| /external/eigen/Eigen/src/SparseCore/ |
| SparseTranspose.h | 15 template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
29 // NOTE: VC10 trigger an ICE if don't put typename TransposeImpl<MatrixType,Sparse>:: in front of Index,
30 // a typedef typename TransposeImpl<MatrixType,Sparse>::Index Index;
33 template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::InnerIterator
39 EIGEN_STRONG_INLINE InnerIterator(const TransposeImpl& trans, typename TransposeImpl<MatrixType,Sparse>::Index outer)
42 inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col(); }
43 inline typename TransposeImpl<MatrixType,Sparse>::Index col() const { return Base::row(); }
46 template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::ReverseInnerIterator
52 EIGEN_STRONG_INLINE ReverseInnerIterator(const TransposeImpl& xpr, typename TransposeImpl<MatrixType,Sparse>::Index outer)
55 inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col();
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| SparseCwiseUnaryOp.h | 16 class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>
34 class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::InnerIterator
35 : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator
38 typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator Base;
57 class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::ReverseInnerIterator
58 : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator
61 typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator Base;
80 class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>
98 class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::InnerIterator
99 : public CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeIterato
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| SparseCwiseBinaryOp.h | 16 // 1 - sparse op dense
17 // 2 - dense op sparse
18 // 3 - sparse op sparse
23 // 1 - sparse op dense product sparse
25 // 2 - dense op sparse product sparse
27 // 3 - sparse op sparse product spars
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| SparseView.h | 22 typedef Sparse StorageKind;
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| SparseUtil.h | 96 template<typename T> struct eval<T,Sparse>
127 template<typename T> struct plain_matrix_type<T,Sparse>
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| SparseDiagonalProduct.h | 15 // The product of a diagonal matrix with a sparse matrix can be easily
18 // 1 - diag * row-major sparse
19 // => each inner vector <=> scalar * sparse vector product
21 // 2 - diag * col-major sparse
22 // => each inner vector <=> densevector * sparse vector cwise product
37 typedef Sparse StorageKind;
86 eigen_assert(lhs.cols() == rhs.rows() && "invalid sparse matrix * diagonal matrix product");
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| SparseProduct.h | 73 typedef Sparse StorageKind;
156 // sparse = sparse * sparse
165 /** \returns an expression of the product of two sparse matrices.
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| SparseDenseProduct.h | 40 typedef Sparse StorageKind;
257 // dense = dense * sparse
289 // sparse * dense
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| SparseVector.h | 18 * \brief a sparse vector class
34 typedef Sparse StorageKind;
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| SparseMatrix.h | 19 * \brief A versatible sparse matrix representation
47 typedef Sparse StorageKind;
558 /** Constructs a sparse matrix from the sparse expression \a other */
575 /** Swaps the content of two sparse matrices of the same type.
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| /external/llvm/include/llvm/ADT/ |
| SparseSet.h | 1 //===--- llvm/ADT/SparseSet.h - Sparse set ----------------------*- C++ -*-===//
11 // Briggs, Torczon, "An efficient representation for sparse sets", ACM Letters
14 // A sparse set holds a small number of objects identified by integer keys from
15 // a moderately sized universe. The sparse set uses more memory than other
97 /// SparseSet contains a dense vector holding all the objects and a sparse
99 /// the sparse array which is the size of the key universe. The SparseT
103 /// When SparseT is uint32_t, find() only touches 2 cache lines, but the sparse
107 /// lines, but the sparse array is 4x smaller. N is the number of elements in
124 SparseT *Sparse;
141 SparseSet() : Sparse(0), Universe(0) {
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| SparseMultiSet.h | 1 //===--- llvm/ADT/SparseMultiSet.h - Sparse multiset ------------*- C++ -*-===//
13 // A sparse multiset holds a small number of objects identified by integer keys
14 // from a moderately sized universe. The sparse multiset uses more memory than
47 /// SparseMultiSet contains a dense vector holding all the objects and a sparse
49 /// the sparse array which is the size of the key universe. The SparseT template
53 /// sparse array uses 4 x Universe bytes.
56 /// lines, but the sparse array is 4x smaller. N is the number of elements in
112 SparseT *Sparse;
186 : Sparse(0), Universe(0), FreelistIdx(SMSNode::INVALID), NumFree(0) { }
188 ~SparseMultiSet() { free(Sparse); }
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| /external/eigen/doc/special_examples/ |
| Tutorial_sparse_example.cpp | 1 #include <Eigen/Sparse>
4 typedef Eigen::SparseMatrix<double> SpMat; // declares a column-major sparse matrix type of double
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| Tutorial_sparse_example_details.cpp | 1 #include <Eigen/Sparse>
5 typedef Eigen::SparseMatrix<double> SpMat; // declares a column-major sparse matrix type of double
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| /external/eigen/bench/ |
| sparse_lu.cpp | 6 #include <Eigen/Sparse>
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| BenchSparseUtil.h | 2 #include <Eigen/Sparse>
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| sparse_cholesky.cpp | 4 #include <Eigen/Sparse>
74 // std::cout << "sparse\n" << chol.matrixL() << "%\n";
93 std::cout << "Generate sparse matrix (might take a while)...\n";
126 // eigen sparse matrices
127 doEigen<Eigen::DefaultBackend>("Eigen/Sparse", sm1, Eigen::IncompleteFactorization);
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| /external/eigen/test/eigen2/ |
| sparse.h | 28 #include <Eigen/Sparse>
37 /* Initializes both a sparse and dense matrix with same random values,
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| /external/eigen/test/ |
| sparse.h | 40 #include <Eigen/Sparse>
49 /* Initializes both a sparse and dense matrix with same random values,
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| /external/eigen/unsupported/Eigen/src/SparseExtra/ |
| DynamicSparseMatrix.h | 19 * \brief A sparse matrix class designed for matrix assembly purpose
41 typedef Sparse StorageKind;
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| /external/v8/test/mjsunit/ |
| array-indexing.js | 30 // Sparse arrays with length 42000.
79 // Find in sparse array.
146 // Find in sparse array.
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| /external/eigen/unsupported/Eigen/src/Skyline/ |
| SkylineMatrix.h | 37 typedef Sparse StorageKind;
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| /external/ceres-solver/docs/ |
| solving.tex | 237 \subsection{\texttt{DENSE\_NORMAL\_CHOLESKY} \& \texttt{SPARSE\_NORMAL\_CHOLESKY}}
238 Large non-linear least square problems are usually sparse. In such cases, using a dense QR factorization is inefficient. Let $H = R^\top R$ be the Cholesky factorization of the normal equations, where $R$ is an upper triangular matrix, then the solution to ~\eqref{eq:normal} is given by
245 implies that $J^\top J = R^\top Q^\top Q R = R^\top R$. There are two variants of Cholesky factorization -- sparse and
252 \texttt{SPARSE\_NORMAL\_CHOLESKY}, as the name implies performs a
253 sparse Cholesky factorization of the normal equations. This leads to
254 substantial savings in time and memory for large sparse
255 problems. Ceres uses the sparse Cholesky factorization routines in Professor Tim Davis' \texttt{SuiteSparse} or
258 \subsection{\texttt{DENSE\_SCHUR} \& \texttt{SPARSE\_SCHUR}}
259 While it is possible to use \texttt{SPARSE\_NORMAL\_CHOLESKY} to solve bundle adjustment problems, bundle adjustment problem have a special structure, and a more efficient scheme for solving~\eqref{eq:normal} can be constructed.
271 where, $B \in \reals^{pc\times pc}$ is a block sparse matrix with $p$ blocks of size $c\times c$ and $C \in \reals^{qs\times qs}$ is a block diagonal m (…)
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