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diff --git a/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
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--- a/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
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@@ -1,396 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-//
-// The algorithm of this class initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-//
-// 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
-
-
-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
-    };
-}
-
-template <typename _Scalar, int NX=Dynamic, int NY=Dynamic>
-struct DenseFunctor
-{
-  typedef _Scalar Scalar;
-  enum {
-    InputsAtCompileTime = NX,
-    ValuesAtCompileTime = NY
-  };
-  typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
-  typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
-  typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
-  typedef ColPivHouseholderQR<JacobianType> QRSolver;
-  const int m_inputs, m_values;
-
-  DenseFunctor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
-  DenseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
-
-  int inputs() const { return m_inputs; }
-  int values() const { return m_values; }
-
-  //int operator()(const InputType &x, ValueType& fvec) { }
-  // should be defined in derived classes
-  
-  //int df(const InputType &x, JacobianType& fjac) { }
-  // should be defined in derived classes
-};
-
-template <typename _Scalar, typename _Index>
-struct SparseFunctor
-{
-  typedef _Scalar Scalar;
-  typedef _Index Index;
-  typedef Matrix<Scalar,Dynamic,1> InputType;
-  typedef Matrix<Scalar,Dynamic,1> ValueType;
-  typedef SparseMatrix<Scalar, ColMajor, Index> JacobianType;
-  typedef SparseQR<JacobianType, COLAMDOrdering<int> > QRSolver;
-  enum {
-    InputsAtCompileTime = Dynamic,
-    ValuesAtCompileTime = Dynamic
-  };
-  
-  SparseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
-
-  int inputs() const { return m_inputs; }
-  int values() const { return m_values; }
-  
-  const int m_inputs, m_values;
-  //int operator()(const InputType &x, ValueType& fvec) { }
-  // to be defined in the functor
-  
-  //int df(const InputType &x, JacobianType& fjac) { }
-  // to be defined in the functor if no automatic differentiation
-  
-};
-namespace internal {
-template <typename QRSolver, typename VectorType>
-void lmpar2(const QRSolver &qr, const VectorType  &diag, const VectorType  &qtb,
-	    typename VectorType::Scalar m_delta, typename VectorType::Scalar &par,
-	    VectorType  &x);
-    }
-/**
-  * \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>
-class LevenbergMarquardt : internal::no_assignment_operator
-{
-  public:
-    typedef _FunctorType FunctorType;
-    typedef typename FunctorType::QRSolver QRSolver;
-    typedef typename FunctorType::JacobianType JacobianType;
-    typedef typename JacobianType::Scalar Scalar;
-    typedef typename JacobianType::RealScalar RealScalar; 
-    typedef typename QRSolver::StorageIndex PermIndex;
-    typedef Matrix<Scalar,Dynamic,1> FVectorType;
-    typedef PermutationMatrix<Dynamic,Dynamic> PermutationType;
-  public:
-    LevenbergMarquardt(FunctorType& functor) 
-    : m_functor(functor),m_nfev(0),m_njev(0),m_fnorm(0.0),m_gnorm(0),
-      m_isInitialized(false),m_info(InvalidInput)
-    {
-      resetParameters();
-      m_useExternalScaling=false; 
-    }
-    
-    LevenbergMarquardtSpace::Status minimize(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
-    LevenbergMarquardtSpace::Status lmder1(
-      FVectorType  &x, 
-      const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
-    );
-    static LevenbergMarquardtSpace::Status lmdif1(
-            FunctorType &functor,
-            FVectorType  &x,
-            Index *nfev,
-            const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
-            );
-    
-    /** Sets the default parameters */
-    void resetParameters() 
-    {
-      using std::sqrt;        
-
-      m_factor = 100.; 
-      m_maxfev = 400; 
-      m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
-      m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
-      m_gtol = 0. ; 
-      m_epsfcn = 0. ;
-    }
-    
-    /** Sets the tolerance for the norm of the solution vector*/
-    void setXtol(RealScalar xtol) { m_xtol = xtol; }
-    
-    /** Sets the tolerance for the norm of the vector function*/
-    void setFtol(RealScalar ftol) { m_ftol = ftol; }
-    
-    /** Sets the tolerance for the norm of the gradient of the error vector*/
-    void setGtol(RealScalar gtol) { m_gtol = gtol; }
-    
-    /** Sets the step bound for the diagonal shift */
-    void setFactor(RealScalar factor) { m_factor = factor; }    
-    
-    /** Sets the error precision  */
-    void setEpsilon (RealScalar epsfcn) { m_epsfcn = epsfcn; }
-    
-    /** Sets the maximum number of function evaluation */
-    void setMaxfev(Index maxfev) {m_maxfev = maxfev; }
-    
-    /** Use an external Scaling. If set to true, pass a nonzero diagonal to diag() */
-    void setExternalScaling(bool value) {m_useExternalScaling  = value; }
-    
-    /** \returns the tolerance for the norm of the solution vector */
-    RealScalar xtol() const {return m_xtol; }
-    
-    /** \returns the tolerance for the norm of the vector function */
-    RealScalar ftol() const {return m_ftol; }
-    
-    /** \returns the tolerance for the norm of the gradient of the error vector */
-    RealScalar gtol() const {return m_gtol; }
-    
-    /** \returns the step bound for the diagonal shift */
-    RealScalar factor() const {return m_factor; }
-    
-    /** \returns the error precision */
-    RealScalar epsilon() const {return m_epsfcn; }
-    
-    /** \returns the maximum number of function evaluation */
-    Index maxfev() const {return m_maxfev; }
-    
-    /** \returns a reference to the diagonal of the jacobian */
-    FVectorType& diag() {return m_diag; }
-    
-    /** \returns the number of iterations performed */
-    Index iterations() { return m_iter; }
-    
-    /** \returns the number of functions evaluation */
-    Index nfev() { return m_nfev; }
-    
-    /** \returns the number of jacobian evaluation */
-    Index njev() { return m_njev; }
-    
-    /** \returns the norm of current vector function */
-    RealScalar fnorm() {return m_fnorm; }
-    
-    /** \returns the norm of the gradient of the error */
-    RealScalar gnorm() {return m_gnorm; }
-    
-    /** \returns the LevenbergMarquardt parameter */
-    RealScalar lm_param(void) { return m_par; }
-    
-    /** \returns a reference to the  current vector function 
-     */
-    FVectorType& fvec() {return m_fvec; }
-    
-    /** \returns a reference to the matrix where the current Jacobian matrix is stored
-     */
-    JacobianType& jacobian() {return m_fjac; }
-    
-    /** \returns a reference to the triangular matrix R from the QR of the jacobian matrix.
-     * \sa jacobian()
-     */
-    JacobianType& matrixR() {return m_rfactor; }
-    
-    /** the permutation used in the QR factorization
-     */
-    PermutationType permutation() {return m_permutation; }
-    
-    /** 
-     * \brief Reports whether the minimization was successful
-     * \returns \c Success if the minimization was succesful,
-     *         \c NumericalIssue if a numerical problem arises during the 
-     *          minimization process, for exemple during the QR factorization
-     *         \c NoConvergence if the minimization did not converge after 
-     *          the maximum number of function evaluation allowed
-     *          \c InvalidInput if the input matrix is invalid
-     */
-    ComputationInfo info() const
-    {
-      
-      return m_info;
-    }
-  private:
-    JacobianType m_fjac; 
-    JacobianType m_rfactor; // The triangular matrix R from the QR of the jacobian matrix m_fjac
-    FunctorType &m_functor;
-    FVectorType m_fvec, m_qtf, m_diag; 
-    Index n;
-    Index m; 
-    Index m_nfev;
-    Index m_njev; 
-    RealScalar m_fnorm; // Norm of the current vector function
-    RealScalar m_gnorm; //Norm of the gradient of the error 
-    RealScalar m_factor; //
-    Index m_maxfev; // Maximum number of function evaluation
-    RealScalar m_ftol; //Tolerance in the norm of the vector function
-    RealScalar m_xtol; // 
-    RealScalar m_gtol; //tolerance of the norm of the error gradient
-    RealScalar m_epsfcn; //
-    Index m_iter; // Number of iterations performed
-    RealScalar m_delta;
-    bool m_useExternalScaling;
-    PermutationType m_permutation;
-    FVectorType m_wa1, m_wa2, m_wa3, m_wa4; //Temporary vectors
-    RealScalar m_par;
-    bool m_isInitialized; // Check whether the minimization step has been called
-    ComputationInfo m_info; 
-};
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::minimize(FVectorType  &x)
-{
-    LevenbergMarquardtSpace::Status status = minimizeInit(x);
-    if (status==LevenbergMarquardtSpace::ImproperInputParameters) {
-      m_isInitialized = true;
-      return status;
-    }
-    do {
-//       std::cout << " uv " << x.transpose() << "\n";
-        status = minimizeOneStep(x);
-    } while (status==LevenbergMarquardtSpace::Running);
-     m_isInitialized = true;
-     return status;
-}
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType  &x)
-{
-    n = x.size();
-    m = m_functor.values();
-
-    m_wa1.resize(n); m_wa2.resize(n); m_wa3.resize(n);
-    m_wa4.resize(m);
-    m_fvec.resize(m);
-    //FIXME Sparse Case : Allocate space for the jacobian
-    m_fjac.resize(m, n);
-//     m_fjac.reserve(VectorXi::Constant(n,5)); // FIXME Find a better alternative
-    if (!m_useExternalScaling)
-        m_diag.resize(n);
-    eigen_assert( (!m_useExternalScaling || m_diag.size()==n) && "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
-    m_qtf.resize(n);
-
-    /* Function Body */
-    m_nfev = 0;
-    m_njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || m < n || m_ftol < 0. || m_xtol < 0. || m_gtol < 0. || m_maxfev <= 0 || m_factor <= 0.){
-      m_info = InvalidInput;
-      return LevenbergMarquardtSpace::ImproperInputParameters;
-    }
-
-    if (m_useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (m_diag[j] <= 0.) 
-            {
-              m_info = InvalidInput;
-              return LevenbergMarquardtSpace::ImproperInputParameters;
-            }
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    m_nfev = 1;
-    if ( m_functor(x, m_fvec) < 0)
-        return LevenbergMarquardtSpace::UserAsked;
-    m_fnorm = m_fvec.stableNorm();
-
-    /*     initialize levenberg-marquardt parameter and iteration counter. */
-    m_par = 0.;
-    m_iter = 1;
-
-    return LevenbergMarquardtSpace::NotStarted;
-}
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::lmder1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-    m = m_functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    resetParameters();
-    m_ftol = tol;
-    m_xtol = tol;
-    m_maxfev = 100*(n+1);
-
-    return minimize(x);
-}
-
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::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> > lm(numDiff);
-    lm.setFtol(tol);
-    lm.setXtol(tol);
-    lm.setMaxfev(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