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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// 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.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
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//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_random_access_diagonal_matrix.h"
#include <algorithm>
#include <memory>
#include <set>
#include <utility>
#include <vector>
#include "Eigen/Dense"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/internal/export.h"
#include "ceres/parallel_for.h"
#include "ceres/stl_util.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres::internal {
BlockRandomAccessDiagonalMatrix::BlockRandomAccessDiagonalMatrix(
const std::vector<Block>& blocks, ContextImpl* context, int num_threads)
: context_(context), num_threads_(num_threads) {
m_ = CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(nullptr, blocks);
double* values = m_->mutable_values();
layout_.reserve(blocks.size());
for (auto& block : blocks) {
layout_.emplace_back(std::make_unique<CellInfo>(values));
values += block.size * block.size;
}
}
CellInfo* BlockRandomAccessDiagonalMatrix::GetCell(int row_block_id,
int col_block_id,
int* row,
int* col,
int* row_stride,
int* col_stride) {
if (row_block_id != col_block_id) {
return nullptr;
}
auto& blocks = m_->row_blocks();
const int stride = blocks[row_block_id].size;
// Each cell is stored contiguously as its own little dense matrix.
*row = 0;
*col = 0;
*row_stride = stride;
*col_stride = stride;
return layout_[row_block_id].get();
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
void BlockRandomAccessDiagonalMatrix::SetZero() {
ParallelSetZero(
context_, num_threads_, m_->mutable_values(), m_->num_nonzeros());
}
void BlockRandomAccessDiagonalMatrix::Invert() {
auto& blocks = m_->row_blocks();
const int num_blocks = blocks.size();
ParallelFor(context_, 0, num_blocks, num_threads_, [this, blocks](int i) {
auto* cell_info = layout_[i].get();
auto& block = blocks[i];
MatrixRef b(cell_info->values, block.size, block.size);
b = b.selfadjointView<Eigen::Upper>().llt().solve(
Matrix::Identity(block.size, block.size));
});
}
void BlockRandomAccessDiagonalMatrix::RightMultiplyAndAccumulate(
const double* x, double* y) const {
CHECK(x != nullptr);
CHECK(y != nullptr);
auto& blocks = m_->row_blocks();
const int num_blocks = blocks.size();
ParallelFor(
context_, 0, num_blocks, num_threads_, [this, blocks, x, y](int i) {
auto* cell_info = layout_[i].get();
auto& block = blocks[i];
ConstMatrixRef b(cell_info->values, block.size, block.size);
VectorRef(y + block.position, block.size).noalias() +=
b * ConstVectorRef(x + block.position, block.size);
});
}
} // namespace ceres::internal