276 lines
8.6 KiB
Matlab
276 lines
8.6 KiB
Matlab
function [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
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% [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
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%
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% Optimiser with outer product gradient and with sequences of univariate steps
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% uses Chris Sims subroutine for line search
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%
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% func0 = name of the function
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% there must be a version of the function called [func0,'_hh.m'], that also
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% gives as second OUTPUT the single contributions at times t=1,...,T
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% of the log-likelihood to compute outer product gradient
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%
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% x = starting guess
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% hh = initial Hessian [OPTIONAL]
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% gg = initial gradient [OPTIONAL]
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% igg = initial inverse Hessian [OPTIONAL]
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% ftol0 = ending criterion for function change
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% nit = maximum number of iterations
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%
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% In each iteration, Hessian is computed with outer product gradient.
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% for final Hessian (to start Metropolis):
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% flagg = 0, final Hessian computed with outer product gradient
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% flagg = 1, final 'mixed' Hessian: diagonal elements computed with numerical second order derivatives
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% with correlation structure as from outer product gradient,
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% flagg = 2, full numerical Hessian
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%
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% varargin = list of parameters for func0
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% Copyright (C) 2004-2008 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 bayestopt_
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icount=0;
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nx=length(x);
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xparam1=x;
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%ftol0=1.e-6;
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htol_base = max(1.e-5, ftol0);
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flagit=0; % mode of computation of hessian in each iteration
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ftol=ftol0;
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gtol=1.e-3;
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htol=htol_base;
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htol0=htol_base;
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gibbstol=length(bayestopt_.pshape)/50; %25;
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func_hh = [func0,'_hh'];
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func = str2func(func0);
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fval0=feval(func,x,varargin{:});
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fval=fval0;
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if isempty(hh)
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[dum, gg, htol0, igg, hhg]=mr_hessian(func_hh,x,flagit,htol,varargin{:});
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hh0 = reshape(dum,nx,nx);
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hh=hhg;
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if min(eig(hh0))<0,
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hh0=hhg; %generalized_cholesky(hh0);
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elseif flagit==2,
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hh=hh0;
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igg=inv(hh);
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end
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if htol0>htol,
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htol=htol0;
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%ftol=htol0;
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end
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else
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hh0=hh;
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hhg=hh;
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igg=inv(hh);
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end
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disp(['Gradient norm ',num2str(norm(gg))])
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ee=eig(hh);
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disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
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disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
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g=gg;
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check=0;
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if max(eig(hh))<0, disp('Negative definite Hessian! Local maximum!'), pause, end,
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save m1 x hh g hhg igg fval0
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igrad=1;
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igibbs=1;
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inx=eye(nx);
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jit=0;
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nig=[];
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ig=ones(nx,1);
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ggx=zeros(nx,1);
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while norm(gg)>gtol & check==0 & jit<nit,
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jit=jit+1;
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tic
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icount=icount+1;
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bayestopt_.penalty = fval0(icount);
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disp([' '])
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disp(['Iteration ',num2str(icount)])
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[fval x0 fc retcode] = csminit(func0,xparam1,fval0(icount),gg,0,igg,varargin{:});
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if igrad,
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[fval1 x01 fc retcode1] = csminit(func0,x0,fval,gg,0,inx,varargin{:});
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if (fval-fval1)>1, %(fval0(icount)-fval),
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disp('Gradient step!!')
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else
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igrad=0;
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end
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fval=fval1;
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x0=x01;
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end
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if (fval0(icount)-fval)<1.e-2*(gg'*(igg*gg))/2 & igibbs,
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if length(find(ig))<nx,
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ggx=ggx*0;
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ggx(find(ig))=gg(find(ig));
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hhx = reshape(dum,nx,nx);
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iggx=eye(length(gg));
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iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
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[fvala x0 fc retcode] = csminit(func0,x0,fval,ggx,0,iggx,varargin{:});
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end
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[fvala, x0, ig] = mr_gstep(func0,x0,htol,varargin{:});
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nig=[nig ig];
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if (fval-fvala)<gibbstol*(fval0(icount)-fval),
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igibbs=0;
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disp('Last sequence of univariate step, gain too small!!')
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else
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disp('Sequence of univariate steps!!')
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end
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fval=fvala;
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end
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if (fval0(icount)-fval)<ftol & flagit==0,
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disp('Try diagonal Hessian')
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ihh=diag(1./(diag(hhg)));
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[fval2 x0 fc retcode2] = csminit(func2str(func),x0,fval,gg,0,ihh,varargin{:});
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if (fval-fval2)>=ftol ,
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%hh=diag(diag(hh));
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disp('Diagonal Hessian successful')
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end
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fval=fval2;
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end
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if (fval0(icount)-fval)<ftol & flagit==0,
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disp('Try gradient direction')
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ihh0=inx.*1.e-4;
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[fval3 x0 fc retcode3] = csminit(func2str(func),x0,fval,gg,0,ihh0,varargin{:});
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if (fval-fval3)>=ftol ,
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%hh=hh0;
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%ihh=ihh0;
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disp('Gradient direction successful')
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end
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fval=fval3;
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end
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xparam1=x0;
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x(:,icount+1)=xparam1;
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fval0(icount+1)=fval;
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%if (fval0(icount)-fval)<ftol*ftol & flagg==1;,
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if (fval0(icount)-fval)<ftol,
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disp('No further improvement is possible!')
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check=1;
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if flagit==2,
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hh=hh0;
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elseif flagg>0,
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[dum, gg, htol0, igg, hhg]=mr_hessian(func_hh,xparam1,flagg,ftol0,varargin{:});
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if flagg==2,
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hh = reshape(dum,nx,nx);
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ee=eig(hh);
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if min(ee)<0
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hh=hhg;
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end
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else
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hh=hhg;
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end
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end
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disp(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))])
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disp(['FVAL ',num2str(fval)])
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disp(['Improvement ',num2str(fval0(icount)-fval)])
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disp(['Ftol ',num2str(ftol)])
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disp(['Htol ',num2str(htol0)])
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disp(['Gradient norm ',num2str(norm(gg))])
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ee=eig(hh);
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disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
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disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
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g(:,icount+1)=gg;
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else
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df = fval0(icount)-fval;
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disp(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))])
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disp(['FVAL ',num2str(fval)])
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disp(['Improvement ',num2str(df)])
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disp(['Ftol ',num2str(ftol)])
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disp(['Htol ',num2str(htol0)])
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if df<htol0,
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htol=max(htol_base,df/10);
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end
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if norm(x(:,icount)-xparam1)>1.e-12,
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try
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save m1 x fval0 nig -append
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catch
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save m1 x fval0 nig
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end
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[dum, gg, htol0, igg, hhg]=mr_hessian(func_hh,xparam1,flagit,htol,varargin{:});
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if htol0>htol, %ftol,
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%ftol=htol0;
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htol=htol0;
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disp(' ')
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disp('Numerical noise in the likelihood')
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disp('Tolerance has to be relaxed')
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disp(' ')
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% elseif htol0<ftol,
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% ftol=max(htol0, ftol0);
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end
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hh0 = reshape(dum,nx,nx);
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hh=hhg;
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if flagit==2,
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if min(eig(hh0))<=0,
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hh0=hhg; %generalized_cholesky(hh0);
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else
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hh=hh0;
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igg=inv(hh);
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end
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end
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end
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disp(['Gradient norm ',num2str(norm(gg))])
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ee=eig(hh);
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disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
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disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
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if max(eig(hh))<0, disp('Negative definite Hessian! Local maximum!'), pause, end,
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t=toc;
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disp(['Elapsed time for iteration ',num2str(t),' s.'])
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g(:,icount+1)=gg;
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save m1 x hh g hhg igg fval0 nig
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end
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end
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save m1 x hh g hhg igg fval0 nig
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if ftol>ftol0,
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disp(' ')
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disp('Numerical noise in the likelihood')
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disp('Tolerance had to be relaxed')
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disp(' ')
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end
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if jit==nit,
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disp(' ')
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disp('Maximum number of iterations reached')
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disp(' ')
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end
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if norm(gg)<=gtol,
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disp(['Estimation ended:'])
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disp(['Gradient norm < ', num2str(gtol)])
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end
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if check==1,
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disp(['Estimation successful.'])
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end
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return
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%
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function f00 = lsearch(lam,func,x,dx,varargin)
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x0=x-dx*lam;
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f00=feval(func,x0,varargin{:});
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