internal/ceres/cxsparse.h

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: strandmark@google.com (Petter Strandmark)

#ifndef CERES_INTERNAL_CXSPARSE_H_
#define CERES_INTERNAL_CXSPARSE_H_

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

#ifndef CERES_NO_CXSPARSE

#include <memory>
#include <string>
#include <vector>

#include "ceres/internal/disable_warnings.h"
#include "ceres/linear_solver.h"
#include "ceres/sparse_cholesky.h"
#include "cs.h"

namespace ceres::internal {

class CompressedRowSparseMatrix;
class TripletSparseMatrix;

// This object provides access to solving linear systems using Cholesky
// factorization with a known symbolic factorization. This features does not
// explicitly exist in CXSparse. The methods in the class are nonstatic because
// the class manages internal scratch space.
class CERES_NO_EXPORT CXSparse {
 public:
  CXSparse();
  ~CXSparse();

  // Solve the system lhs * solution = rhs in place by using an
  // approximate minimum degree fill reducing ordering.
  bool SolveCholesky(cs_di* lhs, double* rhs_and_solution);

  // Solves a linear system given its symbolic and numeric factorization.
  void Solve(cs_dis* symbolic_factor,
             csn* numeric_factor,
             double* rhs_and_solution);

  // Compute the numeric Cholesky factorization of A, given its
  // symbolic factorization.
  //
  // Caller owns the result.
  csn* Cholesky(cs_di* A, cs_dis* symbolic_factor);

  // Creates a sparse matrix from a compressed-column form. No memory is
  // allocated or copied; the structure A is filled out with info from the
  // argument.
  cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);

  // Creates a new matrix from a triplet form. Deallocate the returned matrix
  // with Free. May return nullptr if the compression or allocation fails.
  cs_di* CreateSparseMatrix(TripletSparseMatrix* A);

  // B = A'
  //
  // The returned matrix should be deallocated with Free when not used
  // anymore.
  cs_di* TransposeMatrix(cs_di* A);

  // C = A * B
  //
  // The returned matrix should be deallocated with Free when not used
  // anymore.
  cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);

  // Computes a symbolic factorization of A that can be used in SolveCholesky.
  //
  // The returned matrix should be deallocated with Free when not used anymore.
  cs_dis* AnalyzeCholesky(cs_di* A);

  // Computes a symbolic factorization of A that can be used in
  // SolveCholesky, but does not compute a fill-reducing ordering.
  //
  // The returned matrix should be deallocated with Free when not used anymore.
  cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);

  // Computes a symbolic factorization of A that can be used in
  // SolveCholesky. The difference from AnalyzeCholesky is that this
  // function first detects the block sparsity of the matrix using
  // information about the row and column blocks and uses this block
  // sparse matrix to find a fill-reducing ordering. This ordering is
  // then used to find a symbolic factorization. This can result in a
  // significant performance improvement AnalyzeCholesky on block
  // sparse matrices.
  //
  // The returned matrix should be deallocated with Free when not used
  // anymore.
  cs_dis* BlockAnalyzeCholesky(cs_di* A,
                               const std::vector<int>& row_blocks,
                               const std::vector<int>& col_blocks);

  // Compute an fill-reducing approximate minimum degree ordering of
  // the matrix A. ordering should be non-nullptr and should point to
  // enough memory to hold the ordering for the rows of A.
  void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);

  void Free(cs_di* sparse_matrix);
  void Free(cs_dis* symbolic_factorization);
  void Free(csn* numeric_factorization);

 private:
  // Cached scratch space
  CS_ENTRY* scratch_;
  int scratch_size_;
};

// An implementation of SparseCholesky interface using the CXSparse
// library.
class CERES_NO_EXPORT CXSparseCholesky final : public SparseCholesky {
 public:
  // Factory
  static std::unique_ptr<SparseCholesky> Create(OrderingType ordering_type);

  // SparseCholesky interface.
  ~CXSparseCholesky() override;
  CompressedRowSparseMatrix::StorageType StorageType() const final;
  LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
                                        std::string* message) final;
  LinearSolverTerminationType Solve(const double* rhs,
                                    double* solution,
                                    std::string* message) final;

 private:
  explicit CXSparseCholesky(const OrderingType ordering_type);
  void FreeSymbolicFactorization();
  void FreeNumericFactorization();

  const OrderingType ordering_type_;
  CXSparse cs_;
  cs_dis* symbolic_factor_;
  csn* numeric_factor_;
};

}  // namespace ceres::internal

#include "ceres/internal/reenable_warnings.h"

#else

typedef void cs_dis;

class CXSparse {
 public:
  void Free(void* arg) {}
};
#endif  // CERES_NO_CXSPARSE

#endif  // CERES_INTERNAL_CXSPARSE_H_