ceres-solver/internal/ceres/compressed_row_sparse_matrix.h

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

#ifndef CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_
#define CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_

#include <vector>
#include "ceres/internal/macros.h"
#include "ceres/internal/port.h"
#include "ceres/sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"

namespace ceres {

struct CRSMatrix;

namespace internal {

class TripletSparseMatrix;

class CompressedRowSparseMatrix : public SparseMatrix {
 public:
  // Build a matrix with the same content as the TripletSparseMatrix
  // m. TripletSparseMatrix objects are easier to construct
  // incrementally, so we use them to initialize SparseMatrix
  // objects.
  //
  // We assume that m does not have any repeated entries.
  explicit CompressedRowSparseMatrix(const TripletSparseMatrix& m);

  // Use this constructor only if you know what you are doing. This
  // creates a "blank" matrix with the appropriate amount of memory
  // allocated. However, the object itself is in an inconsistent state
  // as the rows and cols matrices do not match the values of
  // num_rows, num_cols and max_num_nonzeros.
  //
  // The use case for this constructor is that when the user knows the
  // size of the matrix to begin with and wants to update the layout
  // manually, instead of going via the indirect route of first
  // constructing a TripletSparseMatrix, which leads to more than
  // double the peak memory usage.
  CompressedRowSparseMatrix(int num_rows,
                            int num_cols,
                            int max_num_nonzeros);

  // Build a square sparse diagonal matrix with num_rows rows and
  // columns. The diagonal m(i,i) = diagonal(i);
  CompressedRowSparseMatrix(const double* diagonal, int num_rows);

  virtual ~CompressedRowSparseMatrix();

  // SparseMatrix interface.
  virtual void SetZero();
  virtual void RightMultiply(const double* x, double* y) const;
  virtual void LeftMultiply(const double* x, double* y) const;
  virtual void SquaredColumnNorm(double* x) const;
  virtual void ScaleColumns(const double* scale);

  virtual void ToDenseMatrix(Matrix* dense_matrix) const;
  virtual void ToTextFile(FILE* file) const;
  virtual int num_rows() const { return num_rows_; }
  virtual int num_cols() const { return num_cols_; }
  virtual int num_nonzeros() const { return rows_[num_rows_]; }
  virtual const double* values() const { return &values_[0]; }
  virtual double* mutable_values() { return &values_[0]; }

  // Delete the bottom delta_rows.
  // num_rows -= delta_rows
  void DeleteRows(int delta_rows);

  // Append the contents of m to the bottom of this matrix. m must
  // have the same number of columns as this matrix.
  void AppendRows(const CompressedRowSparseMatrix& m);

  void ToCRSMatrix(CRSMatrix* matrix) const;

  // Low level access methods that expose the structure of the matrix.
  const int* cols() const { return &cols_[0]; }
  int* mutable_cols() { return &cols_[0]; }

  const int* rows() const { return &rows_[0]; }
  int* mutable_rows() { return &rows_[0]; }

  const std::vector<int>& row_blocks() const { return row_blocks_; }
  std::vector<int>* mutable_row_blocks() { return &row_blocks_; }

  const std::vector<int>& col_blocks() const { return col_blocks_; }
  std::vector<int>* mutable_col_blocks() { return &col_blocks_; }

  const std::vector<int>& block_offsets() const { return block_offsets_; }
  std::vector<int>* mutable_block_offsets() { return &block_offsets_; }

  const std::vector<int>& crsb_rows() const { return crsb_rows_; }
  std::vector<int>* mutable_crsb_rows() { return &crsb_rows_; }

  const std::vector<int>& crsb_cols() const { return crsb_cols_; }
  std::vector<int>* mutable_crsb_cols() { return &crsb_cols_; }

  // Destructive array resizing method.
  void SetMaxNumNonZeros(int num_nonzeros);

  // Non-destructive array resizing method.
  void set_num_rows(const int num_rows) { num_rows_ = num_rows; }
  void set_num_cols(const int num_cols) { num_cols_ = num_cols; }

  void SolveLowerTriangularInPlace(double* solution) const;
  void SolveLowerTriangularTransposeInPlace(double* solution) const;

  CompressedRowSparseMatrix* Transpose() const;

  static CompressedRowSparseMatrix* CreateBlockDiagonalMatrix(
      const double* diagonal,
      const std::vector<int>& blocks);

  // Compute the sparsity structure of the product m.transpose() * m
  // and create a CompressedRowSparseMatrix corresponding to it.
  //
  // Also compute a "program" vector, which for every term in the
  // block outer product provides the information for the entry
  // in the values array of the result matrix where it should be accumulated.
  //
  // This program is used by the ComputeOuterProduct function below to
  // compute the outer product.
  //
  // Since the entries of the program are the same for rows with the
  // same sparsity structure, the program only stores the result for
  // one row per row block. The ComputeOuterProduct function reuses
  // this information for each row in the row block.
  //
  // stype controls the result matrix in upper or lower triangular matrix
  //    stype = 1:  lower triangular matrix
  //    stype = -1: higher triangular matrix
  static CompressedRowSparseMatrix* CreateOuterProductMatrixAndProgram(
      const CompressedRowSparseMatrix& m,
      const int stype,
      std::vector<int>* program);

  // Compute the values array for the expression m.transpose() * m,
  // where the matrix used to store the result and a program have been
  // created using the CreateOuterProductMatrixAndProgram function
  // above.
  static void ComputeOuterProduct(const CompressedRowSparseMatrix& m,
                                  const int stype,
                                  const std::vector<int>& program,
                                  CompressedRowSparseMatrix* result);

 private:
  int num_rows_;
  int num_cols_;
  std::vector<int> rows_;
  std::vector<int> cols_;
  std::vector<double> values_;

  // If the matrix has an underlying block structure, then it can also
  // carry with it row and column block sizes. This is auxilliary and
  // optional information for use by algorithms operating on the
  // matrix. The class itself does not make use of this information in
  // any way.
  std::vector<int> row_blocks_;
  std::vector<int> col_blocks_;

  // For outerproduct matrix (J' * J), we pre-compute its block offsets
  // information here for fast outerproduct computation in block unit.
  // Since the outerproduct matrix is symmetric, we do not need to
  // distinguish row or col block. In another word, this is the prefix
  // sum of row_blocks_/col_blocks_.
  std::vector<int> block_offsets_;

  // If the matrix has an underlying block structure, then it can also
  // carry with it compressed row sparse block information.
  std::vector<int> crsb_rows_;
  std::vector<int> crsb_cols_;

  CERES_DISALLOW_COPY_AND_ASSIGN(CompressedRowSparseMatrix);
};

}  // namespace internal
}  // namespace ceres

#endif  // CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_