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  /external/ceres-solver/internal/ceres/
corrector_test.cc 151 MatrixRef jac(jacobian, 3, 2);
191 g_jac = sqrt(rho[1]) * (jac - kAlpha / sq_norm *
192 res * res.transpose() * jac);
194 g_grad = rho[1] * jac.transpose() * res;
195 g_hess = rho[1] * jac.transpose() * jac +
196 2.0 * rho[2] * jac.transpose() * res * res.transpose() * jac;
203 c_grad = jac.transpose() * res;
204 c_hess = jac.transpose() * jac
    [all...]
conditioned_cost_function_test.cc 72 jac[kTestCostFunctionSize * kTestCostFunctionSize], local
80 jac[i * 2] = i * i;
106 jacs[0] = jac;
115 double actual = jac[i * kTestCostFunctionSize + j];
autodiff_test.cc 57 T jac[M * N]) { // row-major.
89 RowMajorAccess(jac, M, N, i, j) =
  /external/eigen/unsupported/Eigen/src/AutoDiff/
AutoDiffJacobian.h 57 JacobianType& jac = *_jac; local
60 ActiveValue av(jac.rows());
63 for (Index j=0; j<jac.rows(); j++)
66 for (Index i=0; i<jac.cols(); i++)
71 for (Index i=0; i<jac.rows(); i++)
74 jac.row(i) = av[i].derivatives();
AutoDiffVector.h 67 inline AutoDiffVector(const ValueType& values, const JacobianType& jac)
68 : m_values(values), m_jacobian(jac)
  /external/eigen/unsupported/test/
NumericalDiff.cpp 73 MatrixXd jac(15,3);
85 numDiff.df(x, jac);
86 // std::cout << jac << std::endl;
88 VERIFY_IS_APPROX(jac, actual_jac);
94 MatrixXd jac(15,3);
105 numDiff.df(x, jac);
107 VERIFY_IS_APPROX(jac, actual_jac);
  /external/eigen/unsupported/Eigen/src/NumericalDiff/
NumericalDiff.h 64 int df(const InputType& _x, JacobianType &jac) const
102 jac.col(j) = (val2-val1)/h;
110 jac.col(j) = (val2-val1)/(2*h);

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