// Ceres Solver - A fast non-linear least squares minimizer
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// A solver for sparse linear least squares problem based on solving
// the normal equations via a sparse cholesky factorization.

#ifndef CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_
#define CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_

// This include must come before any #ifndef check on Ceres compile options.
// clang-format off
#include "ceres/internal/config.h"
// clang-format on

#include "ceres/internal/export.h"
#include "ceres/linear_solver.h"

namespace ceres::internal {

class CompressedRowSparseMatrix;

// A variant of SparseNormalCholeskySolver in the case where matrix
// sparsity is not constant across calls to Solve. This means that
// there is no benefit to symbolically factorizing the matrix and
// caching this factorization.
//
// TODO(alex): Add support for Accelerate sparse solvers:
// https://github.com/ceres-solver/ceres-solver/issues/397
class CERES_NO_EXPORT DynamicSparseNormalCholeskySolver
    : public CompressedRowSparseMatrixSolver {
 public:
  explicit DynamicSparseNormalCholeskySolver(LinearSolver::Options options);

 private:
  LinearSolver::Summary SolveImpl(CompressedRowSparseMatrix* A,
                                  const double* b,
                                  const LinearSolver::PerSolveOptions& options,
                                  double* x) final;

  LinearSolver::Summary SolveImplUsingSuiteSparse(CompressedRowSparseMatrix* A,
                                                  double* rhs_and_solution);

  LinearSolver::Summary SolveImplUsingEigen(CompressedRowSparseMatrix* A,
                                            double* rhs_and_solution);

  LinearSolver::Summary SolveImplUsingCuda(CompressedRowSparseMatrix* A,
                                            double* rhs_and_solution);

  const LinearSolver::Options options_;
};

}  // namespace ceres::internal

#endif  // CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_