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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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: strandmark@google.com (Petter Strandmark)
#ifndef CERES_INTERNAL_CXSPARSE_H_
#define CERES_INTERNAL_CXSPARSE_H_
// This include must come before any #ifndef check on Ceres compile options.
#include "ceres/internal/port.h"
#ifndef CERES_NO_CXSPARSE
#include <vector>
#include "cs.h"
namespace ceres {
namespace internal {
class CompressedRowSparseMatrix;
class TripletSparseMatrix;
// This object provides access to solving linear systems using Cholesky
// factorization with a known symbolic factorization. This features does not
// explicity exist in CXSparse. The methods in the class are nonstatic because
// the class manages internal scratch space.
class CXSparse {
public:
CXSparse();
~CXSparse();
// Solves a symmetric linear system A * x = b using Cholesky factorization.
// A - The system matrix.
// symbolic_factorization - The symbolic factorization of A. This is obtained
// from AnalyzeCholesky.
// b - The right hand size of the linear equation. This
// array will also recieve the solution.
// Returns false if Cholesky factorization of A fails.
bool SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b);
// Creates a sparse matrix from a compressed-column form. No memory is
// allocated or copied; the structure A is filled out with info from the
// argument.
cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
// Creates a new matrix from a triplet form. Deallocate the returned matrix
// with Free. May return NULL if the compression or allocation fails.
cs_di* CreateSparseMatrix(TripletSparseMatrix* A);
// B = A'
//
// The returned matrix should be deallocated with Free when not used
// anymore.
cs_di* TransposeMatrix(cs_di* A);
// C = A * B
//
// The returned matrix should be deallocated with Free when not used
// anymore.
cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);
// Computes a symbolic factorization of A that can be used in SolveCholesky.
//
// The returned matrix should be deallocated with Free when not used anymore.
cs_dis* AnalyzeCholesky(cs_di* A);
// Computes a symbolic factorization of A that can be used in
// SolveCholesky, but does not compute a fill-reducing ordering.
//
// The returned matrix should be deallocated with Free when not used anymore.
cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);
// Computes a symbolic factorization of A that can be used in
// SolveCholesky. The difference from AnalyzeCholesky is that this
// function first detects the block sparsity of the matrix using
// information about the row and column blocks and uses this block
// sparse matrix to find a fill-reducing ordering. This ordering is
// then used to find a symbolic factorization. This can result in a
// significant performance improvement AnalyzeCholesky on block
// sparse matrices.
//
// The returned matrix should be deallocated with Free when not used
// anymore.
cs_dis* BlockAnalyzeCholesky(cs_di* A,
const vector<int>& row_blocks,
const vector<int>& col_blocks);
// Compute an fill-reducing approximate minimum degree ordering of
// the matrix A. ordering should be non-NULL and should point to
// enough memory to hold the ordering for the rows of A.
void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);
void Free(cs_di* sparse_matrix);
void Free(cs_dis* symbolic_factorization);
private:
// Cached scratch space
CS_ENTRY* scratch_;
int scratch_size_;
};
} // namespace internal
} // namespace ceres
#else // CERES_NO_CXSPARSE
typedef void cs_dis;
class CXSparse {
public:
void Free(void*) {};
};
#endif // CERES_NO_CXSPARSE
#endif // CERES_INTERNAL_CXSPARSE_H_
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