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    Searched refs:SVD (Results 1 - 16 of 16) sorted by null

  /external/eigen/doc/examples/
function_taking_ref.cpp 2 #include <Eigen/SVD>
  /external/eigen/test/
upperbidiagonalization.cpp 11 #include <Eigen/SVD>
jacobi.cpp 12 #include <Eigen/SVD>
bdcsvd.cpp 18 #include <Eigen/SVD>
geo_eulerangles.cpp 13 #include <Eigen/SVD>
jacobisvd.cpp 15 #include <Eigen/SVD>
geo_orthomethods.cpp 13 #include <Eigen/SVD>
umeyama.cpp 16 #include <Eigen/SVD> // required for SVD
geo_quaternion.cpp 14 #include <Eigen/SVD>
nomalloc.cpp 21 #include <Eigen/SVD>
155 // SVD module
qr_colpivoting.cpp 13 #include <Eigen/SVD>
56 // Verify that we get the same minimum-norm solution as the SVD.
57 JacobiSVD<MatrixType> svd(matrix, ComputeThinU | ComputeThinV);
58 MatrixType svd_solution = svd.solve(rhs);
88 // Verify that we get the same minimum-norm solution as the SVD.
89 JacobiSVD<MatrixType> svd(matrix, ComputeFullU | ComputeFullV);
90 Matrix<Scalar, Cols, Cols2> svd_solution = svd.solve(rhs);
geo_transformations.cpp 13 #include <Eigen/SVD>
  /external/eigen/unsupported/bench/
bench_svd.cpp 13 // Bench to compare the efficiency of SVD algorithms
17 #include <unsupported/Eigen/SVD>
  /external/eigen/Eigen/src/SVD/
JacobiSVD.h 23 /*** QR preconditioners (R-SVD)
25 *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
26 *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
76 void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
78 if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
81 ::new (&m_qr) QRType(svd.rows(), svd.cols());
83 if (svd.m_computeFullU) m_workspace.resize(svd.rows())
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  /external/eigen/lapack/
svd.cpp 11 #include <Eigen/SVD>
56 BDCSVD<PlainMatrixType> svd(mat,option);
58 make_vector(s,diag_size) = svd.singularValues().head(diag_size);
62 matrix(u,*m,*m,*ldu) = svd.matrixU();
63 matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
67 matrix(u,*m,diag_size,*ldu) = svd.matrixU();
68 matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
72 matrix(a,*m,*n,*lda) = svd.matrixU();
73 matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
77 matrix(u,*m,*m,*ldu) = svd.matrixU()
    [all...]
  /external/llvm/lib/Transforms/Instrumentation/
AddressSanitizer.cpp     [all...]

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