From 02c74460e891e334f85cf80f0944698df9121ec5 Mon Sep 17 00:00:00 2001
From: Marco Ratto <marco.ratto@ec.europa.eu>
Date: Tue, 14 Feb 2023 17:22:59 +0100
Subject: [PATCH] distinguish between TolFun (optimizer termination criterion)
and TolGstep/TolGstepRel, used for tuning gradient step. So, we can keep for
example TolFun=1.e-5, while allowing coarser values for TolGstep. this may
help when maximizing non smooth surfaces (e.g. PKF or very large models),
where numerical noise may count. By default TolGstep=TolFun as in usual
historical behavior.
---
matlab/optimization/dynare_minimize_objective.m | 12 +++++++++++-
matlab/optimization/mr_hessian.m | 9 ++++++++-
matlab/optimization/newrat.m | 4 ++--
3 files changed, 21 insertions(+), 4 deletions(-)
diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index e611bed46d..0f82b81c02 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -289,6 +289,8 @@ switch minimizer_algorithm
end
nit=options_.newrat.maxiter;
epsilon = options_.gradient_epsilon;
+ gstep_crit = NaN;
+ gstep_crit_rel = NaN;
robust = false;
Verbose = options_.newrat.verbosity;
Save_files = options_.newrat.Save_files;
@@ -311,6 +313,10 @@ switch minimizer_algorithm
robust = options_list{i,2};
case 'TolFun'
crit = options_list{i,2};
+ case 'TolGstep'
+ gstep_crit = options_list{i,2};
+ case 'TolGstepRel'
+ gstep_crit_rel = options_list{i,2};
case 'verbosity'
Verbose = options_list{i,2};
case 'SaveFiles'
@@ -324,8 +330,12 @@ switch minimizer_algorithm
Save_files = 0;
Verbose = 0;
end
+ if isnan(gstep_crit)
+ gstep_crit = crit;
+ end
hess_info.gstep=options_.gstep;
- hess_info.htol = 1.e-4;
+ hess_info.htol = gstep_crit;
+ hess_info.htol_rel = gstep_crit_rel;
hess_info.h1=epsilon*ones(n_params,1);
hess_info.robust=robust;
% here we force 7th input argument (flagg) to be 0, since outer product
diff --git a/matlab/optimization/mr_hessian.m b/matlab/optimization/mr_hessian.m
index c17b161b67..b763b70c87 100644
--- a/matlab/optimization/mr_hessian.m
+++ b/matlab/optimization/mr_hessian.m
@@ -83,6 +83,11 @@ hess_info.h1 = min(hess_info.h1,0.9.*hmax);
if htol0<hess_info.htol
hess_info.htol=htol0;
end
+if not(isnan(hess_info.htol_rel))
+ htol1=hess_info.htol;
+ hess_info.htol=abs(hess_info.htol_rel*f0);
+end
+
xh1=x;
f1=zeros(size(f0,1),n);
f_1=f1;
@@ -298,4 +303,6 @@ else
hh1 = [];
end
-htol1=hhtol;
+if isnan(hess_info.htol_rel)
+ htol1=hhtol;
+end
diff --git a/matlab/optimization/newrat.m b/matlab/optimization/newrat.m
index 68a9898792..2106ae0e49 100644
--- a/matlab/optimization/newrat.m
+++ b/matlab/optimization/newrat.m
@@ -69,7 +69,7 @@ icount=0;
nx=length(x);
xparam1=x;
%ftol0=1.e-6;
-htol_base = max(1.e-7, ftol0);
+htol_base = max(1.e-7, hess_info.htol);
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;
@@ -232,7 +232,7 @@ 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,Save_files,varargin{:});
+ [dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,htol_base,hess_info,bounds,prior_std,Save_files,varargin{:});
if flagg==2
hh = reshape(dum,nx,nx);
ee=eig(hh);
--
GitLab