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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
#define CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
#include <memory>
#include <vector>
#include "ceres/block_sparse_matrix.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/internal/port.h"
namespace ceres {
namespace internal {
// This class is used to repeatedly compute the inner product
//
// result = m' * m
//
// where the sparsity structure of m remains constant across calls.
//
// Upon creation, the class computes and caches information needed to
// compute v, and then uses it to efficiently compute the product
// every time InnerProductComputer::Compute is called.
//
// See sparse_normal_cholesky_solver.cc for example usage.
//
// Note that the result matrix is a block upper or lower-triangular
// matrix, i.e., it will contain entries in the upper or lower
// triangular part of the matrix corresponding to the block that occur
// along its diagonal.
//
// This is not a problem as sparse linear algebra libraries can ignore
// these entries with ease and the space used is minimal/linear in the
// size of the matrices.
class CERES_EXPORT_INTERNAL InnerProductComputer {
public:
// Factory
//
// m is the input matrix
//
// Since m' * m is a symmetric matrix, we only compute half of the
// matrix and the value of storage_type which must be
// UPPER_TRIANGULAR or LOWER_TRIANGULAR determines which half is
// computed.
//
// The user must ensure that the matrix m is valid for the life time
// of this object.
static InnerProductComputer* Create(
const BlockSparseMatrix& m,
CompressedRowSparseMatrix::StorageType storage_type);
// This factory method allows the user control over range of row
// blocks of m that should be used to compute the inner product.
//
// a = m(start_row_block : end_row_block, :);
// result = a' * a;
static InnerProductComputer* Create(
const BlockSparseMatrix& m,
int start_row_block,
int end_row_block,
CompressedRowSparseMatrix::StorageType storage_type);
// Update result_ to be numerically equal to m' * m.
void Compute();
// Accessors for the result containing the inner product.
//
// Compute must be called before accessing this result for
// the first time.
const CompressedRowSparseMatrix& result() const { return *result_; }
CompressedRowSparseMatrix* mutable_result() const { return result_.get(); }
private:
// A ProductTerm is a term in the block inner product of a matrix
// with itself.
struct ProductTerm {
ProductTerm(const int row, const int col, const int index)
: row(row), col(col), index(index) {}
bool operator<(const ProductTerm& right) const {
if (row == right.row) {
if (col == right.col) {
return index < right.index;
}
return col < right.col;
}
return row < right.row;
}
int row;
int col;
int index;
};
InnerProductComputer(const BlockSparseMatrix& m,
int start_row_block,
int end_row_block);
void Init(CompressedRowSparseMatrix::StorageType storage_type);
CompressedRowSparseMatrix* CreateResultMatrix(
const CompressedRowSparseMatrix::StorageType storage_type,
int num_nonzeros);
int ComputeNonzeros(const std::vector<ProductTerm>& product_terms,
std::vector<int>* row_block_nnz);
void ComputeOffsetsAndCreateResultMatrix(
const CompressedRowSparseMatrix::StorageType storage_type,
const std::vector<ProductTerm>& product_terms);
const BlockSparseMatrix& m_;
const int start_row_block_;
const int end_row_block_;
std::unique_ptr<CompressedRowSparseMatrix> result_;
// For each term in the inner product, result_offsets_ contains the
// location in the values array of the result_ matrix where it
// should be stored.
//
// This is the principal look up table that allows this class to
// compute the inner product fast.
std::vector<int> result_offsets_;
};
} // namespace internal
} // namespace ceres
#endif // CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
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