diff options
Diffstat (limited to 'internal/ceres/cxsparse.cc')
-rw-r--r-- | internal/ceres/cxsparse.cc | 120 |
1 files changed, 102 insertions, 18 deletions
diff --git a/internal/ceres/cxsparse.cc b/internal/ceres/cxsparse.cc index 21c98e0..c6d7743 100644 --- a/internal/ceres/cxsparse.cc +++ b/internal/ceres/cxsparse.cc @@ -32,7 +32,10 @@ #include "ceres/cxsparse.h" +#include <vector> +#include "ceres/compressed_col_sparse_matrix_utils.h" #include "ceres/compressed_row_sparse_matrix.h" +#include "ceres/internal/port.h" #include "ceres/triplet_sparse_matrix.h" #include "glog/logging.h" @@ -44,45 +47,46 @@ CXSparse::CXSparse() : scratch_(NULL), scratch_size_(0) { CXSparse::~CXSparse() { if (scratch_size_ > 0) { - cs_free(scratch_); + cs_di_free(scratch_); } } + bool CXSparse::SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b) { // Make sure we have enough scratch space available. if (scratch_size_ < A->n) { if (scratch_size_ > 0) { - cs_free(scratch_); + cs_di_free(scratch_); } - scratch_ = reinterpret_cast<CS_ENTRY*>(cs_malloc(A->n, sizeof(CS_ENTRY))); + scratch_ = + reinterpret_cast<CS_ENTRY*>(cs_di_malloc(A->n, sizeof(CS_ENTRY))); + scratch_size_ = A->n; } // Solve using Cholesky factorization - csn* numeric_factorization = cs_chol(A, symbolic_factorization); + csn* numeric_factorization = cs_di_chol(A, symbolic_factorization); if (numeric_factorization == NULL) { LOG(WARNING) << "Cholesky factorization failed."; return false; } - // When the Cholesky factorization succeeded, these methods are guaranteed to - // succeeded as well. In the comments below, "x" refers to the scratch space. + // When the Cholesky factorization succeeded, these methods are + // guaranteed to succeeded as well. In the comments below, "x" + // refers to the scratch space. // // Set x = P * b. - cs_ipvec(symbolic_factorization->pinv, b, scratch_, A->n); - + cs_di_ipvec(symbolic_factorization->pinv, b, scratch_, A->n); // Set x = L \ x. - cs_lsolve(numeric_factorization->L, scratch_); - + cs_di_lsolve(numeric_factorization->L, scratch_); // Set x = L' \ x. - cs_ltsolve(numeric_factorization->L, scratch_); - + cs_di_ltsolve(numeric_factorization->L, scratch_); // Set b = P' * x. - cs_pvec(symbolic_factorization->pinv, scratch_, b, A->n); + cs_di_pvec(symbolic_factorization->pinv, scratch_, b, A->n); // Free Cholesky factorization. - cs_nfree(numeric_factorization); + cs_di_nfree(numeric_factorization); return true; } @@ -91,6 +95,72 @@ cs_dis* CXSparse::AnalyzeCholesky(cs_di* A) { 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); + 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_factorization = + reinterpret_cast<cs_dis*>(cs_calloc(1, sizeof(cs_dis))); + symbolic_factorization->pinv = cs_pinv(&scalar_ordering[0], A->n); + cs* permuted_A = cs_symperm(A, symbolic_factorization->pinv, 0); + + symbolic_factorization->parent = cs_etree(permuted_A, 0); + int* postordering = cs_post(symbolic_factorization->parent, A->n); + int* column_counts = cs_counts(permuted_A, + symbolic_factorization->parent, + postordering, + 0); + cs_free(postordering); + cs_spfree(permuted_A); + + symbolic_factorization->cp = (int*) cs_malloc(A->n+1, sizeof(int)); + symbolic_factorization->lnz = cs_cumsum(symbolic_factorization->cp, + column_counts, + A->n); + symbolic_factorization->unz = symbolic_factorization->lnz; + + cs_free(column_counts); + + if (symbolic_factorization->lnz < 0) { + cs_sfree(symbolic_factorization); + symbolic_factorization = NULL; + } + + return symbolic_factorization; +} + cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) { cs_di At; At.m = A->num_cols(); @@ -116,12 +186,26 @@ cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) { return cs_compress(&tsm_wrapper); } -void CXSparse::Free(cs_di* factor) { - cs_free(factor); +void CXSparse::ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering) { + int* cs_ordering = cs_amd(1, A); + 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* factor) { - cs_sfree(factor); +void CXSparse::Free(cs_dis* symbolic_factorization) { + cs_di_sfree(symbolic_factorization); } } // namespace internal |