diff --git a/matlab/mcp_func.m b/matlab/mcp_func.m
index 486764857..424b9203f 100644
--- a/matlab/mcp_func.m
+++ b/matlab/mcp_func.m
@@ -1,4 +1,33 @@
function [res,fjac,domer] = mcp_func(x,jacflag)
+% function [res,fjac,domer] = mcp_func(x,jacflag)
+% wrapper function for mixed complementarity problem when using PATH
+%
+% INPUTS
+% - x [double] N*T array, paths for the endogenous variables (initial guess).
+% - jacflag [scalar] indicator whether Jacobian is requested
+%
+% OUTPUTS
+% - res [double] (N*T)*1 array, residuals of the stacked problem
+% - fjac [double] (N*T)*(N*T) array, Jacobian of the stacked problem
+% - domer [scalar] errorflag that is 1 if solution is not real
+
+% Copyright (C) 2016 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see .
+
global mcp_data
if jacflag
diff --git a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
index 18fcff1c3..d4b419308 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
@@ -3,23 +3,49 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ,eq_index)
-% function [residuals,JJacobian] = perfect_foresight_mcp_problem(x, model_dynamic, Y0, YT,exo_simul,
-% params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1,
-% i_cols_T, i_cols_j, nnzA)
-% computes the residuals and th Jacobian matrix
-% for a perfect foresight problem over T periods.
+% 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)
+% Computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods
+% in a mixed complementarity problem context
%
% INPUTS
-% ...
+% y [double] N*1 array, terminal conditions for the endogenous variables
+% dynamic_function [handle] function handle to _dynamic-file
+% Y0 [double] N*1 array, initial conditions for the endogenous variables
+% YT [double] N*1 array, terminal conditions for the endogenous variables
+% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
+% for all simulation periods
+% params [double] nparams*1 array, parameter values
+% steady_state [double] endo_nbr*1 vector of steady state values
+% maximum_lag [scalar] maximum lag present in the model
+% T [scalar] number of simulation periods
+% ny [scalar] number of endogenous variables
+% i_cols [double] indices of variables appearing in M.lead_lag_incidence
+% and that need to be passed to _dynamic-file
+% i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% appearing in M.lead_lag_incidence
+% i_cols_1 [double] indices of contemporaneous and forward looking variables in
+% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
+% i_cols_T [double] columns of dynamic Jacobian related to contemporaneous and backward-looking
+% 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
-% ...
+% residuals [double] (N*T)*1 array, residuals of the stacked problem
+% JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
% ALGORITHM
-% ...
+% None
%
% SPECIAL REQUIREMENTS
% None.
-% Copyright (C) 1996-2015 Dynare Team
+% Copyright (C) 1996-2016 Dynare Team
%
% This file is part of Dynare.
%
@@ -37,46 +63,46 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% along with Dynare. If not, see .
- YY = [Y0; y; YT];
-
- residuals = zeros(T*ny,1);
- if nargout == 2
- iJacobian = cell(T,1);
- end
+YY = [Y0; y; YT];
- i_rows = 1:ny;
- offset = 0;
- i_cols_J = i_cols;
+residuals = zeros(T*ny,1);
+if nargout == 2
+ iJacobian = cell(T,1);
+end
- for it = 2:(T+1)
- if nargout == 1
- res = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- residuals(i_rows) = res(eq_index);
- elseif nargout == 2
- [res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- residuals(i_rows) = res(eq_index);
- if it == 2
- [rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
- iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
- elseif it == T + 1
- [rows,cols,vals] = find(jacobian(eq_index,i_cols_T));
- iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
- else
- [rows,cols,vals] = find(jacobian(eq_index,i_cols_j));
- iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
- i_cols_J = i_cols_J + ny;
- end
- offset = offset + ny;
+i_rows = 1:ny;
+offset = 0;
+i_cols_J = i_cols;
+
+for it = 2:(T+1)
+ if nargout == 1
+ res = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ residuals(i_rows) = res(eq_index);
+ elseif nargout == 2
+ [res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ residuals(i_rows) = res(eq_index);
+ if it == 2
+ [rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
+ iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
+ elseif it == T + 1
+ [rows,cols,vals] = find(jacobian(eq_index,i_cols_T));
+ iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
+ else
+ [rows,cols,vals] = find(jacobian(eq_index,i_cols_j));
+ iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
+ i_cols_J = i_cols_J + ny;
end
-
- i_rows = i_rows + ny;
- i_cols = i_cols + ny;
+ offset = offset + ny;
end
+
+ i_rows = i_rows + ny;
+ i_cols = i_cols + ny;
+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
+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
diff --git a/matlab/perfect-foresight-models/perfect_foresight_problem.m b/matlab/perfect-foresight-models/perfect_foresight_problem.m
index adce18df5..956119094 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_problem.m
@@ -3,23 +3,46 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ)
-% function [residuals,JJacobian] = perfect_foresight_problem(x, model_dynamic, Y0, YT,exo_simul,
-% params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1,
-% i_cols_T, i_cols_j, nnzA)
-% computes the residuals and th Jacobian matrix
-% for a perfect foresight problem over T periods.
+% function [residuals,JJacobian] = perfect_foresight_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)
+% computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods.
%
% INPUTS
-% ...
+% y [double] N*1 array, terminal conditions for the endogenous variables
+% dynamic_function [handle] function handle to _dynamic-file
+% Y0 [double] N*1 array, initial conditions for the endogenous variables
+% YT [double] N*1 array, terminal conditions for the endogenous variables
+% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
+% for all simulation periods
+% params [double] nparams*1 array, parameter values
+% steady_state [double] endo_nbr*1 vector of steady state values
+% maximum_lag [scalar] maximum lag present in the model
+% T [scalar] number of simulation periods
+% ny [scalar] number of endogenous variables
+% i_cols [double] indices of variables appearing in M.lead_lag_incidence
+% and that need to be passed to _dynamic-file
+% i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% appearing in M.lead_lag_incidence
+% i_cols_1 [double] indices of contemporaneous and forward looking variables in
+% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
+% i_cols_T [double] columns of dynamic Jacobian related to contemporaneous and backward-looking
+% 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
% OUTPUTS
-% ...
+% residuals [double] (N*T)*1 array, residuals of the stacked problem
+% JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
% ALGORITHM
-% ...
+% None
%
% SPECIAL REQUIREMENTS
% None.
-% Copyright (C) 1996-2015 Dynare Team
+% Copyright (C) 1996-2016 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
index d0b403c2b..da267b7da 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -1,5 +1,15 @@
function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
%function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
+% Core function calling solvers for perfect foresight model
+%
+% INPUTS
+% - M_ [struct] contains a description of the model.
+% - options_ [struct] contains various options.
+% - oo_ [struct] contains results
+%
+% OUTPUTS
+% - oo_ [struct] contains results
+% - maxerror [double] contains the maximum absolute error
% Copyright (C) 2015-2016 Dynare Team
%
diff --git a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
index b56fec828..64beba34e 100644
--- a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
+++ b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
@@ -1,7 +1,33 @@
function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, ...
- dynamicmodel] = initialize_stack_solve_algo_7(endogenousvariables, options, M, steadystate_y)
+ dynamicmodel] = initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
+% function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, ...
+% dynamicmodel] = initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
+% Sets up the stacked perfect foresight problem for use with dynare_solve.m
+%
+% INPUTS
+% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
+% - options [struct] contains various options.
+% - M [struct] contains a description of the model.
+% - steadystate_y [double] N*1 array, steady state for the endogenous variables.
+% OUTPUTS
+% - options [struct] contains various options.
+% - y0 [double] N*1 array, initial conditions for the endogenous variables
+% - yT [double] N*1 array, terminal conditions for the endogenous variables
+% - z [double] T*M array, paths for the exogenous variables.
+% - i_cols [double] indices of variables appearing in M.lead_lag_incidence
+% and that need to be passed to _dynamic-file
+% - i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% appearing in M.lead_lag_incidence
+% - i_cols_T [double] columns of dynamic Jacobian related to
+% contemporaneous and backward-looking
+% variables (relevant in last period)
+% - i_cols_j [double] indices of variables in M.lead_lag_incidence
+% in dynamic Jacobian (relevant in intermediate periods)
+% - i_cols_1 [double] indices of contemporaneous and forward looking variables in
+% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
+% - dynamicmodel [handle] function handle to _dynamic-file
-% Copyright (C) 2015 Dynare Team
+% Copyright (C) 2015-16 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index 55c17bb70..05ef5b186 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -3,9 +3,14 @@ function [endogenousvariables, info] = sim1(endogenousvariables, exogenousvariab
% Performs deterministic simulations with lead or lag on one period. Uses sparse matrices.
%
% INPUTS
-% ...
+% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
+% - exogenousvariables [double] T*M array, paths for the exogenous variables.
+% - steadystate [double] N*1 array, steady state for the endogenous variables.
+% - M [struct] contains a description of the model.
+% - options [struct] contains various options.
% OUTPUTS
-% ...
+% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
+% - info [struct] contains informations about the results.
% ALGORITHM
% ...
%
diff --git a/matlab/perfect-foresight-models/solve_stacked_problem.m b/matlab/perfect-foresight-models/solve_stacked_problem.m
index 7b1ab780e..7a1835ba6 100644
--- a/matlab/perfect-foresight-models/solve_stacked_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_problem.m
@@ -1,6 +1,19 @@
function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options);
+% [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options);
+% Solves the perfect foresight model using dynare_solve
+%
+% INPUTS
+% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
+% - exogenousvariables [double] T*M array, paths for the exogenous variables.
+% - steadystate [double] N*1 array, steady state for the endogenous variables.
+% - M [struct] contains a description of the model.
+% - options [struct] contains various options.
+%
+% OUTPUTS
+% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
+% - info [struct] contains informations about the results.
-% Copyright (C) 2015 Dynare Team
+% Copyright (C) 2015-16 Dynare Team
%
% This file is part of Dynare.
%
@@ -20,7 +33,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate);
-if (options.solve_algo == 10 || options.solve_algo == 11)
+if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity problem
[lb,ub,eq_index] = get_complementarity_conditions(M,options.ramsey_policy);
if options.linear_approximation
lb = lb - steadystate_y;