| /dports/math/g2o/g2o-20201223_git/g2o/solvers/eigen/ |
| H A D | linear_solver_eigen.h | 95 xx = _cholesky.solve(bb); in solve()
99 globalStats->choleskyNNZ = _cholesky.matrixL().nestedExpression().nonZeros(); in solve()
107 CholeskyDecomposition _cholesky; variable
118 _cholesky.factorize(_sparseMatrix); in computeCholesky()
119 if (_cholesky.info() != Eigen::Success) { // the matrix is not positive definite in computeCholesky()
139 _cholesky.analyzePattern(_sparseMatrix); in computeSymbolicDecomposition()
159 _cholesky.analyzePatternWithPermutation(_sparseMatrix, scalarP); in computeSymbolicDecomposition()
190 mcc.setCholeskyFactor(_cholesky.matrixL().rows(), in solveBlocks_impl()
191 const_cast<int*>(_cholesky.matrixL().nestedExpression().outerIndexPtr()), in solveBlocks_impl()
194 const_cast<int*>(_cholesky.permutationP().indices().data())); in solveBlocks_impl()
[all …]
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| /dports/math/g2o/g2o-20201223_git/g2o/solvers/dense/ |
| H A D | linear_solver_dense.h | 107 _cholesky.compute(H); in solve()
108 if (_cholesky.isPositive()) { in solve()
109 xvec = _cholesky.solve(bvec); in solve()
118 Eigen::LDLT<MatrixX> _cholesky; variable
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| /dports/devel/boost-docs/boost_1_72_0/libs/python/example/numpy/ |
| H A D | gaussian.cpp | 109 vector2 u = _cholesky * (p - _mu); in operator ()()
110 return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI; in operator ()()
127 : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma)) in bivariate_gaussian()
151 matrix2 _cholesky; member in bivariate_gaussian
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| /dports/devel/boost-python-libs/boost_1_72_0/libs/python/example/numpy/ |
| H A D | gaussian.cpp | 109 vector2 u = _cholesky * (p - _mu); in operator ()()
110 return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI; in operator ()()
127 : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma)) in bivariate_gaussian()
151 matrix2 _cholesky; member in bivariate_gaussian
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| /dports/devel/boost-libs/boost_1_72_0/libs/python/example/numpy/ |
| H A D | gaussian.cpp | 109 vector2 u = _cholesky * (p - _mu); in operator ()()
110 return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI; in operator ()()
127 : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma)) in bivariate_gaussian()
151 matrix2 _cholesky; member in bivariate_gaussian
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| /dports/devel/hyperscan/boost_1_75_0/libs/python/example/numpy/ |
| H A D | gaussian.cpp | 109 vector2 u = _cholesky * (p - _mu); in operator ()()
110 return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI; in operator ()()
127 : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma)) in bivariate_gaussian()
151 matrix2 _cholesky; member in bivariate_gaussian
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| /dports/math/g2o/g2o-20201223_git/g2o/stuff/ |
| H A D | sampler.h | 58 _cholesky = cholDecomp.matrixL(); in setDistribution()
66 return _cholesky * s; in generateSample()
76 CovarianceType _cholesky;
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| /dports/biology/cufflinks/cufflinks-2.2.1-89-gdc3b0cb/src/ |
| H A D | sampling.h | 222 _cholesky = chol_cov; in multinormal_generator()
237 for (size_t i = 0; i < _cholesky.size1(); ++i) in next_rand()
241 _rand(i) += _cholesky(i,j) * temp(j); in next_rand()
256 _cholesky = chol_cov; in set_parameters()
262 boost::numeric::ublas::matrix<ValueType> _cholesky; variable
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| /dports/science/py-scipy/scipy-1.7.1/scipy/linalg/ |
| H A D | decomp_cholesky.py | 13 def _cholesky(a, lower=False, overwrite_a=False, clean=True, function
88 c, lower = _cholesky(a, lower=lower, overwrite_a=overwrite_a, clean=True,
152 c, lower = _cholesky(a, lower=lower, overwrite_a=overwrite_a, clean=False,
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| /dports/math/py-sympy/sympy-1.9/sympy/matrices/ |
| H A D | dense.py | 13 from .decompositions import _cholesky, _LDLdecomposition
78 return _cholesky(self, hermitian=hermitian)
89 cholesky.__doc__ = _cholesky.__doc__
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| H A D | decompositions.py | 195 def _cholesky(M, hermitian=True): function
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| H A D | matrices.py | 50 _rank_decomposition, _cholesky, _LDLdecomposition,
2241 cholesky.__doc__ = _cholesky.__doc__
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| /dports/math/unuran/unuran-1.8.1/tests/ |
| H A D | t_distr_cvec.conf | 287 ~_cholesky( distr );
343 ~_cholesky( distr );
381 ~_cholesky( distr );
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| /dports/math/py-jax/jax-0.2.9/jax/_src/scipy/ |
| H A D | linalg.py | 32 def _cholesky(a, lower): function
40 return _cholesky(a, lower)
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| /dports/devel/py-dask/dask-2021.11.2/dask/array/ |
| H A D | linalg.py | 1221 l, u = _cholesky(a)
1270 l, u = _cholesky(a)
1277 def _cholesky(a): function
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| /dports/math/py-Diofant/Diofant-0.13.0/diofant/matrices/ |
| H A D | matrices.py | 716 return self._cholesky()
823 L = self._cholesky()
825 L = (self.T*self)._cholesky()
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| H A D | dense.py | 324 def _cholesky(self): member in DenseMatrix
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| /dports/science/py-scikit-sparse/scikit-sparse-0.4.6/sksparse/ |
| H A D | cholmod.pyx | 1188 …return _cholesky(A, True, beta=beta, mode=mode, ordering_method=ordering_method, use_long=use_long)
1212 …return _cholesky(A, False, beta=beta, mode=mode, ordering_method=ordering_method, use_long=use_lon…
1214 def _cholesky(A, symmetric, beta, mode, ordering_method="default", use_long=None): function
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| /dports/math/giacxcas/giac-1.6.0/src/ |
| H A D | vecteur.h | 483 gen _cholesky(const gen & a,GIAC_CONTEXT);
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| H A D | giac.i | 1935 gen _cholesky(const gen & a,giac::context * );
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| /dports/math/py-numpy/numpy-1.20.3/numpy/linalg/ |
| H A D | umath_linalg.c.src | 1795 @TYPE@_cholesky(char uplo, char **args, npy_intp const *dimensions, npy_intp const *steps)
1831 @TYPE@_cholesky('L', args, dimensions, steps);
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| /dports/science/nwchem-data/nwchem-7.0.2-release/doc/prog/ |
| H A D | ga.tex | 624 \item {\tt ga\_cholesky(uplo, g\_a)} --- computes the Cholesky factorization of an NxN
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| /dports/science/nwchem/nwchem-7b21660b82ebd85ef659f6fba7e1e73433b0bd0a/doc/prog/ |
| H A D | ga.tex | 624 \item {\tt ga\_cholesky(uplo, g\_a)} --- computes the Cholesky factorization of an NxN
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| /dports/math/py-algopy/algopy-0.5.7/algopy/utpm/ |
| H A D | algorithms.py | 1511 def _cholesky(cls, A_data, L_data): member in RawAlgorithmsMixIn
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| H A D | utpm.py | 2086 cls._cholesky(A.data, out.data)
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