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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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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 <memory>
#include <random>
#include <vector>
#include "ceres/block_structure.h"
#include "ceres/internal/disable_warnings.h"
#include "ceres/internal/export.h"
#include "ceres/sparse_matrix.h"
#include "ceres/types.h"
namespace ceres {
struct CRSMatrix;
namespace internal {
class ContextImpl;
class TripletSparseMatrix;
class CERES_NO_EXPORT CompressedRowSparseMatrix : public SparseMatrix {
public:
enum class StorageType {
UNSYMMETRIC,
// Matrix is assumed to be symmetric but only the lower triangular
// part of the matrix is stored.
LOWER_TRIANGULAR,
// Matrix is assumed to be symmetric but only the upper triangular
// part of the matrix is stored.
UPPER_TRIANGULAR
};
// Create a matrix with the same content as the TripletSparseMatrix
// input. We assume that input does not have any repeated
// entries.
//
// The storage type of the matrix is set to UNSYMMETRIC.
static std::unique_ptr<CompressedRowSparseMatrix> FromTripletSparseMatrix(
const TripletSparseMatrix& input);
// Create a matrix with the same content as the TripletSparseMatrix
// input transposed. We assume that input does not have any repeated
// entries.
//
// The storage type of the matrix is set to UNSYMMETRIC.
static std::unique_ptr<CompressedRowSparseMatrix>
FromTripletSparseMatrixTransposed(const TripletSparseMatrix& input);
// 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.
//
// The storage type is set to UNSYMMETRIC.
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);
//
// The storage type is set to UNSYMMETRIC
CompressedRowSparseMatrix(const double* diagonal, int num_rows);
// SparseMatrix interface.
~CompressedRowSparseMatrix() override;
void SetZero() final;
void RightMultiplyAndAccumulate(const double* x, double* y) const final;
void RightMultiplyAndAccumulate(const double* x,
double* y,
ContextImpl* context,
int num_threads) const final;
void LeftMultiplyAndAccumulate(const double* x, double* y) const final;
void SquaredColumnNorm(double* x) const final;
void ScaleColumns(const double* scale) final;
void ToDenseMatrix(Matrix* dense_matrix) const final;
void ToTextFile(FILE* file) const final;
int num_rows() const final { return num_rows_; }
int num_cols() const final { return num_cols_; }
int num_nonzeros() const final { return rows_[num_rows_]; }
const double* values() const final { return values_.data(); }
double* mutable_values() final { return values_.data(); }
// 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;
std::unique_ptr<CompressedRowSparseMatrix> Transpose() const;
// 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; }
// Low level access methods that expose the structure of the matrix.
const int* cols() const { return cols_.data(); }
int* mutable_cols() { return cols_.data(); }
const int* rows() const { return rows_.data(); }
int* mutable_rows() { return rows_.data(); }
StorageType storage_type() const { return storage_type_; }
void set_storage_type(const StorageType storage_type) {
storage_type_ = storage_type;
}
const std::vector<Block>& row_blocks() const { return row_blocks_; }
std::vector<Block>* mutable_row_blocks() { return &row_blocks_; }
const std::vector<Block>& col_blocks() const { return col_blocks_; }
std::vector<Block>* mutable_col_blocks() { return &col_blocks_; }
// Create a block diagonal CompressedRowSparseMatrix with the given
// block structure. The individual blocks are assumed to be laid out
// contiguously in the diagonal array, one block at a time.
static std::unique_ptr<CompressedRowSparseMatrix> CreateBlockDiagonalMatrix(
const double* diagonal, const std::vector<Block>& blocks);
// Options struct to control the generation of random block sparse
// matrices in compressed row sparse format.
//
// The random matrix generation proceeds as follows.
//
// First the row and column block structure is determined by
// generating random row and column block sizes that lie within the
// given bounds.
//
// Then we walk the block structure of the resulting matrix, and with
// probability block_density determine whether they are structurally
// zero or not. If the answer is no, then we generate entries for the
// block which are distributed normally.
struct RandomMatrixOptions {
// Type of matrix to create.
//
// If storage_type is UPPER_TRIANGULAR (LOWER_TRIANGULAR), then
// create a square symmetric matrix with just the upper triangular
// (lower triangular) part. In this case, num_col_blocks,
// min_col_block_size and max_col_block_size will be ignored and
// assumed to be equal to the corresponding row settings.
StorageType storage_type = StorageType::UNSYMMETRIC;
int num_row_blocks = 0;
int min_row_block_size = 0;
int max_row_block_size = 0;
int num_col_blocks = 0;
int min_col_block_size = 0;
int max_col_block_size = 0;
// 0 < block_density <= 1 is the probability of a block being
// present in the matrix. A given random matrix will not have
// precisely this density.
double block_density = 0.0;
};
// Create a random CompressedRowSparseMatrix whose entries are
// normally distributed and whose structure is determined by
// RandomMatrixOptions.
static std::unique_ptr<CompressedRowSparseMatrix> CreateRandomMatrix(
RandomMatrixOptions options, std::mt19937& prng);
private:
static std::unique_ptr<CompressedRowSparseMatrix> FromTripletSparseMatrix(
const TripletSparseMatrix& input, bool transpose);
int num_rows_;
int num_cols_;
std::vector<int> rows_;
std::vector<int> cols_;
std::vector<double> values_;
StorageType storage_type_;
// If the matrix has an underlying block structure, then it can also
// carry with it row and column block sizes. This is auxiliary 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<Block> row_blocks_;
std::vector<Block> col_blocks_;
};
inline std::ostream& operator<<(std::ostream& s,
CompressedRowSparseMatrix::StorageType type) {
switch (type) {
case CompressedRowSparseMatrix::StorageType::UNSYMMETRIC:
s << "UNSYMMETRIC";
break;
case CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR:
s << "UPPER_TRIANGULAR";
break;
case CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR:
s << "LOWER_TRIANGULAR";
break;
default:
s << "UNKNOWN CompressedRowSparseMatrix::StorageType";
}
return s;
}
} // namespace internal
} // namespace ceres
#include "ceres/internal/reenable_warnings.h"
#endif // CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_