/usr/share/dynare/matlab/csminwel1.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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%[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.
% grad: Either a string naming a function that calculates the gradient, or the null matrix.
% If it's null, the program calculates a numerical gradient. In this case fcn must
% be written so that it can take a matrix argument and produce a row vector of values.
% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
% function value by more than crit.
% nit: Maximum number of iterations.
% method: integer scalar, 2, 3 or 5 points formula.
% 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,
% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
% may be a decent starting point. One can also start from the one with best function value.)
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2006-2012 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 objective_function_penalty_base
fh = [];
xh = [];
[nx,no]=size(x0);
nx=max(nx,no);
Verbose=1;
NumGrad= isempty(grad);
done=0;
itct=0;
fcount=0;
snit=100;
gh = [];
H = [];
retcodeh = [];
%tailstr = ')';
%stailstr = [];
% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
% changed with the editor for each application. Places where this is required are marked
% with ARGLIST comments
%for i=nargin-6:-1:1
% tailstr=[ ',P' num2str(i) tailstr];
% stailstr=[' P' num2str(i) stailstr];
%end
[f0,junk1,junk2,cost_flag] = feval(fcn,x0,varargin{:});
if ~cost_flag
disp('Bad initial parameter.')
return
end
if NumGrad
switch method
case 2
[g,badg] = numgrad2(fcn, f0, x0, epsilon, varargin{:});
case 3
[g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
case 5
[g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
case 13
[g,badg] = numgrad3_(fcn, f0, x0, epsilon, varargin{:});
case 15
[g,badg] = numgrad5_(fcn, f0, x0, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
elseif ischar(grad)
[g,badg] = feval(grad,x0,varargin{:});
else
g=junk1;
badg=0;
end
retcode3=101;
x=x0;
f=f0;
H=H0;
cliff=0;
while ~done
% penalty for dsge_likelihood and dsge_var_likelihood
objective_function_penalty_base = f;
g1=[]; g2=[]; g3=[];
%addition fj. 7/6/94 for control
disp('-----------------')
disp('-----------------')
%disp('f and x at the beginning of new iteration')
disp(sprintf('f at the beginning of new iteration, %20.10f',f))
%-----------Comment out this line if the x vector is long----------------
% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
%-------------------------
itct=itct+1;
[f1 x1 fc retcode1] = csminit1(fcn,x,f,g,badg,H,varargin{:});
%ARGLIST
%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
% P8,P9,P10,P11,P12,P13);
% itct=itct+1;
fcount = fcount+fc;
% erased on 8/4/94
% if (retcode == 1) || (abs(f1-f) < crit)
% done=1;
% end
% if itct > nit
% done = 1;
% retcode = -retcode;
% end
if retcode1 ~= 1
if retcode1==2 || retcode1==4
wall1=1; badg1=1;
else
if NumGrad
switch method
case 2
[g1 badg1] = numgrad2(fcn, f1, x1, epsilon, varargin{:});
case 3
[g1 badg1] = numgrad3(fcn, f1, x1, epsilon, varargin{:});
case 5
[g1,badg1] = numgrad5(fcn, f1, x1, epsilon, varargin{:});
case 13
[g1,badg1] = numgrad3_(fcn, f1, x1, epsilon, varargin{:});
case 15
[g1,badg1] = numgrad5_(fcn, f1, x1, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
elseif ischar(grad),
[g1 badg1] = feval(grad,x1,varargin{:});
else
[junk1,g1,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg1 = ~cost_flag;
end
wall1=badg1;
% g1
save g1.mat g1 x1 f1 varargin;
%ARGLIST
%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
if wall1 % && (~done) by Jinill
% Bad gradient or back and forth on step length. Possibly at
% cliff edge. Try perturbing search direction.
%
%fcliff=fh;xcliff=xh;
Hcliff=H+diag(diag(H).*rand(nx,1));
disp('Cliff. Perturbing search direction.')
[f2 x2 fc retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,varargin{:});
%ARGLIST
%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
% P5,P6,P7,P8,P9,P10,P11,P12,P13);
fcount = fcount+fc; % put by Jinill
if f2 < f
if retcode2==2 || retcode2==4
wall2=1; badg2=1;
else
if NumGrad
switch method
case 2
[g2 badg2] = numgrad2(fcn, f2, x2, epsilon, varargin{:});
case 3
[g2 badg2] = numgrad3(fcn, f2, x2, epsilon, varargin{:});
case 5
[g2,badg2] = numgrad5(fcn, f2, x2, epsilon, varargin{:});
case 13
[g2,badg2] = numgrad3_(fcn, f2, x2, epsilon, varargin{:});
case 15
[g2,badg2] = numgrad5_(fcn, f2, x2, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
elseif ischar(grad),
[g2 badg2] = feval(grad,x2,varargin{:});
else
[junk1,g2,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg2 = ~cost_flag;
end
wall2=badg2;
% g2
badg2
save g2.mat g2 x2 f2 varargin
%ARGLIST
%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
if wall2
disp('Cliff again. Try traversing')
if norm(x2-x1) < 1e-13
f3=f; x3=x; badg3=1;retcode3=101;
else
gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
if(size(x0,2)>1), gcliff=gcliff', end
[f3 x3 fc retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),varargin{:});
%ARGLIST
%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
% P4,P5,P6,P7,P8,...
% P9,P10,P11,P12,P13);
fcount = fcount+fc; % put by Jinill
if retcode3==2 || retcode3==4
wall3=1; badg3=1;
else
if NumGrad
switch method
case 2
[g3 badg3] = numgrad2(fcn, f3, x3, epsilon, varargin{:});
case 3
[g3 badg3] = numgrad3(fcn, f3, x3, epsilon, varargin{:});
case 5
[g3,badg3] = numgrad5(fcn, f3, x3, epsilon, varargin{:});
case 13
[g3,badg3] = numgrad3_(fcn, f3, x3, epsilon, varargin{:});
case 15
[g3,badg3] = numgrad5_(fcn, f3, x3, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
elseif ischar(grad),
[g3 badg3] = feval(grad,x3,varargin{:});
else
[junk1,g3,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg3 = ~cost_flag;
end
wall3=badg3;
% g3
save g3.mat g3 x3 f3 varargin;
%ARGLIST
%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
end
else
f3=f; x3=x; badg3=1; retcode3=101;
end
else
f3=f; x3=x; badg3=1;retcode3=101;
end
else
% normal iteration, no walls, or else we're finished here.
f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
end
else
f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
end
%how to pick gh and xh
if f3 < f - crit && badg3==0 && f3 < f2 && f3 < f1
ih=3;
fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
elseif f2 < f - crit && badg2==0 && f2 < f1
ih=2;
fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
elseif f1 < f - crit && badg1==0
ih=1;
fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
else
[fh,ih] = min([f1,f2,f3]);
%disp(sprintf('ih = %d',ih))
%eval(['xh=x' num2str(ih) ';'])
switch ih
case 1
xh=x1;
case 2
xh=x2;
case 3
xh=x3;
end %case
%eval(['gh=g' num2str(ih) ';'])
%eval(['retcodeh=retcode' num2str(ih) ';'])
retcodei=[retcode1,retcode2,retcode3];
retcodeh=retcodei(ih);
if exist('gh')
nogh=isempty(gh);
else
nogh=1;
end
if nogh
if NumGrad
switch method
case 2
[gh,badgh] = numgrad2(fcn, fh, xh, epsilon, varargin{:});
case 3
[gh,badgh] = numgrad3(fcn, fh, xh, epsilon, varargin{:});
case 5
[gh,badgh] = numgrad5(fcn, fh, xh, epsilon, varargin{:});
case 13
[gh,badgh] = numgrad3_(fcn, fh, xh, epsilon, varargin{:});
case 15
[gh,badgh] = numgrad5_(fcn, fh, xh, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
elseif ischar(grad),
[gh badgh] = feval(grad, xh,varargin{:});
else
[junk1,gh,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badgh = ~cost_flag;
end
end
badgh=1;
end
%end of picking
%ih
%fh
%xh
%gh
%badgh
stuck = (abs(fh-f) < crit);
if (~badg) && (~badgh) && (~stuck)
H = bfgsi1(H,gh-g,xh-x);
end
if Verbose
disp('----')
disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
end
% if Verbose
if itct > nit
disp('iteration count termination')
done = 1;
elseif stuck
disp('improvement < crit termination')
done = 1;
end
rc=retcodeh;
if rc == 1
disp('zero gradient')
elseif rc == 6
disp('smallest step still improving too slow, reversed gradient')
elseif rc == 5
disp('largest step still improving too fast')
elseif (rc == 4) || (rc==2)
disp('back and forth on step length never finished')
elseif rc == 3
disp('smallest step still improving too slow')
elseif rc == 7
disp('warning: possible inaccuracy in H matrix')
end
% end
f=fh;
x=xh;
g=gh;
badg=badgh;
end
% what about making an m-file of 10 lines including numgrad.m
% since it appears three times in csminwel.m
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