diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index 258297072..5c832b73d 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -28,6 +28,19 @@
 #ifndef EIGEN_HYBRIDNONLINEARSOLVER_H
 #define EIGEN_HYBRIDNONLINEARSOLVER_H
 
+namespace HybridNonLinearSolverSpace { 
+    enum Status {
+        Running = -1,
+        ImproperInputParameters = 0,
+        RelativeErrorTooSmall = 1,
+        TooManyFunctionEvaluation = 2,
+        TolTooSmall = 3,
+        NotMakingProgressJacobian = 4,
+        NotMakingProgressIterations = 5,
+        UserAksed = 6
+    };
+}
+
 /**
   * \ingroup NonLinearOptimization_Module
   * \brief Finds a zero of a system of n
@@ -46,17 +59,6 @@ public:
     HybridNonLinearSolver(FunctorType &_functor)
         : functor(_functor) { nfev=njev=iter = 0;  fnorm= 0.; }
 
-    enum Status {
-        Running = -1,
-        ImproperInputParameters = 0,
-        RelativeErrorTooSmall = 1,
-        TooManyFunctionEvaluation = 2,
-        TolTooSmall = 3,
-        NotMakingProgressJacobian = 4,
-        NotMakingProgressIterations = 5,
-        UserAksed = 6
-    };
-
     struct Parameters {
         Parameters()
             : factor(Scalar(100.))
@@ -77,38 +79,38 @@ public:
     /* TODO: if eigen provides a triangular storage, use it here */
     typedef Matrix< Scalar, Dynamic, Dynamic > UpperTriangularType;
 
-    Status hybrj1(
+    HybridNonLinearSolverSpace::Status hybrj1(
             FVectorType  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
-    Status solveInit(
+    HybridNonLinearSolverSpace::Status solveInit(
             FVectorType  &x,
             const int mode=1
             );
-    Status solveOneStep(
+    HybridNonLinearSolverSpace::Status solveOneStep(
             FVectorType  &x,
             const int mode=1
             );
-    Status solve(
+    HybridNonLinearSolverSpace::Status solve(
             FVectorType  &x,
             const int mode=1
             );
 
-    Status hybrd1(
+    HybridNonLinearSolverSpace::Status hybrd1(
             FVectorType  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
-    Status solveNumericalDiffInit(
+    HybridNonLinearSolverSpace::Status solveNumericalDiffInit(
             FVectorType  &x,
             const int mode=1
             );
-    Status solveNumericalDiffOneStep(
+    HybridNonLinearSolverSpace::Status solveNumericalDiffOneStep(
             FVectorType  &x,
             const int mode=1
             );
-    Status solveNumericalDiff(
+    HybridNonLinearSolverSpace::Status solveNumericalDiff(
             FVectorType  &x,
             const int mode=1
             );
@@ -142,7 +144,7 @@ private:
 
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
         FVectorType  &x,
         const Scalar tol
@@ -152,7 +154,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
 
     /* check the input parameters for errors. */
     if (n <= 0 || tol < 0.)
-        return ImproperInputParameters;
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
 
     resetParameters();
     parameters.maxfev = 100*(n+1);
@@ -165,7 +167,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
         FVectorType  &x,
         const int mode
@@ -187,17 +189,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
 
     /*     check the input parameters for errors. */
     if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
-        return ImproperInputParameters;
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
     if (mode == 2)
         for (int j = 0; j < n; ++j)
             if (diag[j] <= 0.)
-                return ImproperInputParameters;
+                return HybridNonLinearSolverSpace::ImproperInputParameters;
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return UserAksed;
+        return HybridNonLinearSolverSpace::UserAksed;
     fnorm = fvec.stableNorm();
 
     /*     initialize iteration counter and monitors. */
@@ -207,11 +209,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
     nslow1 = 0;
     nslow2 = 0;
 
-    return Running;
+    return HybridNonLinearSolverSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
         FVectorType  &x,
         const int mode
@@ -224,7 +226,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
     /* calculate the jacobian matrix. */
     if ( functor.df(x, fjac) < 0)
-        return UserAksed;
+        return HybridNonLinearSolverSpace::UserAksed;
     ++njev;
 
     wa2 = fjac.colwise().blueNorm();
@@ -276,7 +278,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return UserAksed;
+            return HybridNonLinearSolverSpace::UserAksed;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -334,17 +336,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
         /* test for convergence. */
         if (delta <= parameters.xtol * xnorm || fnorm == 0.)
-            return RelativeErrorTooSmall;
+            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
-            return TooManyFunctionEvaluation;
+            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
         if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
-            return TolTooSmall;
+            return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
-            return NotMakingProgressJacobian;
+            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
         if (nslow1 == 10)
-            return NotMakingProgressIterations;
+            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
 
         /* criterion for recalculating jacobian. */
         if (ncfail == 2)
@@ -365,18 +367,18 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
         jeval = false;
     }
-    return Running;
+    return HybridNonLinearSolverSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solve(
         FVectorType  &x,
         const int mode
         )
 {
-    Status status = solveInit(x, mode);
-    while (status==Running)
+    HybridNonLinearSolverSpace::Status status = solveInit(x, mode);
+    while (status==HybridNonLinearSolverSpace::Running)
         status = solveOneStep(x, mode);
     return status;
 }
@@ -384,7 +386,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
 
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
         FVectorType  &x,
         const Scalar tol
@@ -394,7 +396,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
 
     /* check the input parameters for errors. */
     if (n <= 0 || tol < 0.)
-        return ImproperInputParameters;
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
 
     resetParameters();
     parameters.maxfev = 200*(n+1);
@@ -408,7 +410,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
         FVectorType  &x,
         const int mode
@@ -434,17 +436,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
 
     /*     check the input parameters for errors. */
     if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
-        return ImproperInputParameters;
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
     if (mode == 2)
         for (int j = 0; j < n; ++j)
             if (diag[j] <= 0.)
-                return ImproperInputParameters;
+                return HybridNonLinearSolverSpace::ImproperInputParameters;
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return UserAksed;
+        return HybridNonLinearSolverSpace::UserAksed;
     fnorm = fvec.stableNorm();
 
     /*     initialize iteration counter and monitors. */
@@ -454,11 +456,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
     nslow1 = 0;
     nslow2 = 0;
 
-    return Running;
+    return HybridNonLinearSolverSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
         FVectorType  &x,
         const int mode
@@ -473,7 +475,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
     /* calculate the jacobian matrix. */
     if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
-        return UserAksed;
+        return HybridNonLinearSolverSpace::UserAksed;
     nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
 
     wa2 = fjac.colwise().blueNorm();
@@ -525,7 +527,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return UserAksed;
+            return HybridNonLinearSolverSpace::UserAksed;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -583,17 +585,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         /* test for convergence. */
         if (delta <= parameters.xtol * xnorm || fnorm == 0.)
-            return RelativeErrorTooSmall;
+            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
-            return TooManyFunctionEvaluation;
+            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
         if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
-            return TolTooSmall;
+            return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
-            return NotMakingProgressJacobian;
+            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
         if (nslow1 == 10)
-            return NotMakingProgressIterations;
+            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
 
         /* criterion for recalculating jacobian. */
         if (ncfail == 2)
@@ -614,18 +616,18 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         jeval = false;
     }
-    return Running;
+    return HybridNonLinearSolverSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolverSpace::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
         FVectorType  &x,
         const int mode
         )
 {
-    Status status = solveNumericalDiffInit(x, mode);
-    while (status==Running)
+    HybridNonLinearSolverSpace::Status status = solveNumericalDiffInit(x, mode);
+    while (status==HybridNonLinearSolverSpace::Running)
         status = solveNumericalDiffOneStep(x, mode);
     return status;
 }
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index 076110651..7457c688e 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -28,21 +28,8 @@
 #ifndef EIGEN_LEVENBERGMARQUARDT__H
 #define EIGEN_LEVENBERGMARQUARDT__H
 
-/**
-  * \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
-{
-public:
-    LevenbergMarquardt(FunctorType &_functor)
-        : functor(_functor) { nfev = njev = iter = 0;  fnorm=gnorm = 0.; }
 
+namespace LevenbergMarquardtSpace {
     enum Status {
         NotStarted = -2,
         Running = -1,
@@ -57,6 +44,24 @@ public:
         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
+{
+public:
+    LevenbergMarquardt(FunctorType &_functor)
+        : functor(_functor) { nfev = njev = iter = 0;  fnorm=gnorm = 0.; }
 
     struct Parameters {
         Parameters()
@@ -77,45 +82,45 @@ public:
     typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
     typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
 
-    Status lmder1(
+    LevenbergMarquardtSpace::Status lmder1(
             FVectorType &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
-    Status minimize(
+    LevenbergMarquardtSpace::Status minimize(
             FVectorType &x,
             const int mode=1
             );
-    Status minimizeInit(
+    LevenbergMarquardtSpace::Status minimizeInit(
             FVectorType &x,
             const int mode=1
             );
-    Status minimizeOneStep(
+    LevenbergMarquardtSpace::Status minimizeOneStep(
             FVectorType &x,
             const int mode=1
             );
 
-    static Status lmdif1(
+    static LevenbergMarquardtSpace::Status lmdif1(
             FunctorType &functor,
             FVectorType &x,
             int *nfev,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
-    Status lmstr1(
+    LevenbergMarquardtSpace::Status lmstr1(
             FVectorType  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
-    Status minimizeOptimumStorage(
+    LevenbergMarquardtSpace::Status minimizeOptimumStorage(
             FVectorType  &x,
             const int mode=1
             );
-    Status minimizeOptimumStorageInit(
+    LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(
             FVectorType  &x,
             const int mode=1
             );
-    Status minimizeOptimumStorageOneStep(
+    LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(
             FVectorType  &x,
             const int mode=1
             );
@@ -146,7 +151,7 @@ private:
 };
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::lmder1(
         FVectorType  &x,
         const Scalar tol
@@ -157,7 +162,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmder1(
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
-        return ImproperInputParameters;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
 
     resetParameters();
     parameters.ftol = tol;
@@ -169,21 +174,21 @@ LevenbergMarquardt<FunctorType,Scalar>::lmder1(
 
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimize(
         FVectorType  &x,
         const int mode
         )
 {
-    Status status = minimizeInit(x, mode);
+    LevenbergMarquardtSpace::Status status = minimizeInit(x, mode);
     do {
         status = minimizeOneStep(x, mode);
-    } while (status==Running);
+    } while (status==LevenbergMarquardtSpace::Running);
     return status;
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
         FVectorType  &x,
         const int mode
@@ -207,29 +212,29 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
 
     /*     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 ImproperInputParameters;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
 
     if (mode == 2)
         for (int j = 0; j < n; ++j)
             if (diag[j] <= 0.)
-                return ImproperInputParameters;
+                return LevenbergMarquardtSpace::ImproperInputParameters;
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return UserAsked;
+        return LevenbergMarquardtSpace::UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize levenberg-marquardt parameter and iteration counter. */
     par = 0.;
     iter = 1;
 
-    return NotStarted;
+    return LevenbergMarquardtSpace::NotStarted;
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         FVectorType  &x,
         const int mode
@@ -240,7 +245,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
     /* calculate the jacobian matrix. */
     int df_ret = functor.df(x, fjac);
     if (df_ret<0)
-        return UserAsked;
+        return LevenbergMarquardtSpace::UserAsked;
     if (df_ret>0)
         // numerical diff, we evaluated the function df_ret times
         nfev += df_ret;
@@ -282,7 +287,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
-        return CosinusTooSmall;
+        return LevenbergMarquardtSpace::CosinusTooSmall;
 
     /* rescale if necessary. */
     if (mode != 2)
@@ -304,7 +309,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return UserAsked;
+            return LevenbergMarquardtSpace::UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -356,29 +361,29 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         /* tests for convergence. */
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
-            return RelativeErrorAndReductionTooSmall;
+            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
-            return RelativeReductionTooSmall;
+            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
         if (delta <= parameters.xtol * xnorm)
-            return RelativeErrorTooSmall;
+            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
-            return TooManyFunctionEvaluation;
+            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
         if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
-            return FtolTooSmall;
+            return LevenbergMarquardtSpace::FtolTooSmall;
         if (delta <= epsilon<Scalar>() * xnorm)
-            return XtolTooSmall;
+            return LevenbergMarquardtSpace::XtolTooSmall;
         if (gnorm <= epsilon<Scalar>())
-            return GtolTooSmall;
+            return LevenbergMarquardtSpace::GtolTooSmall;
 
     } while (ratio < Scalar(1e-4));
 
-    return Running;
+    return LevenbergMarquardtSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
         FVectorType  &x,
         const Scalar tol
@@ -389,7 +394,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
-        return ImproperInputParameters;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
 
     resetParameters();
     parameters.ftol = tol;
@@ -400,7 +405,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
         FVectorType  &x,
         const int mode
@@ -424,30 +429,30 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
 
     /*     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 ImproperInputParameters;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
 
     if (mode == 2)
         for (int j = 0; j < n; ++j)
             if (diag[j] <= 0.)
-                return ImproperInputParameters;
+                return LevenbergMarquardtSpace::ImproperInputParameters;
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return UserAsked;
+        return LevenbergMarquardtSpace::UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize levenberg-marquardt parameter and iteration counter. */
     par = 0.;
     iter = 1;
 
-    return NotStarted;
+    return LevenbergMarquardtSpace::NotStarted;
 }
 
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         FVectorType  &x,
         const int mode
@@ -464,7 +469,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     fjac.fill(0.);
     int rownb = 2;
     for (i = 0; i < m; ++i) {
-        if (functor.df(x, wa3, rownb) < 0) return UserAsked;
+        if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
         ei_rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
         ++rownb;
     }
@@ -528,7 +533,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
-        return CosinusTooSmall;
+        return LevenbergMarquardtSpace::CosinusTooSmall;
 
     /* rescale if necessary. */
     if (mode != 2)
@@ -550,7 +555,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return UserAsked;
+            return LevenbergMarquardtSpace::UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -602,43 +607,43 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         /* tests for convergence. */
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
-            return RelativeErrorAndReductionTooSmall;
+            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
-            return RelativeReductionTooSmall;
+            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
         if (delta <= parameters.xtol * xnorm)
-            return RelativeErrorTooSmall;
+            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
-            return TooManyFunctionEvaluation;
+            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
         if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
-            return FtolTooSmall;
+            return LevenbergMarquardtSpace::FtolTooSmall;
         if (delta <= epsilon<Scalar>() * xnorm)
-            return XtolTooSmall;
+            return LevenbergMarquardtSpace::XtolTooSmall;
         if (gnorm <= epsilon<Scalar>())
-            return GtolTooSmall;
+            return LevenbergMarquardtSpace::GtolTooSmall;
 
     } while (ratio < Scalar(1e-4));
 
-    return Running;
+    return LevenbergMarquardtSpace::Running;
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
         FVectorType  &x,
         const int mode
         )
 {
-    Status status = minimizeOptimumStorageInit(x, mode);
+    LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x, mode);
     do {
         status = minimizeOptimumStorageOneStep(x, mode);
-    } while (status==Running);
+    } while (status==LevenbergMarquardtSpace::Running);
     return status;
 }
 
 template<typename FunctorType, typename Scalar>
-typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardtSpace::Status
 LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
         FunctorType &functor,
         FVectorType  &x,
@@ -651,7 +656,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
-        return ImproperInputParameters;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
 
     NumericalDiff<FunctorType> numDiff(functor);
     // embedded LevenbergMarquardt
@@ -660,7 +665,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
     lm.parameters.xtol = tol;
     lm.parameters.maxfev = 200*(n+1);
 
-    Status info = Status(lm.minimize(x));
+    LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
     if (nfev)
         * nfev = lm.nfev;
     return info;