| /external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
| chkder.h | 11 const Matrix< Scalar, Dynamic, 1 > &fvec,
30 const Index m = fvec.size(), n = x.size();
53 if (fvec[i] != 0. && fvecp[i] != 0. && abs(fvecp[i] - fvec[i]) >= epsf * abs(fvec[i]))
54 temp = eps * abs((fvecp[i] - fvec[i]) / eps - err[i]) / (abs(fvec[i]) + abs(fvecp[i]));
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| fdjac1.h | 9 Matrix< Scalar, Dynamic, 1 > &fvec,
30 eigen_assert(fvec.size()==n);
48 fjac.col(j) = (wa1-fvec)/h;
70 fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
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| HybridNonLinearSolver.h | 91 FVectorType fvec, qtf, diag; member in class:Eigen::HybridNonLinearSolver
148 fvec.resize(n);
170 if ( functor(x, fvec) < 0)
172 fnorm = fvec.stableNorm();
228 /* form (q transpose)*fvec and store in qtf. */
229 qtf = fjac.transpose() * fvec;
288 /* successful iteration. update x, fvec, and their norms. */
291 fvec = wa4;
390 fvec.resize(n);
410 if ( functor(x, fvec) < 0
[all...] |
| LevenbergMarquardt.h | 107 FVectorType fvec, qtf, diag; member in class:Eigen::LevenbergMarquardt
178 fvec.resize(m);
201 if ( functor(x, fvec) < 0)
203 fnorm = fvec.stableNorm();
251 /* form (q transpose)*fvec and store the first n components in */
253 wa4 = fvec;
329 /* successful iteration. update x, fvec, and their norms. */
332 fvec = wa4;
392 fvec.resize(m);
420 if ( functor(x, fvec) < 0
[all...] |
| /external/eigen/unsupported/test/ |
| NonLinearOptimization.cpp | 18 int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag)
23 assert(15 == fvec.size());
39 fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3));
64 VectorXd x(n), fvec(m), xp, fvecp(m), err;
72 internal::chkder(x, fvec, fjac, xp, fvecp, 1, err);
73 fcn_chkder(x, fvec, fjac, 1);
74 fcn_chkder(x, fvec, fjac, 2);
76 internal::chkder(x, fvec, fjac, xp, fvecp, 2, err);
78 fvecp -= fvec;
101 VERIFY_IS_APPROX(fvec, fvec_ref)
[all...] |
| levenberg_marquardt.cpp | 32 int operator()(const VectorXd &x, VectorXd &fvec) const
43 fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3));
85 VERIFY_IS_APPROX(lm.fvec().blueNorm(), 0.09063596);
114 fnorm = lm.fvec().blueNorm();
144 int operator()(const VectorXd &x, VectorXd &fvec) const
152 assert(fvec.size()==15);
160 fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3));
171 VectorXd x(n), fvec(15);
186 functor(x, fvec);
187 VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596)
[all...] |
| NumericalDiff.cpp | 37 int operator()(const VectorXd &x, VectorXd &fvec) const
48 fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3));
|
| /external/eigen/test/ |
| denseLM.cpp | 56 int operator()(const VectorType& uv, VectorType& fvec)
63 eigen_assert(fvec.size() == m);
69 fvec(j) = m_y(j);
72 fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
|
| sparseLM.cpp | 59 int operator()(const VectorType& uv, VectorType& fvec)
68 fvec = m_y;
77 fvec(j) -= u(i)*std::pow((1-coeff), 2);
|
| /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
| LevenbergMarquardt.h | 61 //int operator()(const InputType &x, ValueType& fvec) { }
88 //int operator()(const InputType &x, ValueType& fvec) { }
219 FVectorType& fvec() {return m_fvec; } function in class:Eigen::LevenbergMarquardt
|
| /prebuilts/gcc/linux-x86/host/x86_64-w64-mingw32-4.8/x86_64-w64-mingw32/include/ |
| dvec.h | 16 #include <fvec.h>
[all...] |
