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Merge branch 'optimizer' of git.dynare.org:JohannesPfeifer/dynare

time-shift
Sébastien Villemot 2021-01-28 16:54:58 +01:00
parent 6ac1af7035 ce56305318
commit 3e7c0b1eef
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4 changed files with 73 additions and 70 deletions
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2
matlab/dynare_estimation_1.m
View File

@ -274,7 +274,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if compute_hessian
crit = options_.newrat.tolerance.f;
newratflag = newratflag>0;
hh = reshape(mr_hessian(xparam1,objective_function,fval,newratflag,crit,new_rat_hess_info,[bounds.lb bounds.ub],bayestopt_.p2,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
hh = reshape(mr_hessian(xparam1,objective_function,fval,newratflag,crit,new_rat_hess_info,[bounds.lb bounds.ub],bayestopt_.p2,0,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
end
options_.kalman_algo = kalman_algo0;
end

1
matlab/optimization/csminit1.m
View File

@ -129,7 +129,6 @@ else
done=0;
factor=3;
shrink=1;
lambdaMin=0;
lambdaMax=inf;
lambdaPeak=0;
fPeak=f0;

60
matlab/optimization/mr_hessian.m
View File

@ -1,15 +1,15 @@
function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,func,penalty,hflag,htol0,hess_info,bounds,prior_std,varargin)
% function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,func,penalty,hflag,htol0,hess_info,bounds,prior_std,varargin)
function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,func,penalty,hflag,htol0,hess_info,bounds,prior_std,Save_files,varargin)
% function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,func,penalty,hflag,htol0,hess_info,bounds,prior_std,Save_files,varargin)
% numerical gradient and Hessian, with 'automatic' check of numerical
% error
%
% adapted from Michel Juillard original routine hessian.m
%
% Inputs:
% - x parameter values
% - func function handle. The function must give two outputs:
% the log-likelihood AND the single contributions at times t=1,...,T
% of the log-likelihood to compute outer product gradient
% - x parameter values
% - penalty penalty due to error code
% - hflag 0: Hessian computed with outer product gradient, one point
% increments for partial derivatives in gradients
@ -26,6 +26,7 @@ function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,f
% computation of Hessian
% - bounds prior bounds of parameters
% - prior_std prior standard devation of parameters (can be NaN)
% - Save_files indicator whether files should be saved
% - varargin other inputs
% e.g. in dsge_likelihood
% varargin{1} --> DynareDataset
@ -99,11 +100,7 @@ while i<n
h10=hess_info.h1(i);
hcheck=0;
xh1(i)=x(i)+hess_info.h1(i);
try
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
it=1;
dx=(fx-f0);
ic=0;
@ -120,21 +117,13 @@ while i<n
hess_info.h1(i) = min(hess_info.h1(i),0.5*hmax(i));
hess_info.h1(i) = max(hess_info.h1(i),1.e-10);
xh1(i)=x(i)+hess_info.h1(i);
try
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
end
if abs(dx(it))>(3*hess_info.htol)
hess_info.h1(i)= hess_info.htol/abs(dx(it))*hess_info.h1(i);
hess_info.h1(i) = max(hess_info.h1(i),1e-10);
xh1(i)=x(i)+hess_info.h1(i);
try
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
[fx,exit_flag,ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
iter=0;
while (fx-f0)==0 && iter<50
hess_info.h1(i)= hess_info.h1(i)*2;
@ -188,7 +177,7 @@ gg=(f1'-f_1')./(2.*hess_info.h1);
if outer_product_gradient
if hflag==2
gg=(f1'-f_1')./(2.*hess_info.h1);
% full numerical Hessian
hessian_mat = zeros(size(f0,1),n*n);
for i=1:n
if i > 1
@ -209,19 +198,17 @@ if outer_product_gradient
xh1(j)=x(j);
xh_1(i)=x(i);
xh_1(j)=x(j);
j=j+1;
end
i=i+1;
end
elseif hflag==1
% full numerical 2nd order derivs only in diagonal
hessian_mat = zeros(size(f0,1),n*n);
for i=1:n
dum = (f1(:,i)+f_1(:,i)-2*f0)./(hess_info.h1(i)*h_1(i));
if dum>eps
hessian_mat(:,(i-1)*n+i)=dum;
else
hessian_mat(:,(i-1)*n+i)=max(eps, gg(i)^2);
end
hessian_mat(:,(i-1)*n+i)=dum;
if any(dum<=eps)
hessian_mat(dum<=eps,(i-1)*n+i)=max(eps, gg(i)^2);
end
end
end
@ -230,26 +217,27 @@ if outer_product_gradient
hh_mat0=ggh'*ggh; % outer product hessian
A=diag(2.*hess_info.h1); % rescaling matrix
% igg=inv(hh_mat); % inverted rescaled outer product hessian
ihh=A'*(hh_mat\A); % inverted outer product hessian
ihh=A'*(hh_mat\A); % inverted outer product hessian (based on rescaling)
if hflag>0 && min(eig(reshape(hessian_mat,n,n)))>0
hh0 = A*reshape(hessian_mat,n,n)*A'; %rescaled second order derivatives
hh = reshape(hessian_mat,n,n); %rescaled second order derivatives
hh = reshape(hessian_mat,n,n); %second order derivatives
sd0=sqrt(diag(hh0)); %rescaled 'standard errors' using second order derivatives
sd=sqrt(diag(hh_mat)); %rescaled 'standard errors' using outer product
hh_mat=hh_mat./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
igg=inv(hh_mat); % rescaled outer product hessian with 'true' std's
ihh=A'*(hh_mat\A); % inverted outer product hessian
hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with 'true' std's
ihh=A'*(hh_mat\A); % update inverted outer product hessian with 'true' std's
sd=sqrt(diag(ihh)); %standard errors
sdh=sqrt(1./diag(hh)); %diagonal standard errors
for j=1:length(sd)
% some heuristic normalizations of the standard errors that
% avoid numerical issues in outer product
sd0(j,1)=min(prior_std(j), sd(j)); %prior std
sd0(j,1)=10^(0.5*(log10(sd0(j,1))+log10(sdh(j,1))));
end
inv_A=inv(A);
ihh=ihh./(sd*sd').*(sd0*sd0'); %inverse outer product with modified std's
igg=inv(A)'*ihh*inv(A); % inverted rescaled outer product hessian with modified std's
hh_mat=inv(igg); % outer product rescaled hessian with modified std's
hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with modified std's
igg=inv_A'*ihh*inv_A; % inverted rescaled outer product hessian with modified std's
% hh_mat=inv(igg); % outer product rescaled hessian with modified std's
hh_mat0=inv_A'/igg*inv_A; % outer product hessian with modified std's
% sd0=sqrt(1./diag(hh0)); %rescaled 'standard errors' using second order derivatives
% sd=sqrt(diag(igg)); %rescaled 'standard errors' using outer product
% igg=igg./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
@ -267,7 +255,9 @@ if outer_product_gradient
hessian_mat=hh_mat0(:);
end
hh1=hess_info.h1;
save hess.mat hessian_mat
if Save_files
save('hess.mat','hessian_mat')
end
else
hessian_mat=[];
ihh=[];

80
matlab/optimization/newrat.m
View File

@ -70,7 +70,7 @@ nx=length(x);
xparam1=x;
%ftol0=1.e-6;
htol_base = max(1.e-7, ftol0);
flagit=0; % mode of computation of hessian in each iteration
flagit=0; % mode of computation of hessian in each iteration; hard-coded outer-product of gradients as it performed best in tests
ftol=ftol0;
gtol=1.e-3;
htol=htol_base;
@ -84,13 +84,17 @@ end
% func0 = str2func([func2str(func0),'_hh']);
% func0 = func0;
[fval0,exit_flag,gg,hh]=penalty_objective_function(x,func0,penalty,varargin{:});
if ~exit_flag
disp_verbose('Bad initial parameter.',Verbose)
return
end
fval=fval0;
% initialize mr_gstep and mr_hessian
outer_product_gradient=1;
if isempty(hh)
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(x,func0,penalty,flagit,htol,hess_info,bounds,prior_std,varargin{:});
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(x,func0,penalty,flagit,htol,hess_info,bounds,prior_std,Save_files,varargin{:});
if isempty(dum)
outer_product_gradient=0;
igg = 1e-4*eye(nx);
@ -117,15 +121,16 @@ else
h1=[];
end
H = igg;
disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
disp_verbose(['Minimum Hessian eigenvalue ',num2str(min(ee))],Verbose)
disp_verbose(['Maximum Hessian eigenvalue ',num2str(max(ee))],Verbose)
if Verbose
disp_eigenvalues_gradient(gg,hh);
end
g=gg;
check=0;
if max(eig(hh))<0
disp_verbose('Negative definite Hessian! Local maximum!',Verbose)
pause
if Verbose
if max(eig(hh))<0
disp('Negative definite Hessian! Local maximum!')
pause
end
end
if Save_files
save('m1.mat','x','hh','g','hhg','igg','fval0')
@ -135,7 +140,9 @@ igrad=1;
igibbs=1;
inx=eye(nx);
jit=0;
nig=[];
if Save_files
nig=[];
end
ig=ones(nx,1);
ggx=zeros(nx,1);
while norm(gg)>gtol && check==0 && jit<nit
@ -156,22 +163,25 @@ while norm(gg)>gtol && check==0 && jit<nit
fval=fval1;
x0=x01;
end
if length(find(ig))<nx
ggx=ggx*0;
ggx(find(ig))=gg(find(ig));
ig_pos=find(ig);
if length(ig_pos)<nx
ggx=ggx*0;
ggx(ig_pos)=gg(ig_pos);
if analytic_derivation || ~outer_product_gradient
hhx=hh;
else
hhx = reshape(dum,nx,nx);
end
iggx=eye(length(gg));
iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
iggx(ig_pos,ig_pos) = inv( hhx(ig_pos,ig_pos) );
[fvala,x0,fc,retcode] = csminit1(func0,x0,penalty,fval,ggx,0,iggx,Verbose,varargin{:});
end
x0 = check_bounds(x0,bounds);
[fvala, x0, ig] = mr_gstep(h1,x0,bounds,func0,penalty,htol0,Verbose,Save_files,gradient_epsilon, parameter_names,varargin{:});
x0 = check_bounds(x0,bounds);
nig=[nig ig];
if Save_files
nig=[nig ig];
end
disp_verbose('Sequence of univariate steps!!',Verbose)
fval=fvala;
if (fval0(icount)-fval)<ftol && flagit==0
@ -208,7 +218,7 @@ while norm(gg)>gtol && check==0 && jit<nit
if flagit==2
hh=hh0;
elseif flagg>0
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,bounds,prior_std,varargin{:});
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,bounds,prior_std,Save_files,varargin{:});
if flagg==2
hh = reshape(dum,nx,nx);
ee=eig(hh);
@ -220,15 +230,14 @@ while norm(gg)>gtol && check==0 && jit<nit
end
end
end
disp_verbose(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))],Verbose)
disp_verbose(['FVAL ',num2str(fval)],Verbose)
disp_verbose(['Improvement ',num2str(fval0(icount)-fval)],Verbose)
disp_verbose(['Ftol ',num2str(ftol)],Verbose)
disp_verbose(['Htol ',num2str(max(htol0))],Verbose)
disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
disp_verbose(['Minimum Hessian eigenvalue ',num2str(min(ee))],Verbose)
disp_verbose(['Maximum Hessian eigenvalue ',num2str(max(ee))],Verbose)
if Verbose
disp(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))])
disp(['FVAL ',num2str(fval)])
disp(['Improvement ',num2str(fval0(icount)-fval)])
disp(['Ftol ',num2str(ftol)])
disp(['Htol ',num2str(max(htol0))])
disp_eigenvalues_gradient(gg,hh);
end
g(:,icount+1)=gg;
else
df = fval0(icount)-fval;
@ -248,7 +257,7 @@ while norm(gg)>gtol && check==0 && jit<nit
save('m1.mat','x','fval0','nig')
end
end
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagit,htol,hess_info,bounds,prior_std,varargin{:});
[dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagit,htol,hess_info,bounds,prior_std,Save_files,varargin{:});
if isempty(dum)
outer_product_gradient=0;
end
@ -280,13 +289,11 @@ while norm(gg)>gtol && check==0 && jit<nit
hhg=hh;
H = inv(hh);
end
disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
disp_verbose(['Minimum Hessian eigenvalue ',num2str(min(ee))],Verbose)
disp_verbose(['Maximum Hessian eigenvalue ',num2str(max(ee))],Verbose)
if max(eig(hh))<0
disp_verbose('Negative definite Hessian! Local maximum!',Verbose)
pause(1)
if Verbose
if max(eig(hh))<0
disp('Negative definite Hessian! Local maximum!')
pause(1)
end
end
t=toc(tic1);
disp_verbose(['Elapsed time for iteration ',num2str(t),' s.'],Verbose)
@ -334,3 +341,10 @@ inx = find(x<=bounds(:,1));
if ~isempty(inx)
x(inx) = bounds(inx,1)+eps;
end
function ee=disp_eigenvalues_gradient(gg,hh)
disp(['Gradient norm ',num2str(norm(gg))])
ee=eig(hh);
disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])