HomeSort by relevance Sort by last modified time
    Searched full:solvers (Results 1 - 25 of 66) sorted by null

1 2 3

  /external/eigen/Eigen/
PardisoSupport 15 * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
IterativeLinearSolvers 13 * Those solvers are accessible via the following classes:
17 * These iterative solvers are associated with some preconditioners:
Eigenvalues 18 * This module mainly provides various eigenvalue solvers.
  /external/eigen/
README.android 5 numerical solvers, and related algorithms.
  /external/ceres-solver/internal/ceres/
linear_solver.h 122 // parameter only makes sense for iterative solvers like CG.
138 // solvers have differing requirements on elimination_groups.
140 // The most common use is for Schur type solvers, where there
144 // num_eliminate_blocks in the Schur type solvers.
147 // Iterative solvers, e.g. Preconditioned Conjugate Gradients
155 // some cases the Schur complement based solvers can detect and
177 // This option only makes sense for unsymmetric linear solvers
202 // This option only makes sense for iterative solvers.
226 // iterative solvers. Direct solvers ignore them
    [all...]
preconditioner.h 76 // solvers have differing requirements on elimination_groups.
78 // The most common use is for Schur type solvers, where there
82 // num_eliminate_blocks in the Schur type solvers.
86 // some cases the Schur complement based solvers can detect and
block_jacobi_preconditioner.h 45 // conjugate gradients, or other iterative symmetric solvers. To use
iterative_schur_complement_solver.h 60 // memory. Further, iterative solvers like this open up the
parameter_block_ordering.h 47 // complement based solvers. The output variable ordering contains an
reorder_program.h 58 // Schur type solvers require that all parameter blocks eliminated
schur_jacobi_preconditioner.h 83 // based solvers. Please see schur_eliminator.h for more details.
system_test.cc 37 // solvers. The second problem is used to test the various
38 // combinations of solvers, orderings, preconditioners and
540 // Single threaded evaluators and linear solvers.
546 // Multithreaded evaluators and linear solvers.
schur_complement_solver.h 60 // solvers. It assumes that the input linear system Ax = b can be
104 // The two solvers can be instantiated by calling
  /external/ceres-solver/docs/source/
features.rst 46 - **Trust Region Solvers** - Ceres supports Levenberg-Marquardt,
50 solvers - dense QR and dense Cholesky factorization (using
54 iterative linear solvers for `bundle adjustment`_ problems.
56 - **Line Search Solvers** - When the problem size is so large that
64 multithreading of the Jacobian evaluation and the linear solvers.
68 non-linear least squares solvers.
72 matrix. Ceres is one of the few solvers that allows you to to do
  /libcore/luni/src/main/java/java/util/concurrent/
ExecutorCompletionService.java 20 * Suppose you have a set of solvers for a certain problem, each
28 * Collection<Callable<Result>> solvers)
32 * for (Callable<Result> s : solvers)
34 * int n = solvers.size();
48 * Collection<Callable<Result>> solvers)
52 * int n = solvers.size();
57 * for (Callable<Result> s : solvers)
  /external/ceres-solver/scripts/
ceres-solver.spec 41 - Threaded Jacobian evaluators and linear solvers
42 - Trust region solvers with non-monotonic steps (Levenberg-Marquardt and Dogleg (Powell & Subspace))
43 - Line search solvers (L-BFGS and Nonlinear CG)
46 - Specialized solvers for bundle adjustment problems in computer vision
47 - Iterative linear solvers for general sparse and bundle adjustment problems
  /external/eigen/doc/
SparseLinearSystems.dox 3 In Eigen, there are several methods available to solve linear systems when the coefficient matrix is sparse. Because of the special representation of this class of matrices, special care should be taken in order to get a good performance. See \ref TutorialSparse for a detailed introduction about sparse matrices in Eigen. This page lists the sparse solvers available in Eigen. The main steps that are common to all these linear solvers are introduced as well. Depending on the properties of the matrix, the desired accuracy, the end-user is able to tune those steps in order to improve the performance of its code. Note that it is not required to know deeply what's hiding behind these steps: the last section presents a benchmark routine that can be easily used to get an insight on the performance of all the available solvers.
7 \section TutorialSparseDirectSolvers Sparse solvers
9 %Eigen currently provides a limited set of built-in solvers, as well as wrappers to external solver libraries.
33 <tr> <th colspan="7"> Wrappers to external solvers </th></tr>
53 All these solvers follow the same general concept.
78 For \c SPD solvers, a second optional template argument allows to specify which triangular part have to be used, e.g.:
108 In the compute() function, the matrix is generally factorized: LLT for self-adjoint matrices, LDLT for general hermitian matrices, LU for non hermitian matrices and QR for rectangular matrices. These are the results of using direct solvers. For this class of solvers precisely, the compute step is further subdivided into analyzePattern() and factorize().
    [all...]
  /external/eigen/unsupported/Eigen/
IterativeSolvers 16 * \defgroup IterativeSolvers_Module Iterative solvers module
  /external/eigen/bench/spbench/
spbenchsolver.cpp 5 cout<< " \nbenchsolver : performs a benchmark of all the solvers available in Eigen \n\n";
18 cout<< " --eps <RelErr> Sets the relative tolerance for iterative solvers (default 1e-08) \n\n";
spbench.dtd 26 <!ELEMENT GLOBAL_PARAMS (#PCDATA)> <!-- Parameters shared by all solvers -->
spbenchsolver.h 82 double best_time_val; // Current best time overall solvers
104 // List all available solvers
493 RelErr = tol; //Relative residual error as stopping criterion for iterative solvers
529 // Call all suitable solvers for this linear system
  /external/eigen/unsupported/Eigen/src/IterativeSolvers/
IterationController.h 66 * \brief Controls the iterations of the iterative solvers
100 /* get/set the "noisyness" (verbosity) of the solvers */
  /external/ceres-solver/include/ceres/
types.h 62 // These solvers are for general rectangular systems formed from the
63 // normal equations A'A x = A'b. They are direct solvers and do not
78 // Specialized solvers, specific to problems with a generalized
287 // factorization based linear solvers, i.e., SPARSE_SCHUR,
iteration_callback.h 123 // number affects only the iterative solvers capable of solving
124 // linear systems inexactly. Factorization-based exact solvers
  /external/ceres-solver/examples/
bundle_adjuster.cc 32 // solvers and loss functions.
192 // ITERATIVE_SCHUR solvers make use of this specialized

Completed in 465 milliseconds

1 2 3