From 458947af5e6a6712b313e205eba73e2978d05e0f Mon Sep 17 00:00:00 2001
From: Thomas Capricelli <orzel@freehackers.org>
Date: Wed, 26 Aug 2009 14:23:05 +0200
Subject: [PATCH] move Parameters as a class member, simplify calling sequence.
 Convenience methods from minpack ( "*1()" ) get their original name back :
 they are only useful when porting, anyway. Still, i prefer to keep them.

---
 .../src/NonLinear/HybridNonLinearSolver.h     |  54 +++----
 .../Eigen/src/NonLinear/LevenbergMarquardt.h  |  65 +++-----
 unsupported/test/NonLinear.cpp                | 153 ++++++++----------
 3 files changed, 106 insertions(+), 166 deletions(-)

diff --git a/unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
index 5e5204ed7..5c3eb9d28 100644
--- a/unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
@@ -33,48 +33,44 @@ public:
         Scalar epsfcn;
     };
 
-    Status solve(
+    Status hybrj1(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
     Status solveInit(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status solveOneStep(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status solve(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
 
-    Status solveNumericalDiff(
+    Status hybrd1(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
     Status solveNumericalDiffInit(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status solveNumericalDiffOneStep(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status solveNumericalDiff(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
 
+    void resetParameters(void) { parameters = Parameters(); }
+    Parameters parameters;
     Matrix< Scalar, Dynamic, 1 >  fvec;
     Matrix< Scalar, Dynamic, Dynamic > fjac;
     Matrix< Scalar, Dynamic, 1 >  R;
@@ -105,24 +101,23 @@ private:
 
 template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solve(
+HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Scalar tol
         )
 {
     n = x.size();
-    Parameters parameters;
 
     /* check the input parameters for errors. */
     if (n <= 0 || tol < 0.)
         return ImproperInputParameters;
 
+    resetParameters();
     parameters.maxfev = 100*(n+1);
     parameters.xtol = tol;
     diag.setConstant(n, 1.);
     return solve(
         x, 
-        parameters,
         2
     );
 }
@@ -131,7 +126,6 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -182,7 +176,6 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -404,13 +397,12 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solve(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
-    Status status = solveInit(x, parameters, mode);
+    Status status = solveInit(x, mode);
     while (status==Running)
-        status = solveOneStep(x, parameters, mode);
+        status = solveOneStep(x, mode);
     return status;
 }
 
@@ -418,25 +410,24 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
 
 template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
+HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Scalar tol
         )
 {
     n = x.size();
-    Parameters parameters;
 
     /* check the input parameters for errors. */
     if (n <= 0 || tol < 0.)
         return ImproperInputParameters;
 
+    resetParameters();
     parameters.maxfev = 200*(n+1);
     parameters.xtol = tol;
 
     diag.setConstant(n, 1.);
     return solveNumericalDiff(
         x,
-        parameters,
         2
     );
 }
@@ -445,16 +436,13 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
     n = x.size();
 
-    int nsub = parameters.nb_of_subdiagonals;
-    int nsup = parameters.nb_of_superdiagonals;
-    if (nsub<0) nsub= n-1;
-    if (nsup<0) nsup= n-1;
+    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
+    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
 
     wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
     qtf.resize(n);
@@ -472,7 +460,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
 
     /*     check the input parameters for errors. */
 
-    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || nsub< 0 || nsup< 0 || parameters.factor <= 0. )
+    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
         return ImproperInputParameters;
     if (mode == 2)
         for (int j = 0; j < n; ++j)
@@ -502,22 +490,19 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
     int i, j, l, iwa[1];
     jeval = true;
-    int nsub = parameters.nb_of_subdiagonals;
-    int nsup = parameters.nb_of_superdiagonals;
-    if (nsub<0) nsub= n-1;
-    if (nsup<0) nsup= n-1;
+    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
+    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
 
     /* calculate the jacobian matrix. */
 
-    if (ei_fdjac1(functor, x, fvec, fjac, nsub, nsup, parameters.epsfcn) <0)
+    if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
         return UserAksed;
-    nfev += std::min(nsub+ nsup+ 1, n);
+    nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
 
     /* compute the qr factorization of the jacobian. */
 
@@ -728,13 +713,12 @@ template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
 HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
-    Status status = solveNumericalDiffInit(x, parameters, mode);
+    Status status = solveNumericalDiffInit(x, mode);
     while (status==Running)
-        status = solveNumericalDiffOneStep(x, parameters, mode);
+        status = solveNumericalDiffOneStep(x, mode);
     return status;
 }
 
diff --git a/unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
index bb164bcba..dda169c97 100644
--- a/unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
@@ -36,69 +36,62 @@ public:
         Scalar epsfcn;
     };
 
-    Status minimize(
+    Status lmder1(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
     Status minimize(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeInit(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeOneStep(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
 
-    Status minimizeNumericalDiff(
+    Status lmdif1(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
     Status minimizeNumericalDiff(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeNumericalDiffInit(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeNumericalDiffOneStep(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
 
-    Status minimizeOptimumStorage(
+    Status lmstr1(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
 
     Status minimizeOptimumStorage(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeOptimumStorageInit(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
     Status minimizeOptimumStorageOneStep(
             Matrix< Scalar, Dynamic, 1 >  &x,
-            const Parameters &parameters,
             const int mode=1
             );
 
+    void resetParameters(void) { parameters = Parameters(); }
+    Parameters parameters;
     Matrix< Scalar, Dynamic, 1 >  fvec;
     Matrix< Scalar, Dynamic, Dynamic > fjac;
     VectorXi ipvt;
@@ -123,27 +116,24 @@ private:
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimize(
+LevenbergMarquardt<FunctorType,Scalar>::lmder1(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Scalar tol
         )
 {
     n = x.size();
     m = functor.nbOfFunctions();
-    Parameters parameters;
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
         return ImproperInputParameters;
 
+    resetParameters();
     parameters.ftol = tol;
     parameters.xtol = tol;
     parameters.maxfev = 100*(n+1);
 
-    return minimize(
-        x,
-        parameters
-    );
+    return minimize(x);
 }
 
 
@@ -151,13 +141,12 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimize(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
-    Status status = minimizeInit(x, parameters, mode);
+    Status status = minimizeInit(x, mode);
     while (status==Running)
-        status = minimizeOneStep(x, parameters, mode);
+        status = minimizeOneStep(x, mode);
     return status;
 }
 
@@ -165,7 +154,6 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -216,7 +204,6 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -408,34 +395,30 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
+LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Scalar tol
         )
 {
     n = x.size();
     m = functor.nbOfFunctions();
-    Parameters parameters;
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
         return ImproperInputParameters;
 
+    resetParameters();
     parameters.ftol = tol;
     parameters.xtol = tol;
     parameters.maxfev = 200*(n+1);
 
-    return minimizeNumericalDiff(
-        x,
-        parameters
-    );
+    return minimizeNumericalDiff(x);
 }
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -484,7 +467,6 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffOneStep(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -679,20 +661,19 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
-    Status status = minimizeNumericalDiffInit(x, parameters, mode);
+    Status status = minimizeNumericalDiffInit(x, mode);
     while (status==Running)
-        status = minimizeNumericalDiffOneStep(x, parameters, mode);
+        status = minimizeNumericalDiffOneStep(x, mode);
     return status;
 }
 
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
+LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Scalar tol
         )
@@ -701,27 +682,23 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
     m = functor.nbOfFunctions();
     Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
     VectorXi ipvt;
-    Parameters parameters;
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
         return ImproperInputParameters;
 
+    resetParameters();
     parameters.ftol = tol;
     parameters.xtol = tol;
     parameters.maxfev = 100*(n+1);
 
-    return minimizeOptimumStorage(
-        x,
-        parameters
-    );
+    return minimizeOptimumStorage(x);
 }
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -773,7 +750,6 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
@@ -986,13 +962,12 @@ template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
 LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
         Matrix< Scalar, Dynamic, 1 >  &x,
-        const Parameters &parameters,
         const int mode
         )
 {
-    Status status = minimizeOptimumStorageInit(x, parameters, mode);
+    Status status = minimizeOptimumStorageInit(x, mode);
     while (status==Running)
-        status = minimizeOptimumStorageOneStep(x, parameters, mode);
+        status = minimizeOptimumStorageOneStep(x, mode);
     return status;
 }
 
diff --git a/unsupported/test/NonLinear.cpp b/unsupported/test/NonLinear.cpp
index cb1675a36..8d97ef939 100644
--- a/unsupported/test/NonLinear.cpp
+++ b/unsupported/test/NonLinear.cpp
@@ -154,7 +154,7 @@ void testLmder1()
   // do the computation
   lmder_functor functor;
   LevenbergMarquardt<lmder_functor> lm(functor);
-  info = lm.minimize(x);
+  info = lm.lmder1(x);
 
   // check return value
   VERIFY( 1 == info);
@@ -181,8 +181,7 @@ void testLmder()
   // do the computation
   lmder_functor functor;
   LevenbergMarquardt<lmder_functor> lm(functor);
-  LevenbergMarquardt<lmder_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return values
   VERIFY( 1 == info);
@@ -270,7 +269,7 @@ void testHybrj1()
   // do the computation
   hybrj_functor functor;
   HybridNonLinearSolver<hybrj_functor> solver(functor);
-  info = solver.solve(x);
+  info = solver.hybrj1(x);
 
   // check return value
   VERIFY( 1 == info);
@@ -302,8 +301,7 @@ void testHybrj()
   hybrj_functor functor;
   HybridNonLinearSolver<hybrj_functor> solver(functor);
   solver.diag.setConstant(n, 1.);
-  HybridNonLinearSolver<hybrj_functor>::Parameters parameters;
-  info = solver.solve(x, parameters, 2);
+  info = solver.solve(x, 2);
 
   // check return value
   VERIFY( 1 == info);
@@ -356,7 +354,7 @@ void testHybrd1()
   // do the computation
   hybrd_functor functor;
   HybridNonLinearSolver<hybrd_functor> solver(functor);
-  info = solver.solveNumericalDiff(x);
+  info = solver.hybrd1(x);
 
   // check return value
   VERIFY( 1 == info);
@@ -382,11 +380,10 @@ void testHybrd()
   // do the computation
   hybrd_functor functor;
   HybridNonLinearSolver<hybrd_functor> solver(functor);
-  HybridNonLinearSolver<hybrd_functor>::Parameters parameters;
-  parameters.nb_of_subdiagonals = 1;
-  parameters.nb_of_superdiagonals = 1;
+  solver.parameters.nb_of_subdiagonals = 1;
+  solver.parameters.nb_of_superdiagonals = 1;
   solver.diag.setConstant(n, 1.);
-  info = solver.solveNumericalDiff(x, parameters, 2);
+  info = solver.solveNumericalDiff(x, 2);
 
   // check return value
   VERIFY( 1 == info);
@@ -457,7 +454,7 @@ void testLmstr1()
   // do the computation
   lmstr_functor functor;
   LevenbergMarquardt<lmstr_functor> lm(functor);
-  info = lm.minimizeOptimumStorage(x);
+  info = lm.lmstr1(x);
 
   // check return value
   VERIFY( 1 == info);
@@ -484,8 +481,7 @@ void testLmstr()
   // do the computation
   lmstr_functor functor;
   LevenbergMarquardt<lmstr_functor> lm(functor);
-  LevenbergMarquardt<lmstr_functor>::Parameters parameters;
-  info = lm.minimizeOptimumStorage(x, parameters);
+  info = lm.minimizeOptimumStorage(x);
 
   // check return values
   VERIFY( 1 == info);
@@ -543,7 +539,7 @@ void testLmdif1()
   // do the computation
   lmdif_functor functor;
   LevenbergMarquardt<lmdif_functor> lm(functor);
-  info = lm.minimizeNumericalDiff(x);
+  info = lm.lmdif1(x);
 
   // check return value
   VERIFY( 1 == info);
@@ -571,8 +567,7 @@ void testLmdif()
   // do the computation
   lmdif_functor functor;
   LevenbergMarquardt<lmdif_functor> lm(functor);
-  LevenbergMarquardt<lmdif_functor>::Parameters parameters;
-  info = lm.minimizeNumericalDiff(x, parameters);
+  info = lm.minimizeNumericalDiff(x);
 
   // check return values
   VERIFY( 1 == info);
@@ -657,8 +652,7 @@ void testNistChwirut2(void)
   // do the computation
   chwirut2_functor functor;
   LevenbergMarquardt<chwirut2_functor> lm(functor);
-  LevenbergMarquardt<chwirut2_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -676,10 +670,10 @@ void testNistChwirut2(void)
    */
   x<< 0.15, 0.008, 0.010;
   // do the computation
-  parameters = LevenbergMarquardt<chwirut2_functor>::Parameters(); // get default back
-  parameters.ftol = 1.E6*epsilon<double>();
-  parameters.xtol = 1.E6*epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E6*epsilon<double>();
+  lm.parameters.xtol = 1.E6*epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -738,8 +732,7 @@ void testNistMisra1a(void)
   // do the computation
   misra1a_functor functor;
   LevenbergMarquardt<misra1a_functor> lm(functor);
-  LevenbergMarquardt<misra1a_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -756,7 +749,7 @@ void testNistMisra1a(void)
    */
   x<< 250., 0.0005;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -825,8 +818,7 @@ void testNistHahn1(void)
   // do the computation
   hahn1_functor functor;
   LevenbergMarquardt<hahn1_functor> lm(functor);
-  LevenbergMarquardt<hahn1_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -848,7 +840,7 @@ void testNistHahn1(void)
    */
   x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -912,8 +904,7 @@ void testNistMisra1d(void)
   // do the computation
   misra1d_functor functor;
   LevenbergMarquardt<misra1d_functor> lm(functor);
-  LevenbergMarquardt<misra1d_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 3 == info); 
@@ -930,7 +921,7 @@ void testNistMisra1d(void)
    */
   x<< 450., 0.0003;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -991,8 +982,7 @@ void testNistLanczos1(void)
   // do the computation
   lanczos1_functor functor;
   LevenbergMarquardt<lanczos1_functor> lm(functor);
-  LevenbergMarquardt<lanczos1_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 2 == info); 
@@ -1013,7 +1003,7 @@ void testNistLanczos1(void)
    */
   x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 2 == info); 
@@ -1078,8 +1068,7 @@ void testNistRat42(void)
   // do the computation
   rat42_functor functor;
   LevenbergMarquardt<rat42_functor> lm(functor);
-  LevenbergMarquardt<rat42_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1097,7 +1086,7 @@ void testNistRat42(void)
    */
   x<< 75., 2.5, 0.07;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1157,8 +1146,7 @@ void testNistMGH10(void)
   // do the computation
   MGH10_functor functor;
   LevenbergMarquardt<MGH10_functor> lm(functor);
-  LevenbergMarquardt<MGH10_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 2 == info); 
@@ -1176,7 +1164,7 @@ void testNistMGH10(void)
    */
   x<< 0.02, 4000., 250.;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 2 == info); 
@@ -1234,11 +1222,10 @@ void testNistBoxBOD(void)
   // do the computation
   BoxBOD_functor functor;
   LevenbergMarquardt<BoxBOD_functor> lm(functor);
-  LevenbergMarquardt<BoxBOD_functor>::Parameters parameters;
-  parameters.ftol = 1.E6*epsilon<double>();
-  parameters.xtol = 1.E6*epsilon<double>();
-  parameters.factor = 10.;
-  info = lm.minimize(x, parameters);
+  lm.parameters.ftol = 1.E6*epsilon<double>();
+  lm.parameters.xtol = 1.E6*epsilon<double>();
+  lm.parameters.factor = 10.;
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1255,10 +1242,10 @@ void testNistBoxBOD(void)
    */
   x<< 100., 0.75;
   // do the computation
-  parameters = LevenbergMarquardt<BoxBOD_functor>::Parameters(); // get default back
-  parameters.ftol = epsilon<double>();
-  parameters.xtol = epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  lm.parameters.ftol = epsilon<double>();
+  lm.parameters.xtol = epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1317,11 +1304,10 @@ void testNistMGH17(void)
   // do the computation
   MGH17_functor functor;
   LevenbergMarquardt<MGH17_functor> lm(functor);
-  LevenbergMarquardt<MGH17_functor>::Parameters parameters;
-  parameters.ftol = epsilon<double>();
-  parameters.xtol = epsilon<double>();
-  parameters.maxfev = 1000;
-  info = lm.minimize(x, parameters);
+  lm.parameters.ftol = epsilon<double>();
+  lm.parameters.xtol = epsilon<double>();
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1341,8 +1327,8 @@ void testNistMGH17(void)
    */
   x<< 0.5  ,1.5  ,-1   ,0.01 ,0.02;
   // do the computation
-  parameters = LevenbergMarquardt<MGH17_functor>::Parameters(); // get default back
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1407,9 +1393,8 @@ void testNistMGH09(void)
   // do the computation
   MGH09_functor functor;
   LevenbergMarquardt<MGH09_functor> lm(functor);
-  LevenbergMarquardt<MGH09_functor>::Parameters parameters;
-  parameters.maxfev = 1000;
-  info = lm.minimize(x, parameters);
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1428,8 +1413,8 @@ void testNistMGH09(void)
    */
   x<< 0.25, 0.39, 0.415, 0.39;
   // do the computation
-  parameters = LevenbergMarquardt<MGH09_functor>::Parameters();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1491,9 +1476,8 @@ void testNistBennett5(void)
   // do the computation
   Bennett5_functor functor;
   LevenbergMarquardt<Bennett5_functor> lm(functor);
-  LevenbergMarquardt<Bennett5_functor>::Parameters parameters;
-  parameters.maxfev = 1000;
-  info = lm.minimize(x, parameters);
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1510,8 +1494,8 @@ void testNistBennett5(void)
    */
   x<< -1500., 45., 0.85;
   // do the computation
-  parameters = LevenbergMarquardt<Bennett5_functor>::Parameters();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1579,10 +1563,9 @@ void testNistThurber(void)
   // do the computation
   thurber_functor functor;
   LevenbergMarquardt<thurber_functor> lm(functor);
-  LevenbergMarquardt<thurber_functor>::Parameters parameters;
-  parameters.ftol = 1.E4*epsilon<double>();
-  parameters.xtol = 1.E4*epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.parameters.ftol = 1.E4*epsilon<double>();
+  lm.parameters.xtol = 1.E4*epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1604,10 +1587,10 @@ void testNistThurber(void)
    */
   x<< 1300 ,1500 ,500  ,75   ,1    ,0.4  ,0.05  ;
   // do the computation
-  parameters = LevenbergMarquardt<thurber_functor>::Parameters();
-  parameters.ftol = 1.E4*epsilon<double>();
-  parameters.xtol = 1.E4*epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E4*epsilon<double>();
+  lm.parameters.xtol = 1.E4*epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1672,10 +1655,9 @@ void testNistRat43(void)
   // do the computation
   rat43_functor functor;
   LevenbergMarquardt<rat43_functor> lm(functor);
-  LevenbergMarquardt<rat43_functor>::Parameters parameters;
-  parameters.ftol = 1.E6*epsilon<double>();
-  parameters.xtol = 1.E6*epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.parameters.ftol = 1.E6*epsilon<double>();
+  lm.parameters.xtol = 1.E6*epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1694,10 +1676,10 @@ void testNistRat43(void)
    */
   x<< 700., 5., 0.75, 1.3;
   // do the computation
-  parameters = LevenbergMarquardt<rat43_functor>::Parameters(); // get default back
-  parameters.ftol = 1.E5*epsilon<double>();
-  parameters.xtol = 1.E5*epsilon<double>();
-  info = lm.minimize(x, parameters);
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E5*epsilon<double>();
+  lm.parameters.xtol = 1.E5*epsilon<double>();
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1760,8 +1742,7 @@ void testNistEckerle4(void)
   // do the computation
   eckerle4_functor functor;
   LevenbergMarquardt<eckerle4_functor> lm(functor);
-  LevenbergMarquardt<eckerle4_functor>::Parameters parameters;
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info); 
@@ -1779,7 +1760,7 @@ void testNistEckerle4(void)
    */
   x<< 1.5, 5., 450.;
   // do the computation
-  info = lm.minimize(x, parameters);
+  info = lm.minimize(x);
 
   // check return value
   VERIFY( 1 == info);