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Diffstat (limited to 'extern/ceres/internal/ceres/cxsparse.cc')
| -rw-r--r-- | extern/ceres/internal/ceres/cxsparse.cc | 284 |
1 files changed, 284 insertions, 0 deletions
diff --git a/extern/ceres/internal/ceres/cxsparse.cc b/extern/ceres/internal/ceres/cxsparse.cc new file mode 100644 index 00000000000..5a028773206 --- /dev/null +++ b/extern/ceres/internal/ceres/cxsparse.cc @@ -0,0 +1,284 @@ +// Ceres Solver - A fast non-linear least squares minimizer +// Copyright 2015 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: strandmark@google.com (Petter Strandmark) + +// This include must come before any #ifndef check on Ceres compile options. +#include "ceres/internal/port.h" + +#ifndef CERES_NO_CXSPARSE + +#include "ceres/cxsparse.h" + +#include <string> +#include <vector> + +#include "ceres/compressed_col_sparse_matrix_utils.h" +#include "ceres/compressed_row_sparse_matrix.h" +#include "ceres/triplet_sparse_matrix.h" +#include "glog/logging.h" + +namespace ceres { +namespace internal { + +using std::vector; + +CXSparse::CXSparse() : scratch_(NULL), scratch_size_(0) {} + +CXSparse::~CXSparse() { + if (scratch_size_ > 0) { + cs_di_free(scratch_); + } +} + +csn* CXSparse::Cholesky(cs_di* A, cs_dis* symbolic_factor) { + return cs_di_chol(A, symbolic_factor); +} + +void CXSparse::Solve(cs_dis* symbolic_factor, csn* numeric_factor, double* b) { + // Make sure we have enough scratch space available. + const int num_cols = numeric_factor->L->n; + if (scratch_size_ < num_cols) { + if (scratch_size_ > 0) { + cs_di_free(scratch_); + } + scratch_ = + reinterpret_cast<CS_ENTRY*>(cs_di_malloc(num_cols, sizeof(CS_ENTRY))); + scratch_size_ = num_cols; + } + + // When the Cholesky factor succeeded, these methods are + // guaranteed to succeeded as well. In the comments below, "x" + // refers to the scratch space. + // + // Set x = P * b. + CHECK(cs_di_ipvec(symbolic_factor->pinv, b, scratch_, num_cols)); + // Set x = L \ x. + CHECK(cs_di_lsolve(numeric_factor->L, scratch_)); + // Set x = L' \ x. + CHECK(cs_di_ltsolve(numeric_factor->L, scratch_)); + // Set b = P' * x. + CHECK(cs_di_pvec(symbolic_factor->pinv, scratch_, b, num_cols)); +} + +bool CXSparse::SolveCholesky(cs_di* lhs, double* rhs_and_solution) { + return cs_cholsol(1, lhs, rhs_and_solution); +} + +cs_dis* CXSparse::AnalyzeCholesky(cs_di* A) { + // order = 1 for Cholesky factor. + return cs_schol(1, A); +} + +cs_dis* CXSparse::AnalyzeCholeskyWithNaturalOrdering(cs_di* A) { + // order = 0 for Natural ordering. + return cs_schol(0, A); +} + +cs_dis* CXSparse::BlockAnalyzeCholesky(cs_di* A, + const vector<int>& row_blocks, + const vector<int>& col_blocks) { + const int num_row_blocks = row_blocks.size(); + const int num_col_blocks = col_blocks.size(); + + vector<int> block_rows; + vector<int> block_cols; + CompressedColumnScalarMatrixToBlockMatrix( + A->i, A->p, row_blocks, col_blocks, &block_rows, &block_cols); + cs_di block_matrix; + block_matrix.m = num_row_blocks; + block_matrix.n = num_col_blocks; + block_matrix.nz = -1; + block_matrix.nzmax = block_rows.size(); + block_matrix.p = &block_cols[0]; + block_matrix.i = &block_rows[0]; + block_matrix.x = NULL; + + int* ordering = cs_amd(1, &block_matrix); + vector<int> block_ordering(num_row_blocks, -1); + std::copy(ordering, ordering + num_row_blocks, &block_ordering[0]); + cs_free(ordering); + + vector<int> scalar_ordering; + BlockOrderingToScalarOrdering(row_blocks, block_ordering, &scalar_ordering); + + cs_dis* symbolic_factor = + reinterpret_cast<cs_dis*>(cs_calloc(1, sizeof(cs_dis))); + symbolic_factor->pinv = cs_pinv(&scalar_ordering[0], A->n); + cs* permuted_A = cs_symperm(A, symbolic_factor->pinv, 0); + + symbolic_factor->parent = cs_etree(permuted_A, 0); + int* postordering = cs_post(symbolic_factor->parent, A->n); + int* column_counts = + cs_counts(permuted_A, symbolic_factor->parent, postordering, 0); + cs_free(postordering); + cs_spfree(permuted_A); + + symbolic_factor->cp = (int*)cs_malloc(A->n + 1, sizeof(int)); + symbolic_factor->lnz = cs_cumsum(symbolic_factor->cp, column_counts, A->n); + symbolic_factor->unz = symbolic_factor->lnz; + + cs_free(column_counts); + + if (symbolic_factor->lnz < 0) { + cs_sfree(symbolic_factor); + symbolic_factor = NULL; + } + + return symbolic_factor; +} + +cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) { + cs_di At; + At.m = A->num_cols(); + At.n = A->num_rows(); + At.nz = -1; + At.nzmax = A->num_nonzeros(); + At.p = A->mutable_rows(); + At.i = A->mutable_cols(); + At.x = A->mutable_values(); + return At; +} + +cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) { + cs_di_sparse tsm_wrapper; + tsm_wrapper.nzmax = tsm->num_nonzeros(); + tsm_wrapper.nz = tsm->num_nonzeros(); + tsm_wrapper.m = tsm->num_rows(); + tsm_wrapper.n = tsm->num_cols(); + tsm_wrapper.p = tsm->mutable_cols(); + tsm_wrapper.i = tsm->mutable_rows(); + tsm_wrapper.x = tsm->mutable_values(); + + return cs_compress(&tsm_wrapper); +} + +void CXSparse::ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering) { + int* cs_ordering = cs_amd(1, A); + std::copy(cs_ordering, cs_ordering + A->m, ordering); + cs_free(cs_ordering); +} + +cs_di* CXSparse::TransposeMatrix(cs_di* A) { return cs_di_transpose(A, 1); } + +cs_di* CXSparse::MatrixMatrixMultiply(cs_di* A, cs_di* B) { + return cs_di_multiply(A, B); +} + +void CXSparse::Free(cs_di* sparse_matrix) { cs_di_spfree(sparse_matrix); } + +void CXSparse::Free(cs_dis* symbolic_factor) { cs_di_sfree(symbolic_factor); } + +void CXSparse::Free(csn* numeric_factor) { cs_di_nfree(numeric_factor); } + +std::unique_ptr<SparseCholesky> CXSparseCholesky::Create( + const OrderingType ordering_type) { + return std::unique_ptr<SparseCholesky>(new CXSparseCholesky(ordering_type)); +} + +CompressedRowSparseMatrix::StorageType CXSparseCholesky::StorageType() const { + return CompressedRowSparseMatrix::LOWER_TRIANGULAR; +} + +CXSparseCholesky::CXSparseCholesky(const OrderingType ordering_type) + : ordering_type_(ordering_type), + symbolic_factor_(NULL), + numeric_factor_(NULL) {} + +CXSparseCholesky::~CXSparseCholesky() { + FreeSymbolicFactorization(); + FreeNumericFactorization(); +} + +LinearSolverTerminationType CXSparseCholesky::Factorize( + CompressedRowSparseMatrix* lhs, std::string* message) { + CHECK_EQ(lhs->storage_type(), StorageType()); + if (lhs == NULL) { + *message = "Failure: Input lhs is NULL."; + return LINEAR_SOLVER_FATAL_ERROR; + } + + cs_di cs_lhs = cs_.CreateSparseMatrixTransposeView(lhs); + + if (symbolic_factor_ == NULL) { + if (ordering_type_ == NATURAL) { + symbolic_factor_ = cs_.AnalyzeCholeskyWithNaturalOrdering(&cs_lhs); + } else { + if (!lhs->col_blocks().empty() && !(lhs->row_blocks().empty())) { + symbolic_factor_ = cs_.BlockAnalyzeCholesky( + &cs_lhs, lhs->col_blocks(), lhs->row_blocks()); + } else { + symbolic_factor_ = cs_.AnalyzeCholesky(&cs_lhs); + } + } + + if (symbolic_factor_ == NULL) { + *message = "CXSparse Failure : Symbolic factorization failed."; + return LINEAR_SOLVER_FATAL_ERROR; + } + } + + FreeNumericFactorization(); + numeric_factor_ = cs_.Cholesky(&cs_lhs, symbolic_factor_); + if (numeric_factor_ == NULL) { + *message = "CXSparse Failure : Numeric factorization failed."; + return LINEAR_SOLVER_FAILURE; + } + + return LINEAR_SOLVER_SUCCESS; +} + +LinearSolverTerminationType CXSparseCholesky::Solve(const double* rhs, + double* solution, + std::string* message) { + CHECK(numeric_factor_ != NULL) + << "Solve called without a call to Factorize first."; + const int num_cols = numeric_factor_->L->n; + memcpy(solution, rhs, num_cols * sizeof(*solution)); + cs_.Solve(symbolic_factor_, numeric_factor_, solution); + return LINEAR_SOLVER_SUCCESS; +} + +void CXSparseCholesky::FreeSymbolicFactorization() { + if (symbolic_factor_ != NULL) { + cs_.Free(symbolic_factor_); + symbolic_factor_ = NULL; + } +} + +void CXSparseCholesky::FreeNumericFactorization() { + if (numeric_factor_ != NULL) { + cs_.Free(numeric_factor_); + numeric_factor_ = NULL; + } +} + +} // namespace internal +} // namespace ceres + +#endif // CERES_NO_CXSPARSE |