diff --git a/matlab/csminwel.m b/matlab/csminwel1.m
similarity index 97%
rename from matlab/csminwel.m
rename to matlab/csminwel1.m
index 9a5d881e9..fbd96495e 100644
--- a/matlab/csminwel.m
+++ b/matlab/csminwel1.m
@@ -1,5 +1,5 @@
-function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
-%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
+function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
+%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
 % fcn:   string naming the objective function to be minimized
 % x0:    initial value of the parameter vector
 % H0:    initial value for the inverse Hessian.  Must be positive definite.
@@ -10,7 +10,7 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,me
 %        function value by more than crit.
 % nit:   Maximum number of iterations.
 % method: integer scalar, 2, 3 or 5 points formula.
-% penalty: scalar double, size of the penality.
+% epsilon: scalar double, numerical differentiation increment
 % varargin: A list of optional length of additional parameters that get handed off to fcn each
 %        time it is called.
 %        Note that if the program ends abnormally, it is possible to retrieve the current x,
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index c73ed8eb1..4f581ccbb 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -429,12 +429,12 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
         verbose = 2;
         if ~options_.dsge_var
             [fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
-                csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend,data,data_index,number_of_observations,no_more_missing_observations);
+                csminwel1('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend,data,data_index,number_of_observations,no_more_missing_observations);
             disp(sprintf('Objective function at mode: %f',fval))
             disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)))
         else
             [fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
-                csminwel('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend);
+                csminwel1('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend);
             disp(sprintf('Objective function at mode: %f',fval))
             disp(sprintf('Objective function at mode: %f',DsgeVarLikelihood(xparam1,gend)))
         end
diff --git a/matlab/maximize_prior_density.m b/matlab/maximize_prior_density.m
index 3ff454399..8cdde3eba 100644
--- a/matlab/maximize_prior_density.m
+++ b/matlab/maximize_prior_density.m
@@ -40,7 +40,7 @@ verbose = 2;
 gradient_method = 2;
 
 [lpd,xparams,grad,hessian,itct,fcount,retcodehat] = ...
-    csminwel('minus_logged_prior_density',iparams,H0,[],crit,nit,gradient_method, options_.gradient_epsilon, ... 
+    csminwel1('minus_logged_prior_density',iparams,H0,[],crit,nit,gradient_method, options_.gradient_epsilon, ... 
              prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound);
 
 lpd = -lpd;
diff --git a/matlab/osr1.m b/matlab/osr1.m
index 884f0d4e2..b79f94078 100644
--- a/matlab/osr1.m
+++ b/matlab/osr1.m
@@ -95,7 +95,7 @@ crit = 1e-7;
 nit = 1000;
 verbose = 2;
 
-[f,p]=csminwel('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,i_params,...
+[f,p]=csminwel1('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,i_params,...
                inv_order_var(i_var),weights(i_var,i_var));
 
 %  options = optimset('fminunc');
diff --git a/matlab/simulated_moments_estimation.m b/matlab/simulated_moments_estimation.m
index 76e810f0c..e328b5870 100644
--- a/matlab/simulated_moments_estimation.m
+++ b/matlab/simulated_moments_estimation.m
@@ -209,10 +209,10 @@ if options.optimization_routine==1
     % Minimization of the objective function.
     if nargin==2
         [fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
-            csminwel('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options);    
+            csminwel1('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options);    
     elseif nargin>2
         [fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
-            csminwel('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options,parallel);
+            csminwel1('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options,parallel);
     end
 elseif options.optimization_routine==2
     optim_options = optimset('display','iter','MaxFunEvals',1000000,'MaxIter',6000,'TolFun',1e-4,'TolX',1e-4);