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7a3c43b847
By virtue of the modeling layer in Ceres being block oriented, all the matrices used by Ceres are also block oriented. When doing sparse direct factorization of these matrices, the fill-reducing ordering algorithms can either be run on the block or the scalar form of these matrices. Running it on the block form exposes more of the super-nodal structure of the matrix to the Cholesky factorization routines. This leads to substantial gains in factorization performance. This changelist adds support for approximate minimium degree orderings to be computed on the block structure of the Schur complement matrix. This affects, SchurComplementSolver and VisibilityBasedPreconditioner and SparseNormalCholesky when using SuiteSparse. A bool, use_block_amd has been added to Solver::Options and bundle_adjuster.cc has been updated to allow testing with it. When combined with a multithreaded Schur elimination, speed ups can be seen quite uniformly across the board. For some problems this can be dramatic, reducing the factorization time from 70 seconds down to 17 seconds. Change-Id: I15ebb0afcbc85ada032ec8d179ee3a2f7c8d3e46
145 lines
5.8 KiB
C++
145 lines
5.8 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#ifndef CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_
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#define CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_
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#include <vector>
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#include <glog/logging.h>
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#include "ceres/sparse_matrix.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/internal/macros.h"
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#include "ceres/internal/port.h"
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#include "ceres/types.h"
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namespace ceres {
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namespace internal {
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class SparseMatrixProto;
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class CompressedRowSparseMatrix : public SparseMatrix {
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public:
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// Build a matrix with the same content as the TripletSparseMatrix
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// m. TripletSparseMatrix objects are easier to construct
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// incrementally, so we use them to initialize SparseMatrix
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// objects.
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//
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// We assume that m does not have any repeated entries.
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explicit CompressedRowSparseMatrix(const TripletSparseMatrix& m);
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#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
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explicit CompressedRowSparseMatrix(const SparseMatrixProto& proto);
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#endif
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// Use this constructor only if you know what you are doing. This
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// creates a "blank" matrix with the appropriate amount of memory
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// allocated. However, the object itself is in an inconsistent state
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// as the rows and cols matrices do not match the values of
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// num_rows, num_cols and max_num_nonzeros.
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//
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// The use case for this constructor is that when the user knows the
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// size of the matrix to begin with and wants to update the layout
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// manually, instead of going via the indirect route of first
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// constructing a TripletSparseMatrix, which leads to more than
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// double the peak memory usage.
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CompressedRowSparseMatrix(int num_rows,
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int num_cols,
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int max_num_nonzeros);
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// Build a square sparse diagonal matrix with num_rows rows and
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// columns. The diagonal m(i,i) = diagonal(i);
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CompressedRowSparseMatrix(const double* diagonal, int num_rows);
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virtual ~CompressedRowSparseMatrix();
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// SparseMatrix interface.
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virtual void SetZero();
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virtual void RightMultiply(const double* x, double* y) const;
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virtual void LeftMultiply(const double* x, double* y) const;
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virtual void SquaredColumnNorm(double* x) const;
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virtual void ScaleColumns(const double* scale);
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virtual void ToDenseMatrix(Matrix* dense_matrix) const;
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#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
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virtual void ToProto(SparseMatrixProto* proto) const;
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#endif
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virtual void ToTextFile(FILE* file) const;
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virtual int num_rows() const { return num_rows_; }
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virtual int num_cols() const { return num_cols_; }
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virtual int num_nonzeros() const { return rows_[num_rows_]; }
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virtual const double* values() const { return values_.get(); }
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virtual double* mutable_values() { return values_.get(); }
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// Delete the bottom delta_rows.
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// num_rows -= delta_rows
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void DeleteRows(int delta_rows);
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// Append the contents of m to the bottom of this matrix. m must
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// have the same number of columns as this matrix.
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void AppendRows(const CompressedRowSparseMatrix& m);
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// Low level access methods that expose the structure of the matrix.
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const int* cols() const { return cols_.get(); }
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int* mutable_cols() { return cols_.get(); }
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const int* rows() const { return rows_.get(); }
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int* mutable_rows() { return rows_.get(); }
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const vector<int>& row_blocks() const { return row_blocks_; };
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vector<int>* mutable_row_blocks() { return &row_blocks_; };
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const vector<int>& col_blocks() const { return col_blocks_; };
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vector<int>* mutable_col_blocks() { return &col_blocks_; };
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private:
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scoped_array<int> cols_;
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scoped_array<int> rows_;
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scoped_array<double> values_;
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int num_rows_;
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int num_cols_;
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int max_num_nonzeros_;
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// If the matrix has an underlying block structure, then it can also
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// carry with it row and column block sizes. This is auxilliary and
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// optional information for use by algorithms operating on the
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// matrix. The class itself does not make use of this information in
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// any way.
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vector<int> row_blocks_;
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vector<int> col_blocks_;
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CERES_DISALLOW_COPY_AND_ASSIGN(CompressedRowSparseMatrix);
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};
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} // namespace internal
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} // namespace ceres
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#endif // CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_
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