internal/ceres/dense_normal_cholesky_solver.cc

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

#include "ceres/dense_normal_cholesky_solver.h"

#include <utility>

#include "Eigen/Dense"
#include "ceres/dense_sparse_matrix.h"
#include "ceres/event_logger.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_solver.h"
#include "ceres/types.h"

namespace ceres::internal {

DenseNormalCholeskySolver::DenseNormalCholeskySolver(
    LinearSolver::Options options)
    : options_(std::move(options)),
      cholesky_(DenseCholesky::Create(options_)) {}

LinearSolver::Summary DenseNormalCholeskySolver::SolveImpl(
    DenseSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double* x) {
  EventLogger event_logger("DenseNormalCholeskySolver::Solve");

  const int num_rows = A->num_rows();
  const int num_cols = A->num_cols();

  Matrix lhs(num_cols, num_cols);
  lhs.setZero();

  event_logger.AddEvent("Setup");

  //   lhs += A'A
  //
  // Using rankUpdate instead of GEMM, exposes the fact that its the
  // same matrix being multiplied with itself and that the product is
  // symmetric.
  lhs.selfadjointView<Eigen::Upper>().rankUpdate(A->matrix().transpose());

  //   rhs = A'b
  Vector rhs = A->matrix().transpose() * ConstVectorRef(b, num_rows);

  if (per_solve_options.D != nullptr) {
    ConstVectorRef D(per_solve_options.D, num_cols);
    lhs += D.array().square().matrix().asDiagonal();
  }
  event_logger.AddEvent("Product");

  LinearSolver::Summary summary;
  summary.num_iterations = 1;
  summary.termination_type = cholesky_->FactorAndSolve(
      num_cols, lhs.data(), rhs.data(), x, &summary.message);
  event_logger.AddEvent("FactorAndSolve");

  return summary;
}

}  // namespace ceres::internal