| /external/eigen/unsupported/test/ |
| NonLinearOptimization.cpp | 17 int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag)
51 fjac(i,0) = -1.;
52 fjac(i,1) = tmp1*tmp2/tmp4;
53 fjac(i,2) = tmp1*tmp3/tmp4;
64 MatrixXd fjac(m,n);
71 internal::chkder(x, fvec, fjac, xp, fvecp, 1, err);
72 fcn_chkder(x, fvec, fjac, 1);
73 fcn_chkder(x, fvec, fjac, 2);
74 fcn_chkder(xp, fvecp, fjac, 1);
75 internal::chkder(x, fvec, fjac, xp, fvecp, 2, err)
[all...] |
| levenberg_marquardt.cpp | 42 int df(const VectorXd &x, MatrixXd &fjac) const
51 fjac(i,0) = -1;
52 fjac(i,1) = tmp1*tmp2/tmp4;
53 fjac(i,2) = tmp1*tmp3/tmp4;
126 // std::cout << fjac*covfac << std::endl;
132 // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
229 // std::cout << fjac*covfac << std::endl;
235 // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
255 int df(const VectorXd &b, MatrixXd &fjac)
258 assert(fjac.rows()==54)
[all...] |
| NumericalDiff.cpp | 53 int actual_df(const VectorXd &x, MatrixXd &fjac) const
62 fjac(i,0) = -1;
63 fjac(i,1) = tmp1*tmp2/tmp4;
64 fjac(i,2) = tmp1*tmp3/tmp4;
|
| /external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
| fdjac1.h | 10 Matrix< Scalar, Dynamic, Dynamic > &fjac,
48 fjac.col(j) = (wa1-fvec)/h;
67 fjac.col(j).setZero();
70 fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
|
| LevenbergMarquardt.h | 102 JacobianType fjac; member in class:Eigen::LevenbergMarquardt
172 fjac.resize(m, n);
215 Index df_ret = functor.df(x, fjac);
224 wa2 = fjac.colwise().blueNorm();
225 ColPivHouseholderQR<JacobianType> qrfac(fjac);
226 fjac = qrfac.matrixQR();
255 gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
292 wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1);
386 // Only R is stored in fjac. Q is only used to compute 'qtf', which is
391 fjac.resize(n, n)
[all...] |
| HybridNonLinearSolver.h | 92 JacobianType fjac; member in class:Eigen::HybridNonLinearSolver
150 fjac.resize(n, n);
198 if ( functor.df(x, fjac) < 0)
202 wa2 = fjac.colwise().blueNorm();
220 HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
225 /* accumulate the orthogonal factor in fjac. */
226 fjac = qrfac.householderQ();
229 qtf = fjac.transpose() * fvec;
327 wa2 = fjac.transpose() * wa4;
334 internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens)
[all...] |
| chkder.h | 12 const Matrix< Scalar, Dynamic, Dynamic > &fjac,
49 err += temp * fjac.col(j);
|
| /external/eigen/test/ |
| denseLM.cpp | 78 int df(const VectorType& uv, JacobianType& fjac)
83 eigen_assert(fjac.rows() == m);
84 eigen_assert(fjac.cols() == n);
92 fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
93 fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
|
| sparseLM.cpp | 83 int df(const VectorType& uv, JacobianType& fjac)
88 eigen_assert(fjac.rows() == m);
89 eigen_assert(fjac.cols() == n);
104 fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
117 fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
|
