/external/eigen/doc/ |
H A D | UsingBlasLapackBackends.dox | 29 * Content : Documentation on the use of BLAS/LAPACK libraries through Eigen 33 namespace Eigen { 35 /** \page TopicUsingBlasLapack Using BLAS/LAPACK from %Eigen 38 Since %Eigen version 3.3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. 44 For LAPACK, you must also link to the standard <a href="http://www.netlib.org/lapack/lapacke.html">Lapacke</a> library, which is used as a convenient think layer between %Eigen's C++ code and LAPACK F77 interface. Then you must activate their usage by defining one or multiple of the following macros (\b before including any %Eigen's header): 59 When doing so, a number of %Eigen's algorithms are silently substituted with calls to BLAS or LAPACK routines. 63 The breadth of %Eigen functionality that can be substituted is listed in the table below. 109 <tr><td>Eigen-value decompositions \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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H A D | UsingIntelMKL.dox | 29 * Content : Documentation on the use of Intel MKL through Eigen 33 namespace Eigen { 35 /** \page TopicUsingIntelMKL Using Intel® MKL from %Eigen 37 <!-- \section TopicUsingIntelMKL_Intro Eigen and Intel® Math Kernel Library (Intel® MKL) --> 39 Since %Eigen version 3.1 and later, users can benefit from built-in Intel® Math Kernel Library (MKL) optimizations with an installed copy of Intel MKL 10.3 (or later). 47 Using Intel MKL through %Eigen is easy: 48 -# define the \c EIGEN_USE_MKL_ALL macro before including any %Eigen's header 52 When doing so, a number of %Eigen's algorithms are silently substituted with calls to Intel MKL routines.
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/external/eigen/test/ |
H A D | mpl2only.cpp | 1 // This file is part of Eigen, a lightweight C++ template library 11 #include <Eigen/Dense> 12 #include <Eigen/SparseCore> 13 #include <Eigen/SparseLU> 14 #include <Eigen/SparseQR> 15 #include <Eigen/Sparse> 16 #include <Eigen/IterativeLinearSolvers> 17 #include <Eigen/Eigen>
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H A D | bug1213.h | 2 #include <Eigen/Core> 5 bool bug1213_2(const Eigen::Matrix<T,dim,1>& x); 7 bool bug1213_1(const Eigen::Vector3f& x);
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H A D | metis_support.cpp | 1 // This file is part of Eigen, a lightweight C++ template library 11 #include <Eigen/SparseLU> 12 #include <Eigen/MetisSupport> 13 #include <unsupported/Eigen/SparseExtra>
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H A D | bug1213_main.cpp | 13 bool bug1213_2(const Eigen::Matrix<T,dim,1>& ) argument 18 template bool bug1213_2<float,3>(const Eigen::Vector3f&);
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H A D | bug1213.cpp | 9 bool bug1213_1(const Eigen::Vector3f& x)
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/external/eigen/unsupported/Eigen/src/SpecialFunctions/ |
H A D | SpecialFunctionsHalf.h | 1 // This file is part of Eigen, a lightweight C++ template library 11 namespace Eigen { namespace 15 template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) { 16 return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a))); 18 template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) { 19 return Eigen::half(Eigen [all...] |
H A D | SpecialFunctionsArrayAPI.h | 1 // This file is part of Eigen, a lightweight C++ template library 14 namespace Eigen { namespace 24 * \sa Eigen::igammac(), Eigen::lgamma() 27 inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived> 28 igamma(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) 30 return Eigen::CwiseBinaryOp<Eigen [all...] |
/external/eigen/doc/examples/ |
H A D | CustomizingEigen_Inheritance.cpp | 1 #include <Eigen/Core> 4 class MyVectorType : public Eigen::VectorXd 7 MyVectorType(void):Eigen::VectorXd() {} 9 // This constructor allows you to construct MyVectorType from Eigen expressions 11 MyVectorType(const Eigen::MatrixBase<OtherDerived>& other) 12 : Eigen::VectorXd(other) 15 // This method allows you to assign Eigen expressions to MyVectorType 17 MyVectorType& operator=(const Eigen::MatrixBase <OtherDerived>& other) 19 this->Eigen::VectorXd::operator=(other);
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H A D | Cwise_erf.cpp | 1 #include <Eigen/Core> 2 #include <unsupported/Eigen/SpecialFunctions> 4 using namespace Eigen;
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H A D | Cwise_erfc.cpp | 1 #include <Eigen/Core> 2 #include <unsupported/Eigen/SpecialFunctions> 4 using namespace Eigen;
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H A D | Cwise_lgamma.cpp | 1 #include <Eigen/Core> 2 #include <unsupported/Eigen/SpecialFunctions> 4 using namespace Eigen;
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H A D | Tutorial_ReductionsVisitorsBroadcasting_broadcast_1nn.cpp | 2 #include <Eigen/Dense> 5 using namespace Eigen; 9 Eigen::MatrixXf m(2,4); 10 Eigen::VectorXf v(2);
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H A D | class_Block.cpp | 1 #include <Eigen/Core> 3 using namespace Eigen; 7 Eigen::Block<Derived> 10 return Eigen::Block<Derived>(m.derived(), 0, 0, rows, cols); 14 const Eigen::Block<const Derived> 17 return Eigen::Block<const Derived>(m.derived(), 0, 0, rows, cols);
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H A D | class_FixedBlock.cpp | 1 #include <Eigen/Core> 3 using namespace Eigen; 7 Eigen::Block<Derived, 2, 2> 10 return Eigen::Block<Derived, 2, 2>(m.derived(), 0, 0); 14 const Eigen::Block<const Derived, 2, 2> 17 return Eigen::Block<const Derived, 2, 2>(m.derived(), 0, 0);
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H A D | class_FixedVectorBlock.cpp | 1 #include <Eigen/Core> 3 using namespace Eigen; 7 Eigen::VectorBlock<Derived, 2> 10 return Eigen::VectorBlock<Derived, 2>(v.derived(), 0); 14 const Eigen::VectorBlock<const Derived, 2> 17 return Eigen::VectorBlock<const Derived, 2>(v.derived(), 0);
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H A D | class_VectorBlock.cpp | 1 #include <Eigen/Core> 3 using namespace Eigen; 7 Eigen::VectorBlock<Derived> 10 return Eigen::VectorBlock<Derived>(v.derived(), start, end-start); 14 const Eigen::VectorBlock<const Derived> 17 return Eigen::VectorBlock<const Derived>(v.derived(), start, end-start);
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H A D | matrixfree_cg.cpp | 2 #include <Eigen/Core> 3 #include <Eigen/Dense> 4 #include <Eigen/IterativeLinearSolvers> 5 #include <unsupported/Eigen/IterativeSolvers> 8 using Eigen::SparseMatrix; 10 namespace Eigen { namespace 14 struct traits<MatrixReplacement> : public Eigen::internal::traits<Eigen::SparseMatrix<double> > 19 // Example of a matrix-free wrapper from a user type to Eigen's compatible type 20 // For the sake of simplicity, this example simply wrap a Eigen 55 namespace Eigen { namespace [all...] |
H A D | DenseBase_middleCols_int.cpp | 1 #include <Eigen/Core> 4 using namespace Eigen;
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/external/eigen/unsupported/test/ |
H A D | cxx11_tensor_dimension.cpp | 1 // This file is part of Eigen, a lightweight C++ template library 12 #include <Eigen/CXX11/Tensor> 14 using Eigen::Tensor; 19 Eigen::DSizes<int, 3> dimensions(2,3,7); 21 VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2); 22 VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3); 23 VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7); 32 Eigen::Sizes<2,3,7> dimensions; 34 VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2); 35 VERIFY_IS_EQUAL((int)Eigen [all...] |
/external/eigen/demos/opengl/ |
H A D | camera.h | 1 // This file is part of Eigen, a lightweight C++ template library 13 #include <Eigen/Geometry> 22 inline Frame(const Eigen::Vector3f& pos = Eigen::Vector3f::Zero(), argument 23 const Eigen::Quaternionf& o = Eigen::Quaternionf()) 32 Eigen::Quaternionf orientation; 33 Eigen::Vector3f position; 60 void setPosition(const Eigen::Vector3f& pos); 61 inline const Eigen [all...] |
/external/eigen/Eigen/src/Core/functors/ |
H A D | TernaryFunctors.h | 1 // This file is part of Eigen, a lightweight C++ template library 13 namespace Eigen { namespace 23 } // end namespace Eigen
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/external/eigen/lapack/ |
H A D | lapack_common.h | 1 // This file is part of Eigen, a lightweight C++ template library 14 #include "../Eigen/src/misc/lapack.h" 20 typedef Eigen::Map<Eigen::Transpositions<Eigen::Dynamic,Eigen::Dynamic,int> > PivotsType;
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/external/eigen/doc/snippets/ |
H A D | Triangular_solve.cpp | 2 m.triangularView<Eigen::Upper>().setOnes(); 5 n.triangularView<Eigen::Lower>() *= 2; 9 << m.triangularView<Eigen::Upper>().solve(n) << endl; 11 << m.triangularView<Eigen::Upper>().solve<Eigen::OnTheRight>(n);
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