diff --git a/doc/dynare.texi b/doc/dynare.texi
index 993c3b2c4b8932d43621b3bd00f3c5d24a4f2b87..c7ff4cf2d4ed0258955a0061c73a56ced0e79c1c 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -4944,6 +4944,7 @@ Metropolis-Hastings simulations instead of starting from
 scratch. Shouldn't be used together with @code{mh_recover}
 
 @item optim = (@var{NAME}, @var{VALUE}, ...)
+@anchor{optim}
 A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the optimization routines. The set of available options depends on the selected optimization routine (ie on the value of option @ref{mode_compute}):
 
 @table @code
@@ -6498,9 +6499,8 @@ is specified in the @code{optim_weights}-block. By attaching weights to
 endogenous variables, the subset of endogenous variables entering the
 objective function, @math{y}, is implicitly specified.
 
-The linear quadratic problem is solved using the numerical optimizer
-@code{csminwel} of Chris Sims.
 
+The linear quadratic problem is solved using the numerical optimizer specified with @ref{opt_algo}.
 
 @optionshead
 
@@ -6511,12 +6511,12 @@ by listing them after the command, as @code{stoch_simul}
 
 @table @code
 
-@item maxit = @var{INTEGER} Determines the maximum number of iterations
-used in the non-linear solver. Default: @code{1000}
+@item opt_algo = @var{INTEGER}
+@anchor{opt_algo}
+Specifies the optimizer for minimizing the objective function. The same solvers as for @code{mode_compute} (@pxref{mode_compute}) are available, except for 5,6, and 10. 
 
-@item tolf = @var{DOUBLE} Convergence criterion for termination based on
-the function value. Iteration will cease when it proves impossible to
-improve the function value by more than tolf. Default: @code{1e-7}
+@item optim = (@var{NAME}, @var{VALUE}, ...)
+A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the optimization routines. The set of available options depends on the selected optimization routine (i.e. on the value of option @ref{opt_algo}). @xref{optim}.
 
 @end table
 
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index dec79b27b53c966047910f36744bdbe335c3fc71..36a0ccf9fd04ecc9f159cd56c03bbd29bbca18b4 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -639,8 +639,7 @@ options_.endogenous_prior_restrictions.irf={};
 options_.endogenous_prior_restrictions.moment={};
 
 % OSR Optimal Simple Rules
-options_.osr.tolf=1e-7;
-options_.osr.maxit=1000;
+options_.osr.opt_algo=4;
 
 % use GPU
 options_.gpu = 0;
diff --git a/matlab/osr1.m b/matlab/osr1.m
index eb23312ffe499556b3bcd583a7014db3e1a6a8a2..a96c618fb38fb24d7bf30ce52e02d3c1735b5227 100644
--- a/matlab/osr1.m
+++ b/matlab/osr1.m
@@ -73,9 +73,6 @@ t0 = M_.params(i_params);
 
 inv_order_var = oo_.dr.inv_order_var;
 
-H0 = 1e-4*eye(np);
-crit=options_.osr.tolf;
-nit=options_.osr.maxit;
 
 %extract unique entries of covariance
 i_var=unique(i_var);
@@ -93,20 +90,25 @@ if isinf(loss)
    error('OSR: Initial likelihood is infinite')
 end
 
+
+if isequal(options_.osr.opt_algo,5)
+    error('OSR: OSR does not support opt_algo=5.')
+elseif isequal(options_.osr.opt_algo,6)
+    error('OSR: OSR does not support opt_algo=6.')
+elseif isequal(options_.osr.opt_algo,10)
+    error('OSR: OSR does not support opt_algo=10.')    
+else
 %%do actual optimization
-[f,p]=csminwel1('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,i_params,...
+[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,[],cellstr(M_.param_names(i_params,:)),[],[], i_params,...
                 inv_order_var(i_var),weights(i_var,i_var));
+end
+
 osr_res.objective_function = f;
 M_.params(i_params)=p; %make sure optimal parameters are set (and not the last draw used in csminwel)
 for i=1:length(i_params)
     osr_res.optim_params.(deblank(M_.param_names(i_params(i),:))) = p(i);
 end
 
-%  options = optimset('fminunc');
-%  options = optimset('display','iter');
-%  [p,f]=fminunc(@osr_obj,t0,options,i_params,...
-%               inv_order_var(i_var),weights(i_var,i_var));
-
 skipline()
 disp('OPTIMAL VALUE OF THE PARAMETERS:')
 skipline()
diff --git a/tests/Makefile.am b/tests/Makefile.am
index 2004024367a943e1922a8cf3c6ea334c1af7a82c..734945b24c640cfb74017c712c23654c064b2b74 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -21,6 +21,12 @@ MODFILES = \
 	optimal_policy/osr_example.mod \
 	optimal_policy/osr_example_objective_correctness.mod \
 	optimal_policy/osr_example_obj_corr_non_stat_vars.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_1.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_3.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_4.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_7.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_8.mod \
+	optimal_policy/OSR/osr_obj_corr_algo_9.mod \
 	optimal_policy/ramsey_.mod \
 	optimal_policy/nk_ramsey.mod \
 	optimal_policy/nk_ramsey_model.mod \
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_1.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_1.mod
new file mode 100644
index 0000000000000000000000000000000000000000..65dcf699197b6a25fa4f90fb5f47bb1ad006ed63
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_1.mod
@@ -0,0 +1,109 @@
+// Example of optimal simple rule using opt_algo=4
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=1);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_3.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_3.mod
new file mode 100644
index 0000000000000000000000000000000000000000..a54224fc82b68e164886cf358565199a2aff6d14
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_3.mod
@@ -0,0 +1,109 @@
+// Example of optimal simple rule using opt_algo=4
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=3);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_4.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_4.mod
new file mode 100644
index 0000000000000000000000000000000000000000..cbbabab6b1d37eae2a4bedc840a9701b19a1929c
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_4.mod
@@ -0,0 +1,110 @@
+// Example of optimal simple rule using opt_algo=4
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+options_.osr.tolf=1e-20;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=4);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_7.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_7.mod
new file mode 100644
index 0000000000000000000000000000000000000000..fea4424572182b35a18db473ea8a46c36452719c
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_7.mod
@@ -0,0 +1,109 @@
+// Example of optimal simple rule using opt_algo=2
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=7);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_8.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_8.mod
new file mode 100644
index 0000000000000000000000000000000000000000..77d014d2f1e85f8eef3d55f4857b27b3808df1ff
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_8.mod
@@ -0,0 +1,109 @@
+// Example of optimal simple rule using opt_algo=2
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=8);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end
diff --git a/tests/optimal_policy/OSR/osr_obj_corr_algo_9.mod b/tests/optimal_policy/OSR/osr_obj_corr_algo_9.mod
new file mode 100644
index 0000000000000000000000000000000000000000..59a2fa250f62137bbd77a12b7920b3dd81c0d3c8
--- /dev/null
+++ b/tests/optimal_policy/OSR/osr_obj_corr_algo_9.mod
@@ -0,0 +1,109 @@
+// Example of optimal simple rule using opt_algo=3
+var y inflation r dummy_var;
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+
+model(linear);
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_; 
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+dummy_var=0.9*dummy_var(-1)+0.01*y;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_;
+end;
+
+shocks;
+var y_;
+stderr 0.63;
+var inf_;
+stderr 0.4;
+end;
+
+options_.nograph=1;
+options_.nocorr=1;
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_;
+
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+end;
+
+
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+osr(opt_algo=9);
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'));
+        
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+%redo computation with covariance specified
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+
+gammax0=1.35533;
+gammac0=1.39664;
+gamma_y_=16.6667;
+gamma_inf_=9.13199;
+        
+%redo computation with double weight on one covariance 
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 1;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+1*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+oo_covar_single=oo_;
+
+%redo computation with single weight on both covariances
+
+optim_weights;
+inflation 1;
+y 1;
+dummy_var 1;
+y,inflation 0.5;
+inflation,y 0.5;
+end;
+
+osr;
+%compute objective function manually
+objective=oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'))+oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+oo_.var(strmatch('dummy_var',M_.endo_names,'exact'),strmatch('dummy_var',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('y',M_.endo_names,'exact'),strmatch('inflation',M_.endo_names,'exact'))+0.5*oo_.var(strmatch('inflation',M_.endo_names,'exact'),strmatch('y',M_.endo_names,'exact'));
+if abs(oo_.osr.objective_function-objective)>1e-8
+    error('Objective Function is wrong')
+end
+if abs(oo_.osr.objective_function-oo_covar_single.osr.objective_function)>1e-8
+    error('Objective Function is wrong')
+end
+if max(abs((cell2mat(struct2cell(oo_.osr.optim_params))-cell2mat(struct2cell(oo_covar_single.osr.optim_params)))./cell2mat(struct2cell(oo_.osr.optim_params))))>1e-4
+    error('Parameters should be identical')
+end