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Searched
refs:Conjugate
(Results
1 - 9
of
9
) sorted by null
/external/eigen/Eigen/src/Core/products/
TriangularSolverMatrix_MKL.h
42
template <typename Index, int Mode, bool
Conjugate
, int TriStorageOrder> \
43
struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,
Conjugate
,TriStorageOrder,ColMajor> \
49
conjA = ((TriStorageOrder==ColMajor) &&
Conjugate
) ? 1 : 0 \
66
transa = (TriStorageOrder==RowMajor) ? ((
Conjugate
) ? 'C' : 'T') : 'N'; \
76
a_tmp = tri.
conjugate
(); \
97
template <typename Index, int Mode, bool
Conjugate
, int TriStorageOrder> \
98
struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,
Conjugate
,TriStorageOrder,ColMajor> \
104
conjA = ((TriStorageOrder==ColMajor) &&
Conjugate
) ? 1 : 0 \
121
transa = (TriStorageOrder==RowMajor) ? ((
Conjugate
) ? 'C' : 'T') : 'N'; \
131
a_tmp = tri.
conjugate
(); \
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TriangularSolverVector.h
17
template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool
Conjugate
, int StorageOrder>
18
struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode,
Conjugate
, StorageOrder>
24
Conjugate
,StorageOrder==RowMajor?ColMajor:RowMajor
30
template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool
Conjugate
>
31
struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode,
Conjugate
, RowMajor>
41
Conjugate
,
61
general_matrix_vector_product<Index,LhsScalar,RowMajor,
Conjugate
,RhsScalar,false>::run(
84
template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool
Conjugate
>
85
struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode,
Conjugate
, ColMajor>
94
typename internal::conditional<
Conjugate
,
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TriangularSolverMatrix.h
18
template <typename Scalar, typename Index, int Side, int Mode, bool
Conjugate
, int TriStorageOrder>
19
struct triangular_solve_matrix<Scalar,Index,Side,Mode,
Conjugate
,TriStorageOrder,RowMajor>
30
NumTraits<Scalar>::IsComplex &&
Conjugate
,
38
template <typename Scalar, typename Index, int Mode, bool
Conjugate
, int TriStorageOrder>
39
struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,
Conjugate
,TriStorageOrder,ColMajor>
68
conj_if<
Conjugate
> conj;
69
gebp_kernel<Scalar, Scalar, Index, Traits::mr, Traits::nr,
Conjugate
, false> gebp_kernel;
180
template <typename Scalar, typename Index, int Mode, bool
Conjugate
, int TriStorageOrder>
181
struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,
Conjugate
,TriStorageOrder,ColMajor>
211
conj_if<
Conjugate
> conj
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GeneralBlockPanelKernel.h
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/external/eigen/Eigen/src/Core/util/
BlasUtil.h
24
template<typename Scalar, typename Index, int nr, int StorageOrder, bool
Conjugate
= false, bool PanelMode=false>
27
template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool
Conjugate
= false, bool PanelMode = false>
41
template<bool
Conjugate
> struct conj_if;
150
* and
conjugate
*/
173
// pop
conjugate
ForwardDeclarations.h
86
template<typename MatrixType> class
Conjugate
;
/external/eigen/Eigen/src/Core/
SolveTriangular.h
19
template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool
Conjugate
, int StorageOrder>
22
template <typename Scalar, typename Index, int Side, int Mode, bool
Conjugate
, int TriStorageOrder, int OtherStorageOrder>
BandMatrix.h
94
Conjugate
= ReturnOpposite && NumTraits<Scalar>::IsComplex,
103
typedef typename internal::conditional<
Conjugate
,
/external/ceres-solver/docs/
solving.tex
81
An inexact Newton method requires two ingredients. First, a cheap method for approximately solving systems of linear equations. Typically an iterative linear solver like the
Conjugate
Gradients method is used for this purpose~\cite{nocedal2000numerical}. Second, a termination rule for the iterative solver. A typical termination rule is of the form
323
For general sparse problems, if the problem is too large for \texttt{CHOLMOD} or a sparse linear algebra library is not linked into Ceres, another option is the \texttt{CGNR} solver. This solver uses the
Conjugate
Gradients solver on the {\em normal equations}, but without forming the normal equations explicitly. It exploits the relation
345
The convergence rate of
Conjugate
Gradients for solving~\eqref{eq:normal} depends on the distribution of eigenvalues of $H$~\cite{saad2003iterative}. A useful upper bound is $\sqrt{\kappa(H)}$, where, $\kappa(H)$f is the condition number of the matrix $H$. For most bundle adjustment problems, $\kappa(H)$ is high and a direct application of
Conjugate
Gradients to~\eqref{eq:normal} results in extremely poor performance.
347
The solution to this problem is to replace~\eqref{eq:normal} with a {\em preconditioned} system. Given a linear system, $Ax =b$ and a preconditioner $M$ the preconditioned system is given by $M^{-1}Ax = M^{-1}b$. The resulting algorithm is known as Preconditioned
Conjugate
Gradients algorithm (PCG) and its worst case complexity now depends on the condition number of the {\em preconditioned} matrix $\kappa(M^{-1}A)$.
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