// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
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// used to endorse or promote products derived from this software without
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//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_sparse_matrix.h"
#include <cstddef>
#include <algorithm>
#include <vector>
#include "ceres/block_structure.h"
#include "ceres/internal/eigen.h"
#include "ceres/random.h"
#include "ceres/small_blas.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
using std::vector;
BlockSparseMatrix::~BlockSparseMatrix() {}
BlockSparseMatrix::BlockSparseMatrix(
CompressedRowBlockStructure* block_structure)
: num_rows_(0),
num_cols_(0),
num_nonzeros_(0),
values_(NULL),
block_structure_(block_structure) {
CHECK_NOTNULL(block_structure_.get());
// Count the number of columns in the matrix.
for (int i = 0; i < block_structure_->cols.size(); ++i) {
num_cols_ += block_structure_->cols[i].size;
}
// Count the number of non-zero entries and the number of rows in
// the matrix.
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
num_rows_ += row_block_size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
num_nonzeros_ += col_block_size * row_block_size;
}
}
CHECK_GE(num_rows_, 0);
CHECK_GE(num_cols_, 0);
CHECK_GE(num_nonzeros_, 0);
VLOG(2) << "Allocating values array with "
<< num_nonzeros_ * sizeof(double) << " bytes."; // NOLINT
values_.reset(new double[num_nonzeros_]);
max_num_nonzeros_ = num_nonzeros_;
CHECK_NOTNULL(values_.get());
}
void BlockSparseMatrix::SetZero() {
std::fill(values_.get(), values_.get() + num_nonzeros_, 0.0);
}
void BlockSparseMatrix::RightMultiply(const double* x, double* y) const {
CHECK_NOTNULL(x);
CHECK_NOTNULL(y);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values_.get() + cells[j].position, row_block_size, col_block_size,
x + col_block_pos,
y + row_block_pos);
}
}
}
void BlockSparseMatrix::LeftMultiply(const double* x, double* y) const {
CHECK_NOTNULL(x);
CHECK_NOTNULL(y);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values_.get() + cells[j].position, row_block_size, col_block_size,
x + row_block_pos,
y + col_block_pos);
}
}
}
void BlockSparseMatrix::SquaredColumnNorm(double* x) const {
CHECK_NOTNULL(x);
VectorRef(x, num_cols_).setZero();
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
const MatrixRef m(values_.get() + cells[j].position,
row_block_size, col_block_size);
VectorRef(x + col_block_pos, col_block_size) += m.colwise().squaredNorm();
}
}
}
void BlockSparseMatrix::ScaleColumns(const double* scale) {
CHECK_NOTNULL(scale);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
MatrixRef m(values_.get() + cells[j].position,
row_block_size, col_block_size);
m *= ConstVectorRef(scale + col_block_pos, col_block_size).asDiagonal();
}
}
}
void BlockSparseMatrix::ToDenseMatrix(Matrix* dense_matrix) const {
CHECK_NOTNULL(dense_matrix);
dense_matrix->resize(num_rows_, num_cols_);
dense_matrix->setZero();
Matrix& m = *dense_matrix;
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cells[j].position;
m.block(row_block_pos, col_block_pos, row_block_size, col_block_size)
+= MatrixRef(values_.get() + jac_pos, row_block_size, col_block_size);
}
}
}
void BlockSparseMatrix::ToTripletSparseMatrix(
TripletSparseMatrix* matrix) const {
CHECK_NOTNULL(matrix);
matrix->Reserve(num_nonzeros_);
matrix->Resize(num_rows_, num_cols_);
matrix->SetZero();
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
int col_block_id = cells[j].block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cells[j].position;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c, ++jac_pos) {
matrix->mutable_rows()[jac_pos] = row_block_pos + r;
matrix->mutable_cols()[jac_pos] = col_block_pos + c;
matrix->mutable_values()[jac_pos] = values_[jac_pos];
}
}
}
}
matrix->set_num_nonzeros(num_nonzeros_);
}
// Return a pointer to the block structure. We continue to hold
// ownership of the object though.
const CompressedRowBlockStructure* BlockSparseMatrix::block_structure()
const {
return block_structure_.get();
}
void BlockSparseMatrix::ToTextFile(FILE* file) const {
CHECK_NOTNULL(file);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
const int row_block_pos = block_structure_->rows[i].block.position;
const int row_block_size = block_structure_->rows[i].block.size;
const vector<Cell>& cells = block_structure_->rows[i].cells;
for (int j = 0; j < cells.size(); ++j) {
const int col_block_id = cells[j].block_id;
const int col_block_size = block_structure_->cols[col_block_id].size;
const int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cells[j].position;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c) {
fprintf(file, "% 10d % 10d %17f\n",
row_block_pos + r,
col_block_pos + c,
values_[jac_pos++]);
}
}
}
}
}
BlockSparseMatrix* BlockSparseMatrix::CreateDiagonalMatrix(
const double* diagonal, const std::vector<Block>& column_blocks) {
// Create the block structure for the diagonal matrix.
CompressedRowBlockStructure* bs = new CompressedRowBlockStructure();
bs->cols = column_blocks;
int position = 0;
bs->rows.resize(column_blocks.size(), CompressedRow(1));
for (int i = 0; i < column_blocks.size(); ++i) {
CompressedRow& row = bs->rows[i];
row.block = column_blocks[i];
Cell& cell = row.cells[0];
cell.block_id = i;
cell.position = position;
position += row.block.size * row.block.size;
}
// Create the BlockSparseMatrix with the given block structure.
BlockSparseMatrix* matrix = new BlockSparseMatrix(bs);
matrix->SetZero();
// Fill the values array of the block sparse matrix.
double* values = matrix->mutable_values();
for (int i = 0; i < column_blocks.size(); ++i) {
const int size = column_blocks[i].size;
for (int j = 0; j < size; ++j) {
// (j + 1) * size is compact way of accessing the (j,j) entry.
values[j * (size + 1)] = diagonal[j];
}
diagonal += size;
values += size * size;
}
return matrix;
}
void BlockSparseMatrix::AppendRows(const BlockSparseMatrix& m) {
const int old_num_nonzeros = num_nonzeros_;
const int old_num_row_blocks = block_structure_->rows.size();
const CompressedRowBlockStructure* m_bs = m.block_structure();
block_structure_->rows.resize(old_num_row_blocks + m_bs->rows.size());
for (int i = 0; i < m_bs->rows.size(); ++i) {
const CompressedRow& m_row = m_bs->rows[i];
CompressedRow& row = block_structure_->rows[old_num_row_blocks + i];
row.block.size = m_row.block.size;
row.block.position = num_rows_;
num_rows_ += m_row.block.size;
row.cells.resize(m_row.cells.size());
for (int c = 0; c < m_row.cells.size(); ++c) {
const int block_id = m_row.cells[c].block_id;
row.cells[c].block_id = block_id;
row.cells[c].position = num_nonzeros_;
num_nonzeros_ += m_row.block.size * m_bs->cols[block_id].size;
}
}
if (num_nonzeros_ > max_num_nonzeros_) {
double* new_values = new double[num_nonzeros_];
std::copy(values_.get(), values_.get() + old_num_nonzeros, new_values);
values_.reset(new_values);
max_num_nonzeros_ = num_nonzeros_;
}
std::copy(m.values(),
m.values() + m.num_nonzeros(),
values_.get() + old_num_nonzeros);
}
void BlockSparseMatrix::DeleteRowBlocks(const int delta_row_blocks) {
const int num_row_blocks = block_structure_->rows.size();
int delta_num_nonzeros = 0;
int delta_num_rows = 0;
const std::vector<Block>& column_blocks = block_structure_->cols;
for (int i = 0; i < delta_row_blocks; ++i) {
const CompressedRow& row = block_structure_->rows[num_row_blocks - i - 1];
delta_num_rows += row.block.size;
for (int c = 0; c < row.cells.size(); ++c) {
const Cell& cell = row.cells[c];
delta_num_nonzeros += row.block.size * column_blocks[cell.block_id].size;
}
}
num_nonzeros_ -= delta_num_nonzeros;
num_rows_ -= delta_num_rows;
block_structure_->rows.resize(num_row_blocks - delta_row_blocks);
}
BlockSparseMatrix* BlockSparseMatrix::CreateRandomMatrix(
const BlockSparseMatrix::RandomMatrixOptions& options) {
CHECK_GT(options.num_row_blocks, 0);
CHECK_GT(options.min_row_block_size, 0);
CHECK_GT(options.max_row_block_size, 0);
CHECK_LE(options.min_row_block_size, options.max_row_block_size);
CHECK_GT(options.num_col_blocks, 0);
CHECK_GT(options.min_col_block_size, 0);
CHECK_GT(options.max_col_block_size, 0);
CHECK_LE(options.min_col_block_size, options.max_col_block_size);
CHECK_GT(options.block_density, 0.0);
CHECK_LE(options.block_density, 1.0);
CompressedRowBlockStructure* bs = new CompressedRowBlockStructure();
// Generate the col block structure.
int col_block_position = 0;
for (int i = 0; i < options.num_col_blocks; ++i) {
// Generate a random integer in [min_col_block_size, max_col_block_size]
const int delta_block_size =
Uniform(options.max_col_block_size - options.min_col_block_size);
const int col_block_size = options.min_col_block_size + delta_block_size;
bs->cols.push_back(Block(col_block_size, col_block_position));
col_block_position += col_block_size;
}
bool matrix_has_blocks = false;
while (!matrix_has_blocks) {
LOG(INFO) << "clearing";
bs->rows.clear();
int row_block_position = 0;
int value_position = 0;
for (int r = 0; r < options.num_row_blocks; ++r) {
const int delta_block_size =
Uniform(options.max_row_block_size - options.min_row_block_size);
const int row_block_size = options.min_row_block_size + delta_block_size;
bs->rows.push_back(CompressedRow());
CompressedRow& row = bs->rows.back();
row.block.size = row_block_size;
row.block.position = row_block_position;
row_block_position += row_block_size;
for (int c = 0; c < options.num_col_blocks; ++c) {
if (RandDouble() > options.block_density) continue;
row.cells.push_back(Cell());
Cell& cell = row.cells.back();
cell.block_id = c;
cell.position = value_position;
value_position += row_block_size * bs->cols[c].size;
matrix_has_blocks = true;
}
}
}
BlockSparseMatrix* matrix = new BlockSparseMatrix(bs);
double* values = matrix->mutable_values();
for (int i = 0; i < matrix->num_nonzeros(); ++i) {
values[i] = RandNormal();
}
return matrix;
}
} // namespace internal
} // namespace ceres