141 lines
5.4 KiB
C++
141 lines
5.4 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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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: strandmark@google.com (Petter Strandmark)
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#ifndef CERES_INTERNAL_CXSPARSE_H_
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#define CERES_INTERNAL_CXSPARSE_H_
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// This include must come before any #ifndef check on Ceres compile options.
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#include "ceres/internal/port.h"
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#ifndef CERES_NO_CXSPARSE
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#include <vector>
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#include "cs.h"
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namespace ceres {
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namespace internal {
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class CompressedRowSparseMatrix;
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class TripletSparseMatrix;
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// This object provides access to solving linear systems using Cholesky
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// factorization with a known symbolic factorization. This features does not
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// explicity exist in CXSparse. The methods in the class are nonstatic because
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// the class manages internal scratch space.
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class CXSparse {
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public:
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CXSparse();
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~CXSparse();
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// Solves a symmetric linear system A * x = b using Cholesky factorization.
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// A - The system matrix.
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// symbolic_factorization - The symbolic factorization of A. This is obtained
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// from AnalyzeCholesky.
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// b - The right hand size of the linear equation. This
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// array will also recieve the solution.
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// Returns false if Cholesky factorization of A fails.
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bool SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b);
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// Creates a sparse matrix from a compressed-column form. No memory is
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// allocated or copied; the structure A is filled out with info from the
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// argument.
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cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
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// Creates a new matrix from a triplet form. Deallocate the returned matrix
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// with Free. May return NULL if the compression or allocation fails.
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cs_di* CreateSparseMatrix(TripletSparseMatrix* A);
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// B = A'
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//
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// The returned matrix should be deallocated with Free when not used
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// anymore.
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cs_di* TransposeMatrix(cs_di* A);
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// C = A * B
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//
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// The returned matrix should be deallocated with Free when not used
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// anymore.
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cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);
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// Computes a symbolic factorization of A that can be used in SolveCholesky.
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//
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// The returned matrix should be deallocated with Free when not used anymore.
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cs_dis* AnalyzeCholesky(cs_di* A);
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// Computes a symbolic factorization of A that can be used in
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// SolveCholesky, but does not compute a fill-reducing ordering.
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//
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// The returned matrix should be deallocated with Free when not used anymore.
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cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);
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// Computes a symbolic factorization of A that can be used in
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// SolveCholesky. The difference from AnalyzeCholesky is that this
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// function first detects the block sparsity of the matrix using
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// information about the row and column blocks and uses this block
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// sparse matrix to find a fill-reducing ordering. This ordering is
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// then used to find a symbolic factorization. This can result in a
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// significant performance improvement AnalyzeCholesky on block
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// sparse matrices.
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//
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// The returned matrix should be deallocated with Free when not used
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// anymore.
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cs_dis* BlockAnalyzeCholesky(cs_di* A,
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const std::vector<int>& row_blocks,
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const std::vector<int>& col_blocks);
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// Compute an fill-reducing approximate minimum degree ordering of
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// the matrix A. ordering should be non-NULL and should point to
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// enough memory to hold the ordering for the rows of A.
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void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);
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void Free(cs_di* sparse_matrix);
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void Free(cs_dis* symbolic_factorization);
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private:
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// Cached scratch space
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CS_ENTRY* scratch_;
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int scratch_size_;
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};
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} // namespace internal
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} // namespace ceres
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#else // CERES_NO_CXSPARSE
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typedef void cs_dis;
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class CXSparse {
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public:
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void Free(void* arg) {}
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};
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#endif // CERES_NO_CXSPARSE
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#endif // CERES_INTERNAL_CXSPARSE_H_
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