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function [info,tmp] = homotopic_steps(endo_simul0,exo_simul0,initial_weight,step_length,pfm)
% Copyright (C) 2012 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 <http://www.gnu.org/licenses/>.
global options_ oo_
%Set bytecode flag
bytecode_flag = options_.ep.use_bytecode;
% Set increase and decrease factors.
increase_factor = 5.0;
decrease_factor = 0.2;
% Save current state of oo_.endo_simul and oo_.exo_simul.
endo_simul = endo_simul0;
exxo_simul = exo_simul0;
initial_step_length = step_length;
max_iter = 1000/step_length;
weight = initial_weight;
verbose = options_.ep.debug;
reduce_step_flag = 0;
if verbose
format long
end
% (re)Set iter.
iter = 0;
% (re)Set iter.
jter = 0;
% (re)Set weight.
weight = initial_weight;
% (re)Set exo_simul to zero.
exo_simul0 = zeros(size(exo_simul0));
while weight<1
iter = iter+1;
exo_simul0(2,:) = weight*exxo_simul(2,:);
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
end
info.convergence = ~flag;% Equal to one if the perfect foresight solver converged for the current value of weight.
if verbose
if info.convergence
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Ok!' ])
else
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Convergence problem!' ])
end
end
if info.convergence
%if d<stochastic_extended_path_depth
endo_simul0 = tmp;
%end
jter = jter + 1;
if jter>3
if verbose
disp('I am increasing the step length!')
end
step_length=step_length*increase_factor;
jter = 0;
end
if abs(1-weight)<options_.dynatol.x;
break
end
weight = weight+step_length;
else% Perfect foresight solver failed for the current value of weight.
if initial_weight>0 && abs(weight-initial_weight)<1e-12% First iteration, the initial weight is too high.
if verbose
disp('I am reducing the initial weight!')
end
initial_weight = initial_weight/2;
weight = initial_weight;
if weight<1e-12
endo_simul0 = endo_simul;
exo_simul0 = exxo_simul;
info.convergence = 0;
info.depth = d;
tmp = [];
return
end
continue
else% Initial weight is OK, but the perfect foresight solver failed on some subsequent iteration.
if verbose
disp('I am reducing the step length!')
end
jter = 0;
if weight>0
weight = weight-step_length;
end
step_length=step_length*decrease_factor;
weight = weight+step_length;
if step_length<options_.dynatol.x
break
end
continue
end
end
if iter>max_iter
info = NaN;
return
end
end
if weight<1
exo_simul0 = exxo_simul;
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
end
info.convergence = ~flag;
if info.convergence
endo_simul0 = tmp;
return
else
if step_length>options_.dynatol.x
endo_simul0 = endo_simul;
exo_simul0 = exxo_simul;
info.convergence = 0;
info.depth = d;
tmp = [];
return
else
error('extended_path::homotopy: Oups! I did my best, but I am not able to simulate this model...')
end
end
end
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