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  /external/eigen/doc/snippets/
PartialRedux_squaredNorm.cpp 3 cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << endl;
Tutorial_Map_using.cpp 15 cout << "Squared euclidean distance: " << (m1-m2).squaredNorm() << endl;
17 (m1-m2map).squaredNorm() << endl;
  /external/eigen/doc/examples/
Tutorial_ReductionsVisitorsBroadcasting_reductions_norm.cpp 18 cout << "v.squaredNorm() = " << v.squaredNorm() << endl;
24 cout << "m.squaredNorm() = " << m.squaredNorm() << endl;
Tutorial_ReductionsVisitorsBroadcasting_broadcast_1nn.cpp 20 (m.colwise() - v).colwise().squaredNorm().minCoeff(&index);
  /external/ceres-solver/internal/ceres/
normal_prior_test.cc 78 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
117 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
123 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
low_rank_inverse_hessian.cc 115 delta_x_dot_delta_gradient / delta_gradient.squaredNorm();
dogleg_strategy.cc 190 alpha_ = gradient_.squaredNorm() / Jg.squaredNorm();
429 polynomial(2) = r2 * (trB * trB + 2.0 * detB) - subspace_g_.squaredNorm();
432 polynomial(4) = r2 * detB * detB - (B_adj * subspace_g_).squaredNorm();
  /external/eigen/unsupported/Eigen/src/IterativeSolvers/
IterationController.h 134 { return converged(v.squaredNorm()); }
148 { return finished(double(v.squaredNorm())); }
MINRES.h 43 const RealScalar rhsNorm2(rhs.squaredNorm());
50 RealScalar residualNorm2(v_new.squaredNorm());
  /external/eigen/Eigen/src/IterativeLinearSolvers/
BiCGSTAB.h 46 RealScalar r0_sqnorm = r0.squaredNorm();
47 RealScalar rhs_sqnorm = rhs.squaredNorm();
68 while ( r.squaredNorm()/rhs_sqnorm > tol2 && i<maxIters )
78 rho = r0_sqnorm = r.squaredNorm();
95 RealScalar tmp = t.squaredNorm();
104 tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
ConjugateGradient.h 45 RealScalar rhsNorm2 = rhs.squaredNorm();
54 RealScalar residualNorm2 = residual.squaredNorm();
76 residualNorm2 = residual.squaredNorm();
  /external/eigen/Eigen/src/Core/
Dot.h 58 * \sa squaredNorm(), norm()
113 EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
122 * \sa dot(), squaredNorm()
128 return sqrt(squaredNorm());
232 return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
252 if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
  /external/eigen/Eigen/src/SparseCore/
SparseDot.h 80 SparseMatrixBase<Derived>::squaredNorm() const
90 return sqrt(squaredNorm());
  /external/eigen/test/eigen2/
eigen2_adjoint.cpp 56 VERIFY_IS_APPROX(ei_real(v1.eigen2_dot(v1)), v1.squaredNorm());
58 VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm());
  /external/eigen/test/
sparse_vector.cpp 79 VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
geo_alignedbox.cpp 118 VERIFY_IS_APPROX( 53.0f, box.diagonal().squaredNorm() );
145 VERIFY_IS_APPROX( 62, box.diagonal().squaredNorm() );
stable_norm.cpp 90 VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail
97 VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail
  /external/eigen/unsupported/doc/examples/
BVH_Example.cpp 22 double minimumOnObjectObject(const Vector2d &v1, const Vector2d &v2) { ++calls; return (v1 - v2).squaredNorm(); }
  /external/eigen/unsupported/test/
mpreal_support.cpp 30 VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm()));
NonLinearOptimization.cpp 691 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
712 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
771 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
788 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
861 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
883 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
947 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
964 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
    [all...]
levenberg_marquardt.cpp 296 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02);
317 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02);
376 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01);
393 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01);
467 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00);
489 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00);
553 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02);
570 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02);
631 // VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.430899764097e-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
652 // VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.428595533845e-25); // should be 1.4307867721E-25, but nist results are on 128-bit float
    [all...]
BVH.cpp 55 if((b.center - p).squaredNorm() < SQR(b.radius))
72 if((b.center - v).squaredNorm() < SQR(b.radius))
78 double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
  /external/eigen/Eigen/src/Eigenvalues/
SelfAdjointEigenSolver.h 585 n = (cross = tmp.row(0).cross(tmp.row(1))).squaredNorm();
591 n = (cross = tmp.row(0).cross(tmp.row(2))).squaredNorm();
597 n = (cross = tmp.row(1).cross(tmp.row(2))).squaredNorm();
623 n = (cross = eivecs.col(k).cross(tmp.row(0).normalized())).squaredNorm();
628 n = (cross = eivecs.col(k).cross(tmp.row(1))).squaredNorm();
633 n = (cross = eivecs.col(k).cross(tmp.row(2))).squaredNorm();
  /external/eigen/Eigen/src/Eigen2Support/Geometry/
ParametrizedLine.h 74 return (diff - diff.eigen2_dot(direction())* direction()).squaredNorm();
  /external/eigen/Eigen/src/Householder/
Householder.h 76 RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();

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