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    Searched refs:JACOBI (Results 1 - 9 of 9) sorted by null

  /external/ceres-solver/internal/ceres/
iterative_schur_complement_solver.cc 71 options_.preconditioner_type == JACOBI));
97 case JACOBI:
types.cc 77 CASESTR(JACOBI);
89 STRENUM(JACOBI);
cgnr_solver.cc 59 if (options_.preconditioner_type == JACOBI) {
66 LOG(FATAL) << "CGNR only supports IDENTITY and JACOBI preconditioners.";
linear_solver.h 74 preconditioner_type(JACOBI),
system_test.cc 502 CONFIGURE(CGNR, SUITE_SPARSE, kAutomaticOrdering, JACOBI);
503 CONFIGURE(ITERATIVE_SCHUR, SUITE_SPARSE, kUserOrdering, JACOBI);
511 CONFIGURE(ITERATIVE_SCHUR, SUITE_SPARSE, kAutomaticOrdering, JACOBI);
solver_impl.cc 748 "to CGNR with JACOBI preconditioner.",
753 // CGNR currently only supports the JACOBI preconditioner.
754 options->preconditioner_type = JACOBI;
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  /external/ceres-solver/include/ceres/
types.h 101 JACOBI,
solver.h 85 preconditioner_type = JACOBI;
410 // Normalize the jacobian using Jacobi scaling before calling
  /external/ceres-solver/docs/
solving.tex 349 The computational cost of using a preconditioner $M$ is the cost of computing $M$ and evaluating the product $M^{-1}y$ for arbitrary vectors $y$. Thus, there are two competing factors to consider: How much of $H$'s structure is captured by $M$ so that the condition number $\kappa(HM^{-1})$ is low, and the computational cost of constructing and using $M$. The ideal preconditioner would be one for which $\kappa(M^{-1}A) =1$. $M=A$ achieves this, but it is not a practical choice, as applying this preconditioner would require solving a linear system equivalent to the unpreconditioned problem. It is usually the case that the more information $M$ has about $H$, the more expensive it is use. For example, Incomplete Cholesky factorization based preconditioners have much better convergence behavior than the Jacobi preconditioner, but are also much more expensive.
352 The simplest of all preconditioners is the diagonal or Jacobi preconditioner, \ie, $M=\operatorname{diag}(A)$, which for block structured matrices like $H$ can be generalized to the block Jacobi preconditioner.
354 For \texttt{ITERATIVE\_SCHUR} there are two obvious choices for block diagonal preconditioners for $S$. The block diagonal of the matrix $B$~\cite{mandel1990block} and the block diagonal $S$, \ie the block Jacobi preconditioner for $S$. Ceres's implements both of these preconditioners and refers to them as \texttt{JACOBI} and \texttt{SCHUR\_JACOBI} respectively.
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