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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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all
...]
/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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