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  /external/ceres-solver/internal/ceres/
linear_operator.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
normal_prior.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
polynomial_solver.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
random.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
scratch_evaluate_preparer.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
scratch_evaluate_preparer.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
test_util.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
CMakeLists.txt 1 # Ceres Solver - A fast non-linear least squares minimizer
3 # http://code.google.com/p/ceres-solver/
79 solver.cc
106 # Only the fully dynamic solver. The build is much faster this way.
implicit_schur_complement_test.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
161 // Backsubstituted solution from the implicit schur solver using the
186 // by the SchurComplement solver.
partitioned_matrix_view.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
45 // structure as required by the Schur complement based solver, found
52 // the Schur complement solver it will result in unpredictable and
schur_complement_solver_test.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
107 scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
115 summary = solver->Solve(A.get(), b.get(), per_solve_options, x.get());
trust_region_minimizer_test.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
187 // if the solver converges to the optimal values or not.
190 Solver::Options solver_options;
225 Solver::Summary summary;
364 Solver::Options options;
366 Solver::Summary summary;
conjugate_gradients_solver.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
31 // A preconditioned conjugate gradients solver
36 // solver to support not just residual based termination but also
193 // the solver terminates at iteration i if
  /external/ceres-solver/docs/
changes.tex 1 %!TEX root = ceres-solver.tex
47 convergence behavior of the solver at a small increase in run time.
92 \item New subspace Dogleg Solver. (Markus Moll)
96 \item New \texttt{DENSE\_NORMAL\_CHOLESKY} linear solver, which uses Eigen's
102 \item The traditional Dogleg solver now uses an elliptical trust
107 \item A better way to compute \texttt{Solver::Summary::fixed\_cost}. (Markus Moll)
117 \item Solver progress logging to file.
152 \item Fix an early return bug in the Dogleg solver. (Markus Moll)
188 \item Powell's Dogleg solver
233 \item New iterative linear solver for general sparse problems - \texttt{CGNR} and a block Jacobi preconditioner for it
    [all...]
bundleadjustment.tex 1 %!TEX root = ceres-solver.tex
91 One way to solve this problem is to set \texttt{Solver::Options::linear\_solver\_type} to \texttt{SPARSE\_NORMAL\_CHOLESKY} and call \texttt{Solve}. And while this is a reasonable thing to do, bundle adjustment problems have a special sparsity structure that can be exploited to solve them much more efficiently. Ceres provides three specialized solvers (collectively known as Schur based solvers) for this task. The example code uses the simplest of them \texttt{DENSE\_SCHUR}.
93 ceres::Solver::Options options;
96 ceres::Solver::Summary summary;
faq.tex 1 %!TEX root = ceres-solver.tex
45 Notice that out of the 20 cells, only 8 are non-zero, in fact out of the 288 entries only 48 entries are non-zero, thus we are hiding substantial sparsity from the solver, and using up much more memory. It is much better to break this up into 5 residual blocks. One residual block of size 2 that depends on all four parameter block and four residual blocks of size 4 each that depend on one parameter block at a time.
67 No. Ceres was designed from the grounds up to be a non-linear least squares solver. Currently we have no plans of extending it into a general purpose non-linear solver.
  /external/eigen/Eigen/
PaStiXSupport 22 * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
  /external/eigen/unsupported/Eigen/
IterativeSolvers 17 * This module aims to provide various iterative linear and non linear solver algorithms.
  /external/ceres-solver/examples/
data_fitting.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
37 using ceres::Solver;
154 Solver::Options options;
159 Solver::Summary summary;
denoising.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
59 DEFINE_bool(verbose, false, "Prints information about the solver progress.");
141 ceres::Solver::Options options;
150 ceres::Solver::Summary summary;
simple_bundle_adjuster.cc 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
200 // standard solver, SPARSE_NORMAL_CHOLESKY, also works fine but it is slower
202 ceres::Solver::Options options;
206 ceres::Solver::Summary summary;
Makefile.example 1 # Ceres Solver - A fast non-linear least squares minimizer
3 # http://code.google.com/p/ceres-solver/
bal_problem.h 1 // Ceres Solver - A fast non-linear least squares minimizer
3 // http://code.google.com/p/ceres-solver/
  /external/eigen/doc/
C09_TutorialSparse.dox 246 %Eigen currently provides a limited set of built-in solvers, as well as wrappers to external solver libraries.
250 <tr><th>Class</th><th>Module</th><th>Solver kind</th><th>Matrix kind</th><th>Features related to performance</th>
292 SolverClassName<SparseMatrix<double> > solver;
293 solver.compute(A);
294 if(solver.info()!=Succeeded) {
298 x = solver.solve(b);
299 if(solver.info()!=Succeeded) {
304 x1 = solver.solve(b1);
312 ConjugateGradient<SparseMatrix<double>, Eigen::Upper> solver;
313 x = solver.compute(A).solve(b)
    [all...]
  /external/v8/benchmarks/
navier-stokes.js 33 var solver = null; variable
37 solver.update();
42 solver = new FluidField(null);
43 solver.setResolution(128, 128);
44 solver.setIterations(20);
45 solver.setDisplayFunction(function(){});
46 solver.setUICallback(prepareFrame);
47 solver.reset();
52 solver = null;

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