diff --git a/matlab/optimization/csminit1.m b/matlab/optimization/csminit1.m
index 98ed14ef09d136eea5e2cb36e1418e20823b96ea..e984cb5454e18bedc6fbb3cf16d1da702d72ce5a 100644
--- a/matlab/optimization/csminit1.m
+++ b/matlab/optimization/csminit1.m
@@ -38,7 +38,7 @@ function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verb
% http://sims.princeton.edu/yftp/optimize/mfiles/csminit.m
%
% Copyright © 1993-2007 Christopher Sims
-% Copyright © 2008-2017 Dynare Team
+% Copyright © 2008-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -55,24 +55,14 @@ function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verb
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-%tailstr = ')';
-%for i=nargin-6:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-%end
-%ANGLE = .03;
ANGLE = .005;
-%THETA = .03;
THETA = .3; %(0<THETA<.5) THETA near .5 makes long line searches, possibly fewer iterations.
FCHANGE = 1000;
MINLAMB = 1e-9;
-% fixed 7/15/94
-% MINDX = .0001;
-% MINDX = 1e-6;
MINDFAC = .01;
fcount=0;
lambda=1;
xhat=x0;
-f=f0;
fhat=f0;
g = g0;
gnorm = norm(g);
@@ -84,34 +74,13 @@ if (gnorm < 1.e-12) && ~badg % put ~badg 8/4/94
else
% with badg true, we don't try to match rate of improvement to directional
% derivative. We're satisfied just to get some improvement in f.
- %
- %if(badg)
- % dx = -g*FCHANGE/(gnorm*gnorm);
- % dxnorm = norm(dx);
- % if dxnorm > 1e12
- % disp('Bad, small gradient problem.')
- % dx = dx*FCHANGE/dxnorm;
- % end
- %else
- % Gauss-Newton step;
- %---------- Start of 7/19/93 mod ---------------
- %[v d] = eig(H0);
- %toc
- %d=max(1e-10,abs(diag(d)));
- %d=abs(diag(d));
- %dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
- % toc
dx = -H0*g;
- % toc
dxnorm = norm(dx);
if dxnorm > 1e12
disp_verbose('Near-singular H problem.',Verbose)
dx = dx*FCHANGE/dxnorm;
end
dfhat = dx'*g0;
- %end
- %
- %
if ~badg
% test for alignment of dx with gradient and fix if necessary
a = -dfhat/(gnorm*dxnorm);
@@ -123,7 +92,6 @@ else
end
end
disp_verbose(sprintf('Predicted improvement: %18.9f',-dfhat/2),Verbose)
- %
% Have OK dx, now adjust length of step (lambda) until min and
% max improvement rate criteria are met.
done=0;
@@ -132,7 +100,6 @@ else
lambdaMax=inf;
lambdaPeak=0;
fPeak=f0;
- lambdahat=0;
while ~done
if size(x0,2)>1
dxtest=x0+dx'*lambda;
@@ -141,16 +108,10 @@ else
end
% home
f = penalty_objective_function(dxtest,fcn,penalty,varargin{:});
- %ARGLIST
- %f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
- % f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
disp_verbose(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ),Verbose)
- %debug
- %disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
if f<fhat
fhat=f;
xhat=dxtest;
- lambdahat = lambda;
end
fcount=fcount+1;
shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
@@ -162,7 +123,6 @@ else
while lambda/factor <= lambdaPeak
factor=factor^.6;
end
- %if (abs(lambda)*(factor-1)*dxnorm < MINDX) || (abs(lambda)*(factor-1) < MINLAMB)
if abs(factor-1)<MINDFAC
if abs(lambda)<4
retcode=2;
@@ -197,7 +157,6 @@ else
if shrink
shrink=0;
factor = factor^.6;
- %if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) || ( abs(lambda)*(factor-1)< MINLAMB)
if abs(factor-1)<MINDFAC
if abs(lambda)<4
retcode=4;
diff --git a/matlab/optimization/csminwel1.m b/matlab/optimization/csminwel1.m
index 0ef28bad86ef97c5982a64d30dca33655177c953..e9f522b40569aaa47f8334abbf2e1cf0fca4fc18 100644
--- a/matlab/optimization/csminwel1.m
+++ b/matlab/optimization/csminwel1.m
@@ -4,9 +4,9 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% fcn: [string] string naming the objective function to be minimized
% x0: [npar by 1] initial value of the parameter vector
% H0: [npar by npar] initial value for the inverse Hessian. Must be positive definite.
-% grad: [string or empty matrix] 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.
+% grad: [string or boolean] Either a string naming a function that calculates the gradient, or a boolean
+% indicating whether the function returns a gradient (column) vector. If false, the program
+% calculates a numerical gradient.
% crit: [scalar] Convergence criterion. Iteration will cease when it proves impossible to improve the
% function value by more than crit.
% nit: [scalar] Maximum number of iterations.
@@ -42,7 +42,7 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
%
% Copyright © 1993-2007 Christopher Sims
-% Copyright © 2006-2021 Dynare Team
+% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%