| // -*- coding: utf-8 |
| // vim: set fileencoding=utf-8 |
| |
| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_LEVENBERGMARQUARDT__H |
| #define EIGEN_LEVENBERGMARQUARDT__H |
| |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| namespace LevenbergMarquardtSpace { |
| enum Status { |
| NotStarted = -2, |
| Running = -1, |
| ImproperInputParameters = 0, |
| RelativeReductionTooSmall = 1, |
| RelativeErrorTooSmall = 2, |
| RelativeErrorAndReductionTooSmall = 3, |
| CosinusTooSmall = 4, |
| TooManyFunctionEvaluation = 5, |
| FtolTooSmall = 6, |
| XtolTooSmall = 7, |
| GtolTooSmall = 8, |
| UserAsked = 9 |
| }; |
| } |
| |
| |
| |
| /** |
| * \ingroup NonLinearOptimization_Module |
| * \brief Performs non linear optimization over a non-linear function, |
| * using a variant of the Levenberg Marquardt algorithm. |
| * |
| * Check wikipedia for more information. |
| * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm |
| */ |
| template<typename FunctorType, typename Scalar=double> |
| class LevenbergMarquardt |
| { |
| static Scalar sqrt_epsilon() |
| { |
| using std::sqrt; |
| return sqrt(NumTraits<Scalar>::epsilon()); |
| } |
| |
| public: |
| LevenbergMarquardt(FunctorType &_functor) |
| : functor(_functor) { nfev = njev = iter = 0; fnorm = gnorm = 0.; useExternalScaling=false; } |
| |
| typedef DenseIndex Index; |
| |
| struct Parameters { |
| Parameters() |
| : factor(Scalar(100.)) |
| , maxfev(400) |
| , ftol(sqrt_epsilon()) |
| , xtol(sqrt_epsilon()) |
| , gtol(Scalar(0.)) |
| , epsfcn(Scalar(0.)) {} |
| Scalar factor; |
| Index maxfev; // maximum number of function evaluation |
| Scalar ftol; |
| Scalar xtol; |
| Scalar gtol; |
| Scalar epsfcn; |
| }; |
| |
| typedef Matrix< Scalar, Dynamic, 1 > FVectorType; |
| typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType; |
| |
| LevenbergMarquardtSpace::Status lmder1( |
| FVectorType &x, |
| const Scalar tol = sqrt_epsilon() |
| ); |
| |
| LevenbergMarquardtSpace::Status minimize(FVectorType &x); |
| LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x); |
| LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x); |
| |
| static LevenbergMarquardtSpace::Status lmdif1( |
| FunctorType &functor, |
| FVectorType &x, |
| Index *nfev, |
| const Scalar tol = sqrt_epsilon() |
| ); |
| |
| LevenbergMarquardtSpace::Status lmstr1( |
| FVectorType &x, |
| const Scalar tol = sqrt_epsilon() |
| ); |
| |
| LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x); |
| LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(FVectorType &x); |
| LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(FVectorType &x); |
| |
| void resetParameters(void) { parameters = Parameters(); } |
| |
| Parameters parameters; |
| FVectorType fvec, qtf, diag; |
| JacobianType fjac; |
| PermutationMatrix<Dynamic,Dynamic> permutation; |
| Index nfev; |
| Index njev; |
| Index iter; |
| Scalar fnorm, gnorm; |
| bool useExternalScaling; |
| |
| Scalar lm_param(void) { return par; } |
| private: |
| |
| FunctorType &functor; |
| Index n; |
| Index m; |
| FVectorType wa1, wa2, wa3, wa4; |
| |
| Scalar par, sum; |
| Scalar temp, temp1, temp2; |
| Scalar delta; |
| Scalar ratio; |
| Scalar pnorm, xnorm, fnorm1, actred, dirder, prered; |
| |
| LevenbergMarquardt& operator=(const LevenbergMarquardt&); |
| }; |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::lmder1( |
| FVectorType &x, |
| const Scalar tol |
| ) |
| { |
| n = x.size(); |
| m = functor.values(); |
| |
| /* check the input parameters for errors. */ |
| if (n <= 0 || m < n || tol < 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| resetParameters(); |
| parameters.ftol = tol; |
| parameters.xtol = tol; |
| parameters.maxfev = 100*(n+1); |
| |
| return minimize(x); |
| } |
| |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType &x) |
| { |
| LevenbergMarquardtSpace::Status status = minimizeInit(x); |
| if (status==LevenbergMarquardtSpace::ImproperInputParameters) |
| return status; |
| do { |
| status = minimizeOneStep(x); |
| } while (status==LevenbergMarquardtSpace::Running); |
| return status; |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(FVectorType &x) |
| { |
| n = x.size(); |
| m = functor.values(); |
| |
| wa1.resize(n); wa2.resize(n); wa3.resize(n); |
| wa4.resize(m); |
| fvec.resize(m); |
| fjac.resize(m, n); |
| if (!useExternalScaling) |
| diag.resize(n); |
| eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'"); |
| qtf.resize(n); |
| |
| /* Function Body */ |
| nfev = 0; |
| njev = 0; |
| |
| /* check the input parameters for errors. */ |
| if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| if (useExternalScaling) |
| for (Index j = 0; j < n; ++j) |
| if (diag[j] <= 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| /* evaluate the function at the starting point */ |
| /* and calculate its norm. */ |
| nfev = 1; |
| if ( functor(x, fvec) < 0) |
| return LevenbergMarquardtSpace::UserAsked; |
| fnorm = fvec.stableNorm(); |
| |
| /* initialize levenberg-marquardt parameter and iteration counter. */ |
| par = 0.; |
| iter = 1; |
| |
| return LevenbergMarquardtSpace::NotStarted; |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x) |
| { |
| using std::abs; |
| using std::sqrt; |
| |
| eigen_assert(x.size()==n); // check the caller is not cheating us |
| |
| /* calculate the jacobian matrix. */ |
| Index df_ret = functor.df(x, fjac); |
| if (df_ret<0) |
| return LevenbergMarquardtSpace::UserAsked; |
| if (df_ret>0) |
| // numerical diff, we evaluated the function df_ret times |
| nfev += df_ret; |
| else njev++; |
| |
| /* compute the qr factorization of the jacobian. */ |
| wa2 = fjac.colwise().blueNorm(); |
| ColPivHouseholderQR<JacobianType> qrfac(fjac); |
| fjac = qrfac.matrixQR(); |
| permutation = qrfac.colsPermutation(); |
| |
| /* on the first iteration and if external scaling is not used, scale according */ |
| /* to the norms of the columns of the initial jacobian. */ |
| if (iter == 1) { |
| if (!useExternalScaling) |
| for (Index j = 0; j < n; ++j) |
| diag[j] = (wa2[j]==0.)? 1. : wa2[j]; |
| |
| /* on the first iteration, calculate the norm of the scaled x */ |
| /* and initialize the step bound delta. */ |
| xnorm = diag.cwiseProduct(x).stableNorm(); |
| delta = parameters.factor * xnorm; |
| if (delta == 0.) |
| delta = parameters.factor; |
| } |
| |
| /* form (q transpose)*fvec and store the first n components in */ |
| /* qtf. */ |
| wa4 = fvec; |
| wa4.applyOnTheLeft(qrfac.householderQ().adjoint()); |
| qtf = wa4.head(n); |
| |
| /* compute the norm of the scaled gradient. */ |
| gnorm = 0.; |
| if (fnorm != 0.) |
| for (Index j = 0; j < n; ++j) |
| if (wa2[permutation.indices()[j]] != 0.) |
| gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); |
| |
| /* test for convergence of the gradient norm. */ |
| if (gnorm <= parameters.gtol) |
| return LevenbergMarquardtSpace::CosinusTooSmall; |
| |
| /* rescale if necessary. */ |
| if (!useExternalScaling) |
| diag = diag.cwiseMax(wa2); |
| |
| do { |
| |
| /* determine the levenberg-marquardt parameter. */ |
| internal::lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1); |
| |
| /* store the direction p and x + p. calculate the norm of p. */ |
| wa1 = -wa1; |
| wa2 = x + wa1; |
| pnorm = diag.cwiseProduct(wa1).stableNorm(); |
| |
| /* on the first iteration, adjust the initial step bound. */ |
| if (iter == 1) |
| delta = (std::min)(delta,pnorm); |
| |
| /* evaluate the function at x + p and calculate its norm. */ |
| if ( functor(wa2, wa4) < 0) |
| return LevenbergMarquardtSpace::UserAsked; |
| ++nfev; |
| fnorm1 = wa4.stableNorm(); |
| |
| /* compute the scaled actual reduction. */ |
| actred = -1.; |
| if (Scalar(.1) * fnorm1 < fnorm) |
| actred = 1. - numext::abs2(fnorm1 / fnorm); |
| |
| /* compute the scaled predicted reduction and */ |
| /* the scaled directional derivative. */ |
| wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1); |
| temp1 = numext::abs2(wa3.stableNorm() / fnorm); |
| temp2 = numext::abs2(sqrt(par) * pnorm / fnorm); |
| prered = temp1 + temp2 / Scalar(.5); |
| dirder = -(temp1 + temp2); |
| |
| /* compute the ratio of the actual to the predicted */ |
| /* reduction. */ |
| ratio = 0.; |
| if (prered != 0.) |
| ratio = actred / prered; |
| |
| /* update the step bound. */ |
| if (ratio <= Scalar(.25)) { |
| if (actred >= 0.) |
| temp = Scalar(.5); |
| if (actred < 0.) |
| temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred); |
| if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1)) |
| temp = Scalar(.1); |
| /* Computing MIN */ |
| delta = temp * (std::min)(delta, pnorm / Scalar(.1)); |
| par /= temp; |
| } else if (!(par != 0. && ratio < Scalar(.75))) { |
| delta = pnorm / Scalar(.5); |
| par = Scalar(.5) * par; |
| } |
| |
| /* test for successful iteration. */ |
| if (ratio >= Scalar(1e-4)) { |
| /* successful iteration. update x, fvec, and their norms. */ |
| x = wa2; |
| wa2 = diag.cwiseProduct(x); |
| fvec = wa4; |
| xnorm = wa2.stableNorm(); |
| fnorm = fnorm1; |
| ++iter; |
| } |
| |
| /* tests for convergence. */ |
| if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) |
| return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall; |
| if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) |
| return LevenbergMarquardtSpace::RelativeReductionTooSmall; |
| if (delta <= parameters.xtol * xnorm) |
| return LevenbergMarquardtSpace::RelativeErrorTooSmall; |
| |
| /* tests for termination and stringent tolerances. */ |
| if (nfev >= parameters.maxfev) |
| return LevenbergMarquardtSpace::TooManyFunctionEvaluation; |
| if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) |
| return LevenbergMarquardtSpace::FtolTooSmall; |
| if (delta <= NumTraits<Scalar>::epsilon() * xnorm) |
| return LevenbergMarquardtSpace::XtolTooSmall; |
| if (gnorm <= NumTraits<Scalar>::epsilon()) |
| return LevenbergMarquardtSpace::GtolTooSmall; |
| |
| } while (ratio < Scalar(1e-4)); |
| |
| return LevenbergMarquardtSpace::Running; |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::lmstr1( |
| FVectorType &x, |
| const Scalar tol |
| ) |
| { |
| n = x.size(); |
| m = functor.values(); |
| |
| /* check the input parameters for errors. */ |
| if (n <= 0 || m < n || tol < 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| resetParameters(); |
| parameters.ftol = tol; |
| parameters.xtol = tol; |
| parameters.maxfev = 100*(n+1); |
| |
| return minimizeOptimumStorage(x); |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(FVectorType &x) |
| { |
| n = x.size(); |
| m = functor.values(); |
| |
| wa1.resize(n); wa2.resize(n); wa3.resize(n); |
| wa4.resize(m); |
| fvec.resize(m); |
| // Only R is stored in fjac. Q is only used to compute 'qtf', which is |
| // Q.transpose()*rhs. qtf will be updated using givens rotation, |
| // instead of storing them in Q. |
| // The purpose it to only use a nxn matrix, instead of mxn here, so |
| // that we can handle cases where m>>n : |
| fjac.resize(n, n); |
| if (!useExternalScaling) |
| diag.resize(n); |
| eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'"); |
| qtf.resize(n); |
| |
| /* Function Body */ |
| nfev = 0; |
| njev = 0; |
| |
| /* check the input parameters for errors. */ |
| if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| if (useExternalScaling) |
| for (Index j = 0; j < n; ++j) |
| if (diag[j] <= 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| /* evaluate the function at the starting point */ |
| /* and calculate its norm. */ |
| nfev = 1; |
| if ( functor(x, fvec) < 0) |
| return LevenbergMarquardtSpace::UserAsked; |
| fnorm = fvec.stableNorm(); |
| |
| /* initialize levenberg-marquardt parameter and iteration counter. */ |
| par = 0.; |
| iter = 1; |
| |
| return LevenbergMarquardtSpace::NotStarted; |
| } |
| |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorType &x) |
| { |
| using std::abs; |
| using std::sqrt; |
| |
| eigen_assert(x.size()==n); // check the caller is not cheating us |
| |
| Index i, j; |
| bool sing; |
| |
| /* compute the qr factorization of the jacobian matrix */ |
| /* calculated one row at a time, while simultaneously */ |
| /* forming (q transpose)*fvec and storing the first */ |
| /* n components in qtf. */ |
| qtf.fill(0.); |
| fjac.fill(0.); |
| Index rownb = 2; |
| for (i = 0; i < m; ++i) { |
| if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked; |
| internal::rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]); |
| ++rownb; |
| } |
| ++njev; |
| |
| /* if the jacobian is rank deficient, call qrfac to */ |
| /* reorder its columns and update the components of qtf. */ |
| sing = false; |
| for (j = 0; j < n; ++j) { |
| if (fjac(j,j) == 0.) |
| sing = true; |
| wa2[j] = fjac.col(j).head(j).stableNorm(); |
| } |
| permutation.setIdentity(n); |
| if (sing) { |
| wa2 = fjac.colwise().blueNorm(); |
| // TODO We have no unit test covering this code path, do not modify |
| // until it is carefully tested |
| ColPivHouseholderQR<JacobianType> qrfac(fjac); |
| fjac = qrfac.matrixQR(); |
| wa1 = fjac.diagonal(); |
| fjac.diagonal() = qrfac.hCoeffs(); |
| permutation = qrfac.colsPermutation(); |
| // TODO : avoid this: |
| for(Index ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors |
| |
| for (j = 0; j < n; ++j) { |
| if (fjac(j,j) != 0.) { |
| sum = 0.; |
| for (i = j; i < n; ++i) |
| sum += fjac(i,j) * qtf[i]; |
| temp = -sum / fjac(j,j); |
| for (i = j; i < n; ++i) |
| qtf[i] += fjac(i,j) * temp; |
| } |
| fjac(j,j) = wa1[j]; |
| } |
| } |
| |
| /* on the first iteration and if external scaling is not used, scale according */ |
| /* to the norms of the columns of the initial jacobian. */ |
| if (iter == 1) { |
| if (!useExternalScaling) |
| for (j = 0; j < n; ++j) |
| diag[j] = (wa2[j]==0.)? 1. : wa2[j]; |
| |
| /* on the first iteration, calculate the norm of the scaled x */ |
| /* and initialize the step bound delta. */ |
| xnorm = diag.cwiseProduct(x).stableNorm(); |
| delta = parameters.factor * xnorm; |
| if (delta == 0.) |
| delta = parameters.factor; |
| } |
| |
| /* compute the norm of the scaled gradient. */ |
| gnorm = 0.; |
| if (fnorm != 0.) |
| for (j = 0; j < n; ++j) |
| if (wa2[permutation.indices()[j]] != 0.) |
| gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); |
| |
| /* test for convergence of the gradient norm. */ |
| if (gnorm <= parameters.gtol) |
| return LevenbergMarquardtSpace::CosinusTooSmall; |
| |
| /* rescale if necessary. */ |
| if (!useExternalScaling) |
| diag = diag.cwiseMax(wa2); |
| |
| do { |
| |
| /* determine the levenberg-marquardt parameter. */ |
| internal::lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1); |
| |
| /* store the direction p and x + p. calculate the norm of p. */ |
| wa1 = -wa1; |
| wa2 = x + wa1; |
| pnorm = diag.cwiseProduct(wa1).stableNorm(); |
| |
| /* on the first iteration, adjust the initial step bound. */ |
| if (iter == 1) |
| delta = (std::min)(delta,pnorm); |
| |
| /* evaluate the function at x + p and calculate its norm. */ |
| if ( functor(wa2, wa4) < 0) |
| return LevenbergMarquardtSpace::UserAsked; |
| ++nfev; |
| fnorm1 = wa4.stableNorm(); |
| |
| /* compute the scaled actual reduction. */ |
| actred = -1.; |
| if (Scalar(.1) * fnorm1 < fnorm) |
| actred = 1. - numext::abs2(fnorm1 / fnorm); |
| |
| /* compute the scaled predicted reduction and */ |
| /* the scaled directional derivative. */ |
| wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1); |
| temp1 = numext::abs2(wa3.stableNorm() / fnorm); |
| temp2 = numext::abs2(sqrt(par) * pnorm / fnorm); |
| prered = temp1 + temp2 / Scalar(.5); |
| dirder = -(temp1 + temp2); |
| |
| /* compute the ratio of the actual to the predicted */ |
| /* reduction. */ |
| ratio = 0.; |
| if (prered != 0.) |
| ratio = actred / prered; |
| |
| /* update the step bound. */ |
| if (ratio <= Scalar(.25)) { |
| if (actred >= 0.) |
| temp = Scalar(.5); |
| if (actred < 0.) |
| temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred); |
| if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1)) |
| temp = Scalar(.1); |
| /* Computing MIN */ |
| delta = temp * (std::min)(delta, pnorm / Scalar(.1)); |
| par /= temp; |
| } else if (!(par != 0. && ratio < Scalar(.75))) { |
| delta = pnorm / Scalar(.5); |
| par = Scalar(.5) * par; |
| } |
| |
| /* test for successful iteration. */ |
| if (ratio >= Scalar(1e-4)) { |
| /* successful iteration. update x, fvec, and their norms. */ |
| x = wa2; |
| wa2 = diag.cwiseProduct(x); |
| fvec = wa4; |
| xnorm = wa2.stableNorm(); |
| fnorm = fnorm1; |
| ++iter; |
| } |
| |
| /* tests for convergence. */ |
| if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) |
| return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall; |
| if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) |
| return LevenbergMarquardtSpace::RelativeReductionTooSmall; |
| if (delta <= parameters.xtol * xnorm) |
| return LevenbergMarquardtSpace::RelativeErrorTooSmall; |
| |
| /* tests for termination and stringent tolerances. */ |
| if (nfev >= parameters.maxfev) |
| return LevenbergMarquardtSpace::TooManyFunctionEvaluation; |
| if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) |
| return LevenbergMarquardtSpace::FtolTooSmall; |
| if (delta <= NumTraits<Scalar>::epsilon() * xnorm) |
| return LevenbergMarquardtSpace::XtolTooSmall; |
| if (gnorm <= NumTraits<Scalar>::epsilon()) |
| return LevenbergMarquardtSpace::GtolTooSmall; |
| |
| } while (ratio < Scalar(1e-4)); |
| |
| return LevenbergMarquardtSpace::Running; |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(FVectorType &x) |
| { |
| LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x); |
| if (status==LevenbergMarquardtSpace::ImproperInputParameters) |
| return status; |
| do { |
| status = minimizeOptimumStorageOneStep(x); |
| } while (status==LevenbergMarquardtSpace::Running); |
| return status; |
| } |
| |
| template<typename FunctorType, typename Scalar> |
| LevenbergMarquardtSpace::Status |
| LevenbergMarquardt<FunctorType,Scalar>::lmdif1( |
| FunctorType &functor, |
| FVectorType &x, |
| Index *nfev, |
| const Scalar tol |
| ) |
| { |
| Index n = x.size(); |
| Index m = functor.values(); |
| |
| /* check the input parameters for errors. */ |
| if (n <= 0 || m < n || tol < 0.) |
| return LevenbergMarquardtSpace::ImproperInputParameters; |
| |
| NumericalDiff<FunctorType> numDiff(functor); |
| // embedded LevenbergMarquardt |
| LevenbergMarquardt<NumericalDiff<FunctorType>, Scalar > lm(numDiff); |
| lm.parameters.ftol = tol; |
| lm.parameters.xtol = tol; |
| lm.parameters.maxfev = 200*(n+1); |
| |
| LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x)); |
| if (nfev) |
| * nfev = lm.nfev; |
| return info; |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_LEVENBERGMARQUARDT__H |
| |
| //vim: ai ts=4 sts=4 et sw=4 |
