395 lines
16 KiB
Matlab
395 lines
16 KiB
Matlab
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
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%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
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% fcn: string naming the objective function to be minimized
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% x0: initial value of the parameter vector
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% H0: initial value for the inverse Hessian. Must be positive definite.
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% grad: Either a string naming a function that calculates the gradient, or the null matrix.
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% If it's null, the program calculates a numerical gradient. In this case fcn must
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% be written so that it can take a matrix argument and produce a row vector of values.
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% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
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% function value by more than crit.
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% nit: Maximum number of iterations.
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% method: integer scalar, 2, 3 or 5 points formula.
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% epsilon: scalar double, numerical differentiation increment
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% varargin: A list of optional length of additional parameters that get handed off to fcn each
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% time it is called.
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% Note that if the program ends abnormally, it is possible to retrieve the current x,
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% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
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% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
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% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
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% may be a decent starting point. One can also start from the one with best function value.)
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% Original file downloaded from:
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% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
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% Copyright (C) 1993-2007 Christopher Sims
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% Copyright (C) 2006-2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global objective_function_penalty_base
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fh = [];
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xh = [];
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[nx,no]=size(x0);
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nx=max(nx,no);
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Verbose=1;
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NumGrad= isempty(grad);
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done=0;
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itct=0;
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fcount=0;
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snit=100;
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gh = [];
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H = [];
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retcodeh = [];
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%tailstr = ')';
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%stailstr = [];
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% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
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% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
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% changed with the editor for each application. Places where this is required are marked
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% with ARGLIST comments
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%for i=nargin-6:-1:1
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% tailstr=[ ',P' num2str(i) tailstr];
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% stailstr=[' P' num2str(i) stailstr];
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%end
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[f0,junk1,junk2,cost_flag] = feval(fcn,x0,varargin{:});
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if ~cost_flag
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disp('Bad initial parameter.')
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return
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end
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if NumGrad
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switch method
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case 2
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[g,badg] = numgrad2(fcn, f0, x0, epsilon, varargin{:});
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case 3
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[g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
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case 5
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[g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
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case {12, 22}
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[g,badg] = numgrad2_(fcn, f0, x0, epsilon, [], varargin{:});
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case {13, 23}
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[g,badg] = numgrad3_(fcn, f0, x0, epsilon, [], varargin{:});
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case {15, 25}
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[g,badg] = numgrad5_(fcn, f0, x0, epsilon, [], varargin{:});
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otherwise
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error('csminwel1: Unknown method for gradient evaluation!')
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end
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elseif ischar(grad)
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[g,badg] = feval(grad,x0,varargin{:});
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else
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g=junk1;
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badg=0;
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end
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retcode3=101;
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x=x0;
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f=f0;
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H=H0;
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cliff=0;
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while ~done
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% penalty for dsge_likelihood and DsgeVarLikelihood
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objective_function_penalty_base = f;
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g1=[]; g2=[]; g3=[];
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%addition fj. 7/6/94 for control
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disp('-----------------')
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disp('-----------------')
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%disp('f and x at the beginning of new iteration')
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disp(sprintf('f at the beginning of new iteration, %20.10f',f))
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%-----------Comment out this line if the x vector is long----------------
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% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
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%-------------------------
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itct=itct+1;
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[f1 x1 fc retcode1] = csminit1(fcn,x,f,g,badg,H,varargin{:});
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%ARGLIST
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%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
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% P8,P9,P10,P11,P12,P13);
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% itct=itct+1;
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fcount = fcount+fc;
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% erased on 8/4/94
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% if (retcode == 1) || (abs(f1-f) < crit)
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% done=1;
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% end
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% if itct > nit
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% done = 1;
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% retcode = -retcode;
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% end
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if retcode1 ~= 1
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if retcode1==2 || retcode1==4
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wall1=1; badg1=1;
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else
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if NumGrad
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switch method
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case 2
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[g1 badg1] = numgrad2(fcn, f1, x1, epsilon, varargin{:});
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case 3
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[g1 badg1] = numgrad3(fcn, f1, x1, epsilon, varargin{:});
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case 5
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[g1,badg1] = numgrad5(fcn, f1, x1, epsilon, varargin{:});
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case 12
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[g1,badg1] = numgrad2_(fcn, f1, x1, epsilon, [], varargin{:});
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case 13
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[g1,badg1] = numgrad3_(fcn, f1, x1, epsilon, [], varargin{:});
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case 15
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[g1,badg1] = numgrad5_(fcn, f1, x1, epsilon, [], varargin{:});
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case 22
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[g1,badg1] = numgrad2_(fcn, f1, x1, epsilon, abs(diag(H)), varargin{:});
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case 23
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[g1,badg1] = numgrad3_(fcn, f1, x1, epsilon, abs(diag(H)), varargin{:});
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case 25
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[g1,badg1] = numgrad5_(fcn, f1, x1, epsilon, abs(diag(H)), varargin{:});
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otherwise
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error('csminwel1: Unknown method for gradient evaluation!')
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end
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elseif ischar(grad),
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[g1 badg1] = feval(grad,x1,varargin{:});
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else
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[junk1,g1,junk2, cost_flag] = feval(fcn,x1,varargin{:});
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badg1 = ~cost_flag;
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end
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wall1=badg1;
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% g1
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save g1.mat g1 x1 f1 varargin;
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%ARGLIST
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%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
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end
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if wall1 % && (~done) by Jinill
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% Bad gradient or back and forth on step length. Possibly at
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% cliff edge. Try perturbing search direction.
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%
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%fcliff=fh;xcliff=xh;
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Hcliff=H+diag(diag(H).*rand(nx,1));
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disp('Cliff. Perturbing search direction.')
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[f2 x2 fc retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,varargin{:});
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%ARGLIST
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%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
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% P5,P6,P7,P8,P9,P10,P11,P12,P13);
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fcount = fcount+fc; % put by Jinill
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if f2 < f
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if retcode2==2 || retcode2==4
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wall2=1; badg2=1;
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else
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if NumGrad
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switch method
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case 2
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[g2 badg2] = numgrad2(fcn, f2, x2, epsilon, varargin{:});
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case 3
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[g2 badg2] = numgrad3(fcn, f2, x2, epsilon, varargin{:});
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case 5
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[g2,badg2] = numgrad5(fcn, f2, x2, epsilon, varargin{:});
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case 12
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[g2,badg2] = numgrad2_(fcn, f2, x2, epsilon, [], varargin{:});
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case 13
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[g2,badg2] = numgrad3_(fcn, f2, x2, epsilon, [], varargin{:});
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case 15
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[g2,badg2] = numgrad5_(fcn, f2, x2, epsilon, [], varargin{:});
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case 22
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[g2,badg2] = numgrad2_(fcn, f2, x2, epsilon, abs(diag(H)), varargin{:});
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case 23
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[g2,badg2] = numgrad3_(fcn, f2, x2, epsilon, abs(diag(H)), varargin{:});
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case 25
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[g2,badg2] = numgrad5_(fcn, f2, x2, epsilon, abs(diag(H)), varargin{:});
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otherwise
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error('csminwel1: Unknown method for gradient evaluation!')
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end
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elseif ischar(grad),
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[g2 badg2] = feval(grad,x2,varargin{:});
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else
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[junk1,g2,junk2, cost_flag] = feval(fcn,x1,varargin{:});
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badg2 = ~cost_flag;
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end
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wall2=badg2;
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% g2
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badg2
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save g2.mat g2 x2 f2 varargin
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%ARGLIST
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%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
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end
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if wall2
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disp('Cliff again. Try traversing')
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if norm(x2-x1) < 1e-13
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f3=f; x3=x; badg3=1;retcode3=101;
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else
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gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
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if(size(x0,2)>1), gcliff=gcliff', end
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[f3 x3 fc retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),varargin{:});
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%ARGLIST
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%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
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% P4,P5,P6,P7,P8,...
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% P9,P10,P11,P12,P13);
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fcount = fcount+fc; % put by Jinill
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if retcode3==2 || retcode3==4
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wall3=1; badg3=1;
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else
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if NumGrad
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switch method
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case 2
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[g3 badg3] = numgrad2(fcn, f3, x3, epsilon, varargin{:});
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case 3
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[g3 badg3] = numgrad3(fcn, f3, x3, epsilon, varargin{:});
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case 5
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[g3,badg3] = numgrad5(fcn, f3, x3, epsilon, varargin{:});
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case 12
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[g3,badg3] = numgrad2_(fcn, f3, x3, epsilon, [], varargin{:});
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case 13
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[g3,badg3] = numgrad3_(fcn, f3, x3, epsilon, [], varargin{:});
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case 15
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[g3,badg3] = numgrad5_(fcn, f3, x3, epsilon, [], varargin{:});
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case 22
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[g3,badg3] = numgrad2_(fcn, f3, x3, epsilon, abs(diag(H)), varargin{:});
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case 23
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[g3,badg3] = numgrad3_(fcn, f3, x3, epsilon, abs(diag(H)), varargin{:});
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case 25
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[g3,badg3] = numgrad5_(fcn, f3, x3, epsilon, abs(diag(H)), varargin{:});
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otherwise
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error('csminwel1: Unknown method for gradient evaluation!')
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end
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elseif ischar(grad),
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[g3 badg3] = feval(grad,x3,varargin{:});
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else
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[junk1,g3,junk2, cost_flag] = feval(fcn,x1,varargin{:});
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badg3 = ~cost_flag;
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end
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wall3=badg3;
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% g3
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save g3.mat g3 x3 f3 varargin;
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%ARGLIST
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%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
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end
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end
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else
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f3=f; x3=x; badg3=1; retcode3=101;
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end
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else
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f3=f; x3=x; badg3=1;retcode3=101;
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end
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else
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% normal iteration, no walls, or else we're finished here.
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f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
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end
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else
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f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
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end
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%how to pick gh and xh
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if f3 < f - crit && badg3==0 && f3 < f2 && f3 < f1
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ih=3;
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fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
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elseif f2 < f - crit && badg2==0 && f2 < f1
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ih=2;
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fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
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elseif f1 < f - crit && badg1==0
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ih=1;
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fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
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else
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[fh,ih] = min([f1,f2,f3]);
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%disp(sprintf('ih = %d',ih))
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%eval(['xh=x' num2str(ih) ';'])
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switch ih
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case 1
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xh=x1;
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case 2
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xh=x2;
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case 3
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xh=x3;
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end %case
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%eval(['gh=g' num2str(ih) ';'])
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%eval(['retcodeh=retcode' num2str(ih) ';'])
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retcodei=[retcode1,retcode2,retcode3];
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retcodeh=retcodei(ih);
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if exist('gh')
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nogh=isempty(gh);
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else
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nogh=1;
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end
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if nogh
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if NumGrad
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switch method
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case 2
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[gh,badgh] = numgrad2(fcn, fh, xh, epsilon, varargin{:});
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case 3
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[gh,badgh] = numgrad3(fcn, fh, xh, epsilon, varargin{:});
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case 5
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[gh,badgh] = numgrad5(fcn, fh, xh, epsilon, varargin{:});
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case 12
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[gh,badgh] = numgrad2_(fcn, fh, xh, epsilon, [], varargin{:});
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case 13
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[gh,badgh] = numgrad3_(fcn, fh, xh, epsilon, [], varargin{:});
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case 15
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[gh,badgh] = numgrad5_(fcn, fh, xh, epsilon, [], varargin{:});
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case 22
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[gh,badgh] = numgrad2_(fcn, fh, xh, epsilon, abs(diag(H)), varargin{:});
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case 23
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[gh,badgh] = numgrad3_(fcn, fh, xh, epsilon, abs(diag(H)), varargin{:});
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case 25
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[gh,badgh] = numgrad5_(fcn, fh, xh, epsilon, abs(diag(H)), varargin{:});
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otherwise
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error('csminwel1: Unknown method for gradient evaluation!')
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end
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elseif ischar(grad),
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[gh badgh] = feval(grad, xh,varargin{:});
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else
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[junk1,gh,junk2, cost_flag] = feval(fcn,x1,varargin{:});
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badgh = ~cost_flag;
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end
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end
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badgh=1;
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end
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%end of picking
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%ih
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%fh
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%xh
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%gh
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%badgh
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stuck = (abs(fh-f) < crit);
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if (~badg) && (~badgh) && (~stuck)
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H = bfgsi1(H,gh-g,xh-x);
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end
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if Verbose
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disp('----')
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disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
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end
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% if Verbose
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if itct > nit
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disp('iteration count termination')
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done = 1;
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elseif stuck
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disp('improvement < crit termination')
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done = 1;
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end
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rc=retcodeh;
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if rc == 1
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disp('zero gradient')
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elseif rc == 6
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disp('smallest step still improving too slow, reversed gradient')
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elseif rc == 5
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disp('largest step still improving too fast')
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elseif (rc == 4) || (rc==2)
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disp('back and forth on step length never finished')
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elseif rc == 3
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disp('smallest step still improving too slow')
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elseif rc == 7
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disp('warning: possible inaccuracy in H matrix')
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end
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% end
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f=fh;
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x=xh;
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g=gh;
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badg=badgh;
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end
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% what about making an m-file of 10 lines including numgrad.m
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% since it appears three times in csminwel.m |