[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
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| 5 | //
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| 6 | // This code initially comes from MINPACK whose original authors are:
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| 7 | // Copyright Jorge More - Argonne National Laboratory
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| 8 | // Copyright Burt Garbow - Argonne National Laboratory
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| 9 | // Copyright Ken Hillstrom - Argonne National Laboratory
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| 10 | //
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| 11 | // This Source Code Form is subject to the terms of the Minpack license
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| 12 | // (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
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| 13 |
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| 14 | #ifndef EIGEN_LMONESTEP_H
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| 15 | #define EIGEN_LMONESTEP_H
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| 16 |
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| 17 | namespace Eigen {
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| 18 |
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| 19 | template<typename FunctorType>
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| 20 | LevenbergMarquardtSpace::Status
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| 21 | LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType &x)
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| 22 | {
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| 23 | using std::abs;
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| 24 | using std::sqrt;
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| 25 | RealScalar temp, temp1,temp2;
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| 26 | RealScalar ratio;
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| 27 | RealScalar pnorm, xnorm, fnorm1, actred, dirder, prered;
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| 28 | eigen_assert(x.size()==n); // check the caller is not cheating us
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| 29 |
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| 30 | temp = 0.0; xnorm = 0.0;
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| 31 | /* calculate the jacobian matrix. */
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| 32 | Index df_ret = m_functor.df(x, m_fjac);
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| 33 | if (df_ret<0)
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| 34 | return LevenbergMarquardtSpace::UserAsked;
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| 35 | if (df_ret>0)
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| 36 | // numerical diff, we evaluated the function df_ret times
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| 37 | m_nfev += df_ret;
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| 38 | else m_njev++;
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| 39 |
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| 40 | /* compute the qr factorization of the jacobian. */
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| 41 | for (int j = 0; j < x.size(); ++j)
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| 42 | m_wa2(j) = m_fjac.col(j).blueNorm();
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| 43 | QRSolver qrfac(m_fjac);
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| 44 | if(qrfac.info() != Success) {
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| 45 | m_info = NumericalIssue;
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| 46 | return LevenbergMarquardtSpace::ImproperInputParameters;
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| 47 | }
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| 48 | // Make a copy of the first factor with the associated permutation
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| 49 | m_rfactor = qrfac.matrixR();
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| 50 | m_permutation = (qrfac.colsPermutation());
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| 51 |
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| 52 | /* on the first iteration and if external scaling is not used, scale according */
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| 53 | /* to the norms of the columns of the initial jacobian. */
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| 54 | if (m_iter == 1) {
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| 55 | if (!m_useExternalScaling)
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| 56 | for (Index j = 0; j < n; ++j)
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| 57 | m_diag[j] = (m_wa2[j]==0.)? 1. : m_wa2[j];
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| 58 |
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| 59 | /* on the first iteration, calculate the norm of the scaled x */
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| 60 | /* and initialize the step bound m_delta. */
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| 61 | xnorm = m_diag.cwiseProduct(x).stableNorm();
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| 62 | m_delta = m_factor * xnorm;
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| 63 | if (m_delta == 0.)
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| 64 | m_delta = m_factor;
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| 65 | }
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| 66 |
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| 67 | /* form (q transpose)*m_fvec and store the first n components in */
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| 68 | /* m_qtf. */
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| 69 | m_wa4 = m_fvec;
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| 70 | m_wa4 = qrfac.matrixQ().adjoint() * m_fvec;
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| 71 | m_qtf = m_wa4.head(n);
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| 72 |
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| 73 | /* compute the norm of the scaled gradient. */
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| 74 | m_gnorm = 0.;
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| 75 | if (m_fnorm != 0.)
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| 76 | for (Index j = 0; j < n; ++j)
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| 77 | if (m_wa2[m_permutation.indices()[j]] != 0.)
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| 78 | m_gnorm = (std::max)(m_gnorm, abs( m_rfactor.col(j).head(j+1).dot(m_qtf.head(j+1)/m_fnorm) / m_wa2[m_permutation.indices()[j]]));
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| 79 |
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| 80 | /* test for convergence of the gradient norm. */
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| 81 | if (m_gnorm <= m_gtol) {
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| 82 | m_info = Success;
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| 83 | return LevenbergMarquardtSpace::CosinusTooSmall;
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| 84 | }
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| 85 |
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| 86 | /* rescale if necessary. */
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| 87 | if (!m_useExternalScaling)
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| 88 | m_diag = m_diag.cwiseMax(m_wa2);
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| 89 |
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| 90 | do {
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| 91 | /* determine the levenberg-marquardt parameter. */
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| 92 | internal::lmpar2(qrfac, m_diag, m_qtf, m_delta, m_par, m_wa1);
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| 93 |
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| 94 | /* store the direction p and x + p. calculate the norm of p. */
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| 95 | m_wa1 = -m_wa1;
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| 96 | m_wa2 = x + m_wa1;
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| 97 | pnorm = m_diag.cwiseProduct(m_wa1).stableNorm();
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| 98 |
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| 99 | /* on the first iteration, adjust the initial step bound. */
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| 100 | if (m_iter == 1)
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| 101 | m_delta = (std::min)(m_delta,pnorm);
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| 102 |
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| 103 | /* evaluate the function at x + p and calculate its norm. */
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| 104 | if ( m_functor(m_wa2, m_wa4) < 0)
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| 105 | return LevenbergMarquardtSpace::UserAsked;
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| 106 | ++m_nfev;
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| 107 | fnorm1 = m_wa4.stableNorm();
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| 108 |
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| 109 | /* compute the scaled actual reduction. */
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| 110 | actred = -1.;
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| 111 | if (Scalar(.1) * fnorm1 < m_fnorm)
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| 112 | actred = 1. - numext::abs2(fnorm1 / m_fnorm);
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| 113 |
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| 114 | /* compute the scaled predicted reduction and */
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| 115 | /* the scaled directional derivative. */
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| 116 | m_wa3 = m_rfactor.template triangularView<Upper>() * (m_permutation.inverse() *m_wa1);
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| 117 | temp1 = numext::abs2(m_wa3.stableNorm() / m_fnorm);
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| 118 | temp2 = numext::abs2(sqrt(m_par) * pnorm / m_fnorm);
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| 119 | prered = temp1 + temp2 / Scalar(.5);
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| 120 | dirder = -(temp1 + temp2);
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| 121 |
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| 122 | /* compute the ratio of the actual to the predicted */
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| 123 | /* reduction. */
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| 124 | ratio = 0.;
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| 125 | if (prered != 0.)
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| 126 | ratio = actred / prered;
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| 127 |
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| 128 | /* update the step bound. */
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| 129 | if (ratio <= Scalar(.25)) {
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| 130 | if (actred >= 0.)
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| 131 | temp = RealScalar(.5);
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| 132 | if (actred < 0.)
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| 133 | temp = RealScalar(.5) * dirder / (dirder + RealScalar(.5) * actred);
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| 134 | if (RealScalar(.1) * fnorm1 >= m_fnorm || temp < RealScalar(.1))
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| 135 | temp = Scalar(.1);
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| 136 | /* Computing MIN */
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| 137 | m_delta = temp * (std::min)(m_delta, pnorm / RealScalar(.1));
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| 138 | m_par /= temp;
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| 139 | } else if (!(m_par != 0. && ratio < RealScalar(.75))) {
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| 140 | m_delta = pnorm / RealScalar(.5);
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| 141 | m_par = RealScalar(.5) * m_par;
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| 142 | }
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| 143 |
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| 144 | /* test for successful iteration. */
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| 145 | if (ratio >= RealScalar(1e-4)) {
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| 146 | /* successful iteration. update x, m_fvec, and their norms. */
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| 147 | x = m_wa2;
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| 148 | m_wa2 = m_diag.cwiseProduct(x);
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| 149 | m_fvec = m_wa4;
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| 150 | xnorm = m_wa2.stableNorm();
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| 151 | m_fnorm = fnorm1;
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| 152 | ++m_iter;
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| 153 | }
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| 154 |
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| 155 | /* tests for convergence. */
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| 156 | if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1. && m_delta <= m_xtol * xnorm)
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| 157 | {
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| 158 | m_info = Success;
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| 159 | return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
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| 160 | }
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| 161 | if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1.)
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| 162 | {
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| 163 | m_info = Success;
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| 164 | return LevenbergMarquardtSpace::RelativeReductionTooSmall;
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| 165 | }
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| 166 | if (m_delta <= m_xtol * xnorm)
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| 167 | {
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| 168 | m_info = Success;
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| 169 | return LevenbergMarquardtSpace::RelativeErrorTooSmall;
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| 170 | }
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| 171 |
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| 172 | /* tests for termination and stringent tolerances. */
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| 173 | if (m_nfev >= m_maxfev)
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| 174 | {
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| 175 | m_info = NoConvergence;
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| 176 | return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
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| 177 | }
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| 178 | if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
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| 179 | {
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| 180 | m_info = Success;
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| 181 | return LevenbergMarquardtSpace::FtolTooSmall;
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| 182 | }
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| 183 | if (m_delta <= NumTraits<Scalar>::epsilon() * xnorm)
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| 184 | {
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| 185 | m_info = Success;
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| 186 | return LevenbergMarquardtSpace::XtolTooSmall;
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| 187 | }
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| 188 | if (m_gnorm <= NumTraits<Scalar>::epsilon())
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| 189 | {
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| 190 | m_info = Success;
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| 191 | return LevenbergMarquardtSpace::GtolTooSmall;
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| 192 | }
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| 193 |
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| 194 | } while (ratio < Scalar(1e-4));
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| 195 |
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| 196 | return LevenbergMarquardtSpace::Running;
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| 197 | }
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| 198 |
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| 199 |
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| 200 | } // end namespace Eigen
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| 201 |
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| 202 | #endif // EIGEN_LMONESTEP_H
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