/external/ceres-solver/internal/ceres/ |
H A D | triplet_sparse_matrix_test.cc | 300 int nnz = 0; local 303 m.mutable_rows()[nnz] = i; 304 m.mutable_cols()[nnz] = j; 305 m.mutable_values()[nnz++] = i+j; 308 m.set_num_nonzeros(nnz);
|
H A D | linear_least_squares_problems.cc | 196 int nnz = 0; local 200 rows[nnz] = 0; 201 cols[nnz] = 0; 202 values[nnz++] = 1; 204 rows[nnz] = 0; 205 cols[nnz] = 2; 206 values[nnz++] = 2; 211 rows[nnz] = 1; 212 cols[nnz] = 0; 213 values[nnz 304 int nnz = 0; local 438 int nnz = 0; local [all...] |
H A D | compressed_row_sparse_matrix.cc | 451 int nnz = 0; local 454 (*program)[product[0].index] = nnz; 461 crsm_cols[++nnz] = current.col; 467 (*program)[current.index] = nnz;
|
/external/eigen/Eigen/src/OrderingMethods/ |
H A D | Ordering.h | 131 Index nnz = mat.nonZeros(); local 133 Index Alen = internal::colamd_recommended(nnz, m, n); 141 for(Index i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
|
H A D | Eigen_Colamd.h | 196 the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro. It returns -1 if any 197 argument is negative. 2*nnz space is required for the row and column 201 and nnz/5 more space is recommended for run time efficiency. 258 * \param nnz nonzeros in A 264 inline Index colamd_recommended ( Index nnz, Index n_row, Index n_col) argument 266 if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0) 269 return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5)); 334 Index nnz ; /* nonzeros in A */ local 392 nnz [all...] |
/external/eigen/Eigen/src/SparseCore/ |
H A D | ConservativeSparseSparseProduct.h | 37 // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs) 47 Index nnz = 0; local 60 indices[nnz] = i; 61 ++nnz; 69 for(Index k=0; k<nnz; ++k) 82 // FIXME reserve nnz non zeros 83 // FIXME implement fast sort algorithms for very small nnz 88 //if((nnz<20 [all...] |
H A D | MappedSparseMatrix.h | 108 inline MappedSparseMatrix(Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr) argument 109 : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_nnz(nnz), m_outerIndex(outerIndexPtr),
|
H A D | SparseBlock.h | 135 Index nnz = tmp.nonZeros(); local 145 if(nnz>free_size) 148 typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz); 153 std::memcpy(&newdata.value(start), &tmp.data().value(0), nnz*sizeof(Scalar)); 154 std::memcpy(&newdata.index(start), &tmp.data().index(0), nnz*sizeof(Index)); 156 std::memcpy(&newdata.value(start+nnz), &matrix.data().value(end), tail_size*sizeof(Scalar)); 157 std::memcpy(&newdata.index(start+nnz), &matrix.data().index(end), tail_size*sizeof(Index)); 159 newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz); 166 matrix.data().resize(start + nnz + tail_size); 168 std::memmove(&matrix.data().value(start+nnz), [all...] |
H A D | SparseSelfAdjointView.h | 351 Index nnz = count.sum(); local 354 dest.resizeNonZeros(nnz);
|
/external/eigen/bench/ |
H A D | sparse_setter.cpp | 107 std::cout << "nnz = " << coords.size() << "\n"; 302 const int nnz, 312 for (int n = 0; n < nnz; n++){ 316 //cumsum the nnz per row to get Bp[] 322 Bp[n_row] = nnz; 325 for(int n = 0; n < nnz; n++){ 384 I nnz = 0; local 397 Aj[nnz] = j; 398 Ax[nnz] = x; 399 nnz 300 coo_tocsr(const int n_row, const int n_col, const int nnz, const Coordinates Aij, const Values Ax, int Bp[], int Bj[], T Bx[]) argument [all...] |
/external/eigen/unsupported/Eigen/src/IterativeSolvers/ |
H A D | IncompleteCholesky.h | 147 Index nnz = m_L.nonZeros(); local 148 Map<ScalarType> vals(m_L.valuePtr(), nnz); //values 149 Map<IndexType> rowIdx(m_L.innerIndexPtr(), nnz); //Row indices
|
/external/eigen/Eigen/src/SparseLU/ |
H A D | SparseLU.h | 493 Index nnz = m_mat.nonZeros(); local 497 Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu);
|
/external/eigen/Eigen/src/SuperLUSupport/ |
H A D | SuperLUSupport.h | 114 union {int nnz;int lda;}; member in union:Eigen::SluMatrix::__anon21001::__anon21002 186 res.storage.nnz = mat.nonZeros(); 245 res.storage.nnz = mat.nonZeros(); 704 m_l.resizeNonZeros(Lstore->nnz); 706 m_u.resizeNonZeros(Ustore->nnz);
|