| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
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| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
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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. |
| inline double Norm(const Vector& 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_ |