Perfect foresight: remove unused nnzJ argument in several stacked problem functions

matlab/perfect-foresight-models/linear_perfect_foresight_problem.m

 function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
                                                   exo_simul, params, steady_state, maximum_lag, T, ny, i_cols, ...
-                                                  i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, nnzJ, jendo, jexog)
+                                                  i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, jendo, jexog)
 
 % Computes the residuals and the Jacobian matrix for a linear perfect foresight problem over T periods.
 %
@@ -76,4 +76,4 @@ end
 if nargout == 2
     iJacobian = cat(1,iJacobian{:});
     JJacobian = sparse(iJacobian(:,1), iJacobian(:,2), iJacobian(:,3), T*ny, T*ny);
-end
\ No newline at end of file
+end

matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m

@@ -2,12 +2,12 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
                                                   exo_simul, params, steady_state, ...
                                                   maximum_lag, T, ny, i_cols, ...
                                                   i_cols_J1, i_cols_1, i_cols_T, ...
-                                                  i_cols_j, i_cols_0,i_cols_J0, nnzJ,eq_index)
+                                                  i_cols_j, i_cols_0,i_cols_J0, eq_index)
 % function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
 %                                            exo_simul, params, steady_state, ...
 %                                            maximum_lag, T, ny, i_cols, ...
 %                                            i_cols_J1, i_cols_1, i_cols_T, ...
-%                                            i_cols_j,nnzJ,eq_index)
+%                                            i_cols_j,eq_index)
 % Computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods
 % in a mixed complementarity problem context
 %
@@ -33,7 +33,6 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
 %                                variables (relevant in last period)
 %   i_cols_j            [double] indices of variables in M.lead_lag_incidence
 %                                in dynamic Jacobian (relevant in intermediate periods)
-%   nnzJ                [scalar] number of non-zero elements in Jacobian
 %   eq_index            [double] N*1 array, index vector describing residual mapping resulting
 %                                from complementarity setup
 % OUTPUTS
@@ -45,7 +44,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
 % SPECIAL REQUIREMENTS
 %   None.
 
-% Copyright (C) 1996-2017 Dynare Team
+% Copyright (C) 1996-2019 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -106,4 +105,4 @@ end
 if nargout == 2
     iJacobian = cat(1,iJacobian{:});
     JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T*ny,T*ny);
-end
\ No newline at end of file
+end

matlab/perfect-foresight-models/perfect_foresight_problem.m

@@ -2,7 +2,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
                                                   exo_simul, params, steady_state, ...
                                                   maximum_lag, T, ny, i_cols, ...
                                                   i_cols_J1, i_cols_1, i_cols_T, ...
-                                                  i_cols_j, i_cols_0, i_cols_J0, nnzJ)
+                                                  i_cols_j, i_cols_0, i_cols_J0)
 
 % Computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods.
 %
@@ -31,7 +31,6 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
 % - i_cols_0            [double] indices of contemporaneous variables in M.lead_lag_incidence in dynamic
 %                                Jacobian (relevant in problems with periods=1)
 % - i_cols_J0           [double] indices of contemporaneous variables appearing in M.lead_lag_incidence (relevant in problems with periods=1)
-% - nnzJ                [scalar] number of non-zero elements in Jacobian
 %
 % OUTPUTS
 % - residuals           [double] (N*T)*1 array, residuals of the stacked problem
@@ -100,4 +99,4 @@ end
 if nargout == 2
     iJacobian = cat(1,iJacobian{:});
     JJacobian = sparse(iJacobian(:,1), iJacobian(:,2), iJacobian(:,3), T*ny, T*ny);
-end
\ No newline at end of file
+end

matlab/perfect-foresight-models/perfect_foresight_solver.m

@@ -12,7 +12,7 @@ function perfect_foresight_solver()
 % SPECIAL REQUIREMENTS
 %   none
 
-% Copyright (C) 1996-2018 Dynare Team
+% Copyright (C) 1996-2019 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -195,8 +195,7 @@ if ~isreal(oo_.endo_simul(:)) % can only happen without bytecode
     residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
                                           oo_.exo_simul,M_.params,oo_.steady_state, ...
                                           M_.maximum_lag, periods, M_.endo_nbr, i_cols, ...
-                                          i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
-                                          M_.NNZDerivatives(1));
+                                          i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0);
     if max(abs(residuals))< options_.dynatol.f
         oo_.deterministic_simulation.status = 1;
         oo_.endo_simul=real(oo_.endo_simul);
@@ -223,4 +222,4 @@ else
 end
 
 ts = dseries(transpose(oo_.endo_simul), initial_period, M_.endo_names);
-assignin('base', 'Simulated_time_series', ts);
\ No newline at end of file
+assignin('base', 'Simulated_time_series', ts);

matlab/perfect-foresight-models/perfect_foresight_solver_core.m

@@ -144,8 +144,7 @@ if nargout>1
             residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
                                                   oo_.exo_simul,M_.params,oo_.steady_state, ...
                                                   M_.maximum_lag, periods,M_.endo_nbr,i_cols, ...
-                                                  i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
-                                                  M_.NNZDerivatives(1));
+                                                  i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0);
         end
         maxerror = max(max(abs(residuals)));
     end

matlab/perfect-foresight-models/solve_stacked_linear_problem.m

 function [endogenousvariables, info] = solve_stacked_linear_problem(endogenousvariables, exogenousvariables, steadystate_y, steadystate_x, M, options)
 
-% Copyright (C) 2015-2017 Dynare Team
+% Copyright (C) 2015-2019 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -46,7 +46,7 @@ x = bsxfun(@minus, exogenousvariables, steadystate_x');
                           x, M.params, steadystate_y, ...
                           M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
                           i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
-                          M.NNZDerivatives(1), jendo, jexog);
+                          jendo, jexog);
 
 if all(imag(y)<.1*options.dynatol.x)
     if ~isreal(y)
@@ -62,4 +62,4 @@ if check
     info.status = false;
 else
     info.status = true;
-end
\ No newline at end of file
+end

matlab/perfect-foresight-models/solve_stacked_problem.m

@@ -51,14 +51,13 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
                               exogenousvariables, M.params, steadystate, ...
                               M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
                               i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
-                              M.NNZDerivatives(1),eq_index);
+                              eq_index);
 else
     [y, check] = dynare_solve(@perfect_foresight_problem,z(:),options, ...
                               dynamicmodel, y0, yT, ...
                               exogenousvariables, M.params, steadystate, ...
                               M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
-                              i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
-                              M.NNZDerivatives(1));
+                              i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0);
 end
 
 if all(imag(y)<.1*options.dynatol.x)
@@ -75,4 +74,4 @@ if check
     info.status = false;
 else
     info.status = true;
-end
\ No newline at end of file
+end