| /external/eigen/doc/ |
| AsciiQuickReference.txt | 203 A.eigenvalues(); // eig(A);
205 eig.eigenvalues(); // diag(val)
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| QuickReference.dox | 23 <tr><td>\link Eigenvalues_Module Eigenvalues \endlink</td><td>\code#include <Eigen/Eigenvalues>\endcode</td><td>Eigenvalue, eigenvector decompositions (EigenSolver, SelfAdjointEigenSolver, ComplexEigenSolver)</td></tr>
25 <tr><td></td><td>\code#include <Eigen/Dense>\endcode</td><td>Includes Core, Geometry, LU, Cholesky, SVD, QR, and Eigenvalues header files</td></tr>
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| A05_PortingFrom2To3.dox | 192 <td>\code #include<Eigen/Eigenvalues> \endcode </td>
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| SparseLinearSystems.dox | 121 For iterative solvers, the compute step is used to eventually setup a preconditioner. For instance, with the ILUT preconditioner, the incomplete factors L and U are computed in this step. Remember that, basically, the goal of the preconditioner is to speedup the convergence of an iterative method by solving a modified linear system where the coefficient matrix has more clustered eigenvalues. For real problems, an iterative solver should always be used with a preconditioner. In Eigen, a preconditioner is selected by simply adding it as a template parameter to the iterative solver object.
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| Doxyfile.in | 211 "eigenvalues_module=This is defined in the %Eigenvalues module. \code #include <Eigen/Eigenvalues> \endcode" \
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| /external/eigen/unsupported/Eigen/src/MatrixFunctions/ |
| MatrixPower.h | 144 * loop. We should move 0 eigenvalues to bottom right corner. We need not
148 * If the 0 eigenvalues are semisimple, they can form a 0 matrix at the
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| MatrixSquareRoot.h | 134 // pre: T.block(i,i,2,2) has complex conjugate eigenvalues
140 // TODO: This case (2-by-2 blocks with complex conjugate eigenvalues) is probably hidden somewhere
145 = (es.eigenvectors() * es.eigenvalues().cwiseSqrt().asDiagonal() * es.eigenvectors().inverse()).real();
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| MatrixLogarithm.h | 80 /** \brief Compute logarithm of triangular matrix with clustered eigenvalues. */
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| /external/ceres-solver/internal/ceres/ |
| low_rank_inverse_hessian.cc | 150 // Where \lambda_1 & \lambda_m are the smallest and largest eigenvalues of
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| line_search_direction.cc | 233 // Where \lambda_1 & \lambda_m are the smallest and largest eigenvalues
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| /external/eigen/unsupported/Eigen/src/Polynomials/ |
| Companion.h | 125 * "Balancing a matrix for calculation of eigenvalues and eigenvectors"
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| /external/eigen/Eigen/src/Core/ |
| MatrixBase.h | 125 /** \internal Return type of eigenvalues() */
380 EigenvaluesReturnType eigenvalues() const;
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| DenseBase.h | 243 /** \internal the return type of MatrixBase::eigenvalues() */
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| /external/eigen/Eigen/src/LU/ |
| FullPivLU.h | 26 * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
576 * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
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| /external/ceres-solver/include/ceres/ |
| covariance.h | 297 // this sum that correspond to small eigenvalues.
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| /external/opencv/cxcore/src/ |
| cxmatrix.cpp | [all...] |
| /external/eigen/Eigen/src/Eigenvalues/ |
| Tridiagonalization.h | 47 * eigenvalues and eigenvectors of a selfadjoint matrix.
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| /external/chromium_org/third_party/skia/src/core/ |
| SkMatrix.cpp | [all...] |
| /external/opencv/cvaux/src/ |
| cveigenobjects.cpp | 438 // eigVals - pointer to corresponding eigenvalues (array of <nObjects>
627 /* Calculation of eigenvalues & eigenvectors */
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| /external/skia/src/core/ |
| SkMatrix.cpp | [all...] |
| /external/ceres-solver/docs/source/ |
| solving.rst | 666 solving :eq:`normal` depends on the distribution of eigenvalues
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