/external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
LMpar.h | 55 VectorType wa1, wa2; local 73 wa2 = diag.cwiseProduct(x); 74 dxnorm = wa2.blueNorm(); 86 wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2)/dxnorm; 120 wa2 = diag.cwiseProduct(x); 121 dxnorm = wa2.blueNorm(); 132 wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
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/external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
lmpar.h | 36 Matrix< Scalar, Dynamic, 1 > wa1, wa2; local 62 wa2 = diag.cwiseProduct(x); 63 dxnorm = wa2.blueNorm(); 77 wa1[j] = diag[l] * (wa2[l] / dxnorm); 119 wa2 = diag.cwiseProduct(x); 120 dxnorm = wa2.blueNorm(); 133 wa1[j] = diag[l] * (wa2[l] / dxnorm); 193 Matrix< Scalar, Dynamic, 1 > wa1, wa2; local 210 wa2 = diag.cwiseProduct(x); 211 dxnorm = wa2.blueNorm() [all...] |
HybridNonLinearSolver.h | 113 FVectorType wa1, wa2, wa3, wa4; member in class:Eigen::HybridNonLinearSolver 147 wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n); 202 wa2 = fjac.colwise().blueNorm(); 209 diag[j] = (wa2[j]==0.) ? 1. : wa2[j]; 233 diag = diag.cwiseMax(wa2); 241 wa2 = x + wa1; 249 if ( functor(wa2, wa4) < 0) 289 x = wa2; 290 wa2 = diag.cwiseProduct(x) [all...] |
LevenbergMarquardt.h | 122 FVectorType wa1, wa2, wa3, wa4; member in class:Eigen::LevenbergMarquardt 176 wa1.resize(n); wa2.resize(n); wa3.resize(n); 231 wa2 = fjac.colwise().blueNorm(); 241 diag[j] = (wa2[j]==0.)? 1. : wa2[j]; 261 if (wa2[permutation.indices()[j]] != 0.) 262 gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); 270 diag = diag.cwiseMax(wa2); 279 wa2 = x + wa1; 287 if ( functor(wa2, wa4) < 0 [all...] |
/external/lmfit/lib/ |
lmmin.c | 215 double* wa2 = (double*)pws; local 304 lm_qrfac(m, n, fjac, Pivot, wa1, wa2, wa3); 305 /* return values are Pivot, wa1=rdiag, wa2=acnorm */ 328 if (wa2[Pivot[j]] == 0) 333 gnorm = MAX(gnorm, fabs(sum / wa2[Pivot[j]] / fnorm)); 346 diag[j] = wa2[j] ? wa2[j] : 1; 380 diag[j] = MAX(diag[j], wa2[j]); 390 wa1, wa2, wf, wa3); 419 wa2[j] = x[j] - wa1[j] [all...] |