// Ceres Solver - A fast non-linear least squares minimizer
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#ifndef CERES_INTERNAL_EIGEN_VECTOR_OPS_H_
#define CERES_INTERNAL_EIGEN_VECTOR_OPS_H_

#include <numeric>

#include "ceres/internal/eigen.h"
#include "ceres/parallel_for.h"
#include "ceres/parallel_vector_ops.h"

namespace ceres::internal {

// Blas1 operations on Eigen vectors. These functions are needed as an
// abstraction layer so that we can use different versions of a vector style
// object in the conjugate gradients linear solver.
template <typename Derived>
inline double Norm(const Eigen::DenseBase<Derived>& x,
                   ContextImpl* context,
                   int num_threads) {
  std::vector<double> norms(num_threads);
  ParallelFor(context,
              0,
              x.rows(),
              num_threads,
              [&x, &norms](int thread_id, std::tuple<int, int> range) {
                auto [start, end] = range;
                norms[thread_id] += x.segment(start, end - start).squaredNorm();
              });
  return std::sqrt(std::accumulate(norms.begin(), norms.end(), 0.));
}
inline void SetZero(Vector& x, ContextImpl* context, int num_threads) {
  ParallelSetZero(context, num_threads, x);
}
inline void Axpby(double a,
                  const Vector& x,
                  double b,
                  const Vector& y,
                  Vector& z,
                  ContextImpl* context,
                  int num_threads) {
  ParallelAssign(context, num_threads, z, a * x + b * y);
}
template <typename VectorLikeX, typename VectorLikeY>
inline double Dot(const VectorLikeX& x,
                  const VectorLikeY& y,
                  ContextImpl* context,
                  int num_threads) {
  std::vector<double> dots(num_threads);
  ParallelFor(context,
              0,
              x.rows(),
              num_threads,
              [&x, &y, &dots](int thread_id, std::tuple<int, int> range) {
                auto [start, end] = range;
                const int block_size = end - start;
                const auto& x_block = x.segment(start, block_size);
                const auto& y_block = y.segment(start, block_size);
                dots[thread_id] += x_block.dot(y_block);
              });
  return std::accumulate(dots.begin(), dots.end(), 0.);
}
inline void Copy(const Vector& from,
                 Vector& to,
                 ContextImpl* context,
                 int num_threads) {
  ParallelAssign(context, num_threads, to, from);
}

}  // namespace ceres::internal

#endif  // CERES_INTERNAL_EIGEN_VECTOR_OPS_H_