// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2023 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 // used to endorse or promote products derived from this software without // 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 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: keir@google.com (Keir Mierle) #include "ceres/dense_sparse_matrix.h" #include #include #include "ceres/internal/eigen.h" #include "ceres/internal/export.h" #include "ceres/triplet_sparse_matrix.h" #include "glog/logging.h" namespace ceres::internal { DenseSparseMatrix::DenseSparseMatrix(int num_rows, int num_cols) : m_(Matrix(num_rows, num_cols)) {} DenseSparseMatrix::DenseSparseMatrix(const TripletSparseMatrix& m) : m_(Matrix::Zero(m.num_rows(), m.num_cols())) { const double* values = m.values(); const int* rows = m.rows(); const int* cols = m.cols(); int num_nonzeros = m.num_nonzeros(); for (int i = 0; i < num_nonzeros; ++i) { m_(rows[i], cols[i]) += values[i]; } } DenseSparseMatrix::DenseSparseMatrix(Matrix m) : m_(std::move(m)) {} void DenseSparseMatrix::SetZero() { m_.setZero(); } void DenseSparseMatrix::RightMultiplyAndAccumulate(const double* x, double* y) const { VectorRef(y, num_rows()).noalias() += m_ * ConstVectorRef(x, num_cols()); } void DenseSparseMatrix::LeftMultiplyAndAccumulate(const double* x, double* y) const { VectorRef(y, num_cols()).noalias() += m_.transpose() * ConstVectorRef(x, num_rows()); } void DenseSparseMatrix::SquaredColumnNorm(double* x) const { // This implementation is 3x faster than the naive version // x = m_.colwise().square().sum(), likely because m_ // is a row major matrix. const int num_rows = m_.rows(); const int num_cols = m_.cols(); std::fill_n(x, num_cols, 0.0); const double* m = m_.data(); for (int i = 0; i < num_rows; ++i) { for (int j = 0; j < num_cols; ++j, ++m) { x[j] += (*m) * (*m); } } } void DenseSparseMatrix::ScaleColumns(const double* scale) { m_ *= ConstVectorRef(scale, num_cols()).asDiagonal(); } void DenseSparseMatrix::ToDenseMatrix(Matrix* dense_matrix) const { *dense_matrix = m_; } int DenseSparseMatrix::num_rows() const { return m_.rows(); } int DenseSparseMatrix::num_cols() const { return m_.cols(); } int DenseSparseMatrix::num_nonzeros() const { return m_.rows() * m_.cols(); } const Matrix& DenseSparseMatrix::matrix() const { return m_; } Matrix* DenseSparseMatrix::mutable_matrix() { return &m_; } void DenseSparseMatrix::ToTextFile(FILE* file) const { CHECK(file != nullptr); for (int r = 0; r < m_.rows(); ++r) { for (int c = 0; c < m_.cols(); ++c) { fprintf(file, "% 10d % 10d %17f\n", r, c, m_(r, c)); } } } } // namespace ceres::internal