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diff --git a/internal/ceres/suitesparse.h b/internal/ceres/suitesparse.h new file mode 100644 index 0000000..eb691c0 --- /dev/null +++ b/internal/ceres/suitesparse.h @@ -0,0 +1,233 @@ +// Ceres Solver - A fast non-linear least squares minimizer +// Copyright 2010, 2011, 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: sameeragarwal@google.com (Sameer Agarwal) +// +// A simple C++ interface to the SuiteSparse and CHOLMOD libraries. + +#ifndef CERES_INTERNAL_SUITESPARSE_H_ +#define CERES_INTERNAL_SUITESPARSE_H_ + +#ifndef CERES_NO_SUITESPARSE + +#include <cstring> +#include <string> +#include <vector> + +#include <glog/logging.h> +#include "cholmod.h" +#include "ceres/internal/port.h" + +namespace ceres { +namespace internal { + +class CompressedRowSparseMatrix; +class TripletSparseMatrix; + +// The raw CHOLMOD and SuiteSparseQR libraries have a slightly +// cumbersome c like calling format. This object abstracts it away and +// provides the user with a simpler interface. The methods here cannot +// be static as a cholmod_common object serves as a global variable +// for all cholmod function calls. +class SuiteSparse { + public: + SuiteSparse() { cholmod_start(&cc_); } + ~SuiteSparse() { cholmod_finish(&cc_); } + + // Functions for building cholmod_sparse objects from sparse + // matrices stored in triplet form. The matrix A is not + // modifed. Called owns the result. + cholmod_sparse* CreateSparseMatrix(TripletSparseMatrix* A); + + // This function works like CreateSparseMatrix, except that the + // return value corresponds to A' rather than A. + cholmod_sparse* CreateSparseMatrixTranspose(TripletSparseMatrix* A); + + // Create a cholmod_sparse wrapper around the contents of A. This is + // a shallow object, which refers to the contents of A and does not + // use the SuiteSparse machinery to allocate memory, this object + // should be disposed off with a delete and not a call to Free as is + // the case for objects returned by CreateSparseMatrixTranspose. + cholmod_sparse* CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A); + + // Given a vector x, build a cholmod_dense vector of size out_size + // with the first in_size entries copied from x. If x is NULL, then + // an all zeros vector is returned. Caller owns the result. + cholmod_dense* CreateDenseVector(const double* x, int in_size, int out_size); + + // The matrix A is scaled using the matrix whose diagonal is the + // vector scale. mode describes how scaling is applied. Possible + // values are CHOLMOD_ROW for row scaling - diag(scale) * A, + // CHOLMOD_COL for column scaling - A * diag(scale) and CHOLMOD_SYM + // for symmetric scaling which scales both the rows and the columns + // - diag(scale) * A * diag(scale). + void Scale(cholmod_dense* scale, int mode, cholmod_sparse* A) { + cholmod_scale(scale, mode, A, &cc_); + } + + // Create and return a matrix m = A * A'. Caller owns the + // result. The matrix A is not modified. + cholmod_sparse* AATranspose(cholmod_sparse* A) { + cholmod_sparse*m = cholmod_aat(A, NULL, A->nrow, 1, &cc_); + m->stype = 1; // Pay attention to the upper triangular part. + return m; + } + + // y = alpha * A * x + beta * y. Only y is modified. + void SparseDenseMultiply(cholmod_sparse* A, double alpha, double beta, + cholmod_dense* x, cholmod_dense* y) { + double alpha_[2] = {alpha, 0}; + double beta_[2] = {beta, 0}; + cholmod_sdmult(A, 0, alpha_, beta_, x, y, &cc_); + } + + // Find an ordering of A or AA' (if A is unsymmetric) that minimizes + // the fill-in in the Cholesky factorization of the corresponding + // matrix. This is done by using the AMD algorithm. + // + // Using this ordering, the symbolic Cholesky factorization of A (or + // AA') is computed and returned. + // + // A is not modified, only the pattern of non-zeros of A is used, + // the actual numerical values in A are of no consequence. + // + // Caller owns the result. + cholmod_factor* AnalyzeCholesky(cholmod_sparse* A); + + cholmod_factor* BlockAnalyzeCholesky(cholmod_sparse* A, + const vector<int>& row_blocks, + const vector<int>& col_blocks); + + // If A is symmetric, then compute the symbolic Cholesky + // factorization of A(ordering, ordering). If A is unsymmetric, then + // compute the symbolic factorization of + // A(ordering,:) A(ordering,:)'. + // + // A is not modified, only the pattern of non-zeros of A is used, + // the actual numerical values in A are of no consequence. + // + // Caller owns the result. + cholmod_factor* AnalyzeCholeskyWithUserOrdering(cholmod_sparse* A, + const vector<int>& ordering); + + // Use the symbolic factorization in L, to find the numerical + // factorization for the matrix A or AA^T. Return true if + // successful, false otherwise. L contains the numeric factorization + // on return. + bool Cholesky(cholmod_sparse* A, cholmod_factor* L); + + // Given a Cholesky factorization of a matrix A = LL^T, solve the + // linear system Ax = b, and return the result. If the Solve fails + // NULL is returned. Caller owns the result. + cholmod_dense* Solve(cholmod_factor* L, cholmod_dense* b); + + // Combine the calls to Cholesky and Solve into a single call. If + // the cholesky factorization or the solve fails, return + // NULL. Caller owns the result. + cholmod_dense* SolveCholesky(cholmod_sparse* A, + cholmod_factor* L, + cholmod_dense* b); + + // 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 (in particular AMD) can either + // be run on the block or the scalar form of these matrices. The two + // SuiteSparse::AnalyzeCholesky methods allows the the client to + // compute the symbolic factorization of a matrix by either using + // AMD on the matrix or a user provided ordering of the rows. + // + // But since the underlying matrices are block oriented, it is worth + // running AMD on just the block structre of these matrices and then + // lifting these block orderings to a full scalar ordering. This + // preserves the block structure of the permuted matrix, and exposes + // more of the super-nodal structure of the matrix to the numerical + // factorization routines. + // + // Find the block oriented AMD ordering of a matrix A, whose row and + // column blocks are given by row_blocks, and col_blocks + // respectively. The matrix may or may not be symmetric. The entries + // of col_blocks do not need to sum to the number of columns in + // A. If this is the case, only the first sum(col_blocks) are used + // to compute the ordering. + bool BlockAMDOrdering(const cholmod_sparse* A, + const vector<int>& row_blocks, + const vector<int>& col_blocks, + vector<int>* ordering); + + // Given a set of blocks and a permutation of these blocks, compute + // the corresponding "scalar" ordering, where the scalar ordering of + // size sum(blocks). + static void BlockOrderingToScalarOrdering(const vector<int>& blocks, + const vector<int>& block_ordering, + vector<int>* scalar_ordering); + + // Extract the block sparsity pattern of the scalar sparse matrix + // A and return it in compressed column form. The compressed column + // form is stored in two vectors block_rows, and block_cols, which + // correspond to the row and column arrays in a compressed column sparse + // matrix. + // + // If c_ij is the block in the matrix A corresponding to row block i + // and column block j, then it is expected that A contains at least + // one non-zero entry corresponding to the top left entry of c_ij, + // as that entry is used to detect the presence of a non-zero c_ij. + static void ScalarMatrixToBlockMatrix(const cholmod_sparse* A, + const vector<int>& row_blocks, + const vector<int>& col_blocks, + vector<int>* block_rows, + vector<int>* block_cols); + + void Free(cholmod_sparse* m) { cholmod_free_sparse(&m, &cc_); } + void Free(cholmod_dense* m) { cholmod_free_dense(&m, &cc_); } + void Free(cholmod_factor* m) { cholmod_free_factor(&m, &cc_); } + + void Print(cholmod_sparse* m, const string& name) { + cholmod_print_sparse(m, const_cast<char*>(name.c_str()), &cc_); + } + + void Print(cholmod_dense* m, const string& name) { + cholmod_print_dense(m, const_cast<char*>(name.c_str()), &cc_); + } + + void Print(cholmod_triplet* m, const string& name) { + cholmod_print_triplet(m, const_cast<char*>(name.c_str()), &cc_); + } + + cholmod_common* mutable_cc() { return &cc_; } + + private: + cholmod_common cc_; +}; + +} // namespace internal +} // namespace ceres + +#endif // CERES_NO_SUITESPARSE + +#endif // CERES_INTERNAL_SUITESPARSE_H_ |