| /external/ceres-solver/internal/ceres/ |
| 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);
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| 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++] = 3
304 int nnz = 0; local
438 int nnz = 0; local
[all...] |
| 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;
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| /external/eigen/Eigen/src/OrderingMethods/ |
| 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];
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| 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)
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 = p [n_col]
[all...] |
| /external/eigen/Eigen/src/SparseCore/ |
| 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<200 && nnz<t200) || nnz * log2(nnz) < t
[all...] |
| 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), &matrix.data().value(end), tail_size*sizeof(Scalar))
[all...] |
| SparseSelfAdjointView.h | 351 Index nnz = count.sum(); local
354 dest.resizeNonZeros(nnz);
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| /external/eigen/bench/ |
| 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++
[all...] |
| /external/eigen/unsupported/Eigen/src/IterativeSolvers/ |
| 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
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| /external/eigen/Eigen/src/SparseLU/ |
| 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);
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| /external/eigen/Eigen/src/SuperLUSupport/ |
| SuperLUSupport.h | 114 union {int nnz;int lda;}; member in union:Eigen::SluMatrix::__anon24810::__anon24811
186 res.storage.nnz = mat.nonZeros();
245 res.storage.nnz = mat.nonZeros();
704 m_l.resizeNonZeros(Lstore->nnz);
706 m_u.resizeNonZeros(Ustore->nnz);
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