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  /external/jmonkeyengine/engine/src/core-effects/com/jme3/post/filters/
DepthOfFieldFilter.java 133 * down. A value of 1 (the default) performs a sparse 5x5 evenly
138 * The sparse convolution is as follows:
  /external/regex-re2/util/
sparse_array.h 39 // Representation for Sparse Sets, ACM Letters on Programming Languages
49 // Briggs & Torczon describe a sparse set implementation. I have
50 // trivially generalized it to create a sparse array (actually the original
56 // size max_size_. At any point, the number of elements in the sparse array is
59 // The vector dense_ contains the size_ elements in the sparse array (with
86 // To make the sparse array as efficient as possible for non-primitive types,
87 // elements may or may not be destroyed when they are deleted from the sparse
154 // Can sort the sparse array so that future iterations
sparse_set.h 38 // Representation for Sparse Sets, ACM Letters on Programming Languages
41 // For a generalization to sparse array, see sparse_array.h.
162 // Can sort the sparse array so that future iterations
  /external/smali/baksmali/src/main/java/org/jf/baksmali/Adaptors/Format/
SparseSwitchMethodItem.java 81 writer.write(".sparse-switch\n");
90 writer.write(".end sparse-switch");
  /system/core/toolbox/
dd.h 66 uint64_t sparse; /* # of sparse output blocks */ member in struct:__anon44024
  /external/ceres-solver/internal/ceres/
sparse_normal_cholesky_solver.cc 91 LOG(FATAL) << "Unknown sparse linear algebra library : "
95 LOG(FATAL) << "Unknown sparse linear algebra library : "
121 // Wrap the augmented Jacobian in a compressed sparse column matrix.
125 // using a sparse Cholesky factorization. Notice that when compared
evaluator.h 98 // Build and return a sparse matrix for storing and working with the Jacobian
101 // sparse. Since the sparsity pattern of the Jacobian remains constant over
108 // of their client's requirements for the kind of sparse matrix storage and
matrix.proto 63 // A block sparse matrix, either in column major or row major format.
104 // A sparse matrix. It is a union; only one field is permitted. If new sparse
schur_complement_solver.h 90 // complement matrix is large and sparse. It requires that
92 // sparse Cholesky factorization of the Schur complement. This solver
154 // Sparse Cholesky factorization based solver.
triplet_sparse_matrix.h 45 // manipulate sparse matrices in triplet (i,j,s) form. This object is
110 // Build a sparse diagonal matrix of size num_rows x num_rows from
112 // sparse matrix.
program_evaluator.h 38 // jacobian blocks in the passed sparse matrix.
41 // supporting writing to multiple sparse matrix formats. For example, when the
42 // ProgramEvaluator is parameterized for writing to block sparse matrices, the
44 // block sparse matrix by the user's CostFunction; there is no copying.
69 // // larger sparse jacobian.
  /external/e2fsprogs/util/
Makefile.in 44 copy-sparse
  /external/eigen/Eigen/src/SparseCore/
AmbiVector.h 18 * Hybrid sparse/dense vector class designed for intensive read-write operations.
68 // that we can handle dense vector even in sparse mode.
236 eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
360 Index m_currentEl; // the current element in sparse/linked-list mode
CoreIterators.h 20 * \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
  /external/webkit/Source/WebCore/manual-tests/
array-out-of-memory.html 13 // In order to force arr[target] to be stored in the vector, rather than the sparse map, we need ensure the vector is sufficiently densely populated.
  /sdk/eclipse/plugins/com.android.ide.eclipse.gldebugger/src/com/android/ide/eclipse/gltrace/state/transforms/
SparseArrayElementAddTransform.java 24 * element to a sparse array, if there is no item with the same key already.
  /system/core/libsparse/
Android.mk 8 sparse.c \
  /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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introduction.tex 4 Ceres Solver\footnote{For brevity, in the rest of this document we will just use the term Ceres.} is a non-linear least squares solver developed at Google. It is designed to solve small and large sparse problems accurately and efficiently~\footnote{For a gentle but brief introduction to non-liner least squares problems, please start by reading the~\hyperref[part:tutorial]{Tutorial}}. Amongst its various features is a simple but expressive API with support for automatic differentiation, robust norms, local parameterizations, automatic gradient checking, multithreading and automatic problem structure detection.
6 The key computational cost when solving a non-linear least squares problem is the solution of a linear least squares problem in each iteration. To this end Ceres supports a number of different linear solvers suited for different needs. This includes dense QR factorization (using \eigen) for small scale problems, sparse Cholesky factorization (using \texttt{SuiteSparse}) for general sparse problems and specialized Schur complement based solvers for problems that arise in multi-view geometry~\cite{hartley-zisserman-book-2004}.
  /external/eigen/test/
CMakeLists.txt 21 set(EIGEN_TEST_MATRIX_DIR "" CACHE STRING "Enable testing of realword sparse matrices contained in the specified path")
24 message(STATUS "Test realworld sparse matrices: ${EIGEN_TEST_MATRIX_DIR}")
238 ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags: ${SPARSE_LIBS}\n")
  /external/eigen/test/eigen2/
eigen2_sparse_product.cpp 10 #include "sparse.h"
40 // sparse * dense
46 // dense * sparse
  /external/eigen/unsupported/test/
kronecker_product.cpp 12 #include "sparse.h"
87 // DM = dense matrix; SM = sparse matrix
152 // test kroneckerProduct(SM,SM,SM) with sparse pattern
  /external/openfst/src/include/fst/
sparse-power-weight.h 0 // sparse-power-weight.h
28 #include <fst/sparse-tuple-weight.h>
70 // Sparse cartesian power semiring: W ^ n
  /external/smali/dexlib/src/main/java/org/jf/dexlib/Code/Format/
SparseSwitchDataPseudoInstruction.java 58 throw new RuntimeException("The sparse-switch data must contain at least 1 key/target");
62 throw new RuntimeException("The sparse-switch data contains too many elements. " +
118 "sparse-switch-data instruction");
  /external/v8/test/mjsunit/
sparse-array-reverse.js 39 // Simple test of reverse on sparse array.
59 // CONG pseudo random number generator. Used for fuzzing the sparse array
70 // Fuzzing test of reverse on sparse array.

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