template <typename T_CPCs, typename T_sds,
          require_any_var_vector_t<T_CPCs, T_sds>* = nullptr,
          require_vt_same<T_CPCs, T_sds>* = nullptr>
inline auto read_cov_L(const T_CPCs& CPCs, const T_sds& sds,
                       scalar_type_t<T_CPCs>& log_prob) {
  size_t K = sds.rows();
  // adjust due to transformation from correlations to covariances
  log_prob += (sum(log(sds.val())) + LOG_TWO) * K;
 
  auto corr_L = read_corr_L(CPCs, K, log_prob);
  var_value<Eigen::MatrixXd> res
      = sds.val().matrix().asDiagonal() * corr_L.val();
 
  reverse_pass_callback([sds, corr_L, log_prob, res]() mutable {
    size_t K = sds.size();
 
    corr_L.adj() += sds.val().matrix().asDiagonal() * res.adj();
    sds.adj() += (res.adj().cwiseProduct(corr_L.val())).rowwise().sum();
 
    sds.adj() += (K * log_prob.adj() / sds.val().array()).matrix();
  });
 
  return res;
}
 
}  // namespace math
}  // namespace stan
 
#endif