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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     [all...]
  /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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