diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index b9841745225d19f493e7a9f51c278c828cfb4b9e..bb7b1f5a3ab43506c03dfb2d032b4e3d634ddc22 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -487,6 +487,9 @@ options_.homotopy_force_continue = 0;
%csminwel optimization routine
csminwel.tolerance.f=1e-7;
csminwel.maxiter=1000;
+csminwel.verbosity=1;
+csminwel.Save_files=1;
+
options_.csminwel=csminwel;
%newrat optimization routine
@@ -494,6 +497,9 @@ newrat.hess=1; % dynare numerical hessian
newrat.tolerance.f=1e-5;
newrat.tolerance.f_analytic=1e-7;
newrat.maxiter=1000;
+newrat.verbosity=1;
+newrat.Save_files=1;
+
options_.newrat=newrat;
% Simplex optimization routine (variation on Nelder Mead algorithm).
diff --git a/matlab/bfgsi1.m b/matlab/optimization/bfgsi1.m
similarity index 69%
rename from matlab/bfgsi1.m
rename to matlab/optimization/bfgsi1.m
index c3b87ca49f8d550649da7047c39be84e972c65a3..696ee410b1ae82930b51c58ff3221e9c02d3bae0 100644
--- a/matlab/bfgsi1.m
+++ b/matlab/optimization/bfgsi1.m
@@ -1,13 +1,15 @@
-function H = bfgsi1(H0,dg,dx)
-% H = bfgsi1(H0,dg,dx)
+function H = bfgsi1(H0,dg,dx,Verbose,Save_files)
+% H = bfgsi1(H0,dg,dx,Verbose,Save_files)
% Update Inverse Hessian matrix
%
% Inputs:
% H0 [npar by npar] initial inverse Hessian matrix
% dg [npar by 1] previous change in gradient
% dx [npar by 1] previous change in x;
+% Verbose [scalar] Indicator for silent mode
+% Save_files [scalar] Indicator whether to save files
%
-% 6/8/93 version that updates inverse hessian instead of hessian
+% 6/8/93 version that updates inverse Hessian instead of Hessian
% itself.
%
% Original file downloaded from:
@@ -42,10 +44,12 @@ dgdx = dg'*dx;
if (abs(dgdx) >1e-12)
H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
else
- disp('bfgs update failed.')
- disp(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))]);
- disp(['dg''*dx = ' num2str(dgdx)])
- disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
+ disp_verbose('bfgs update failed.',Verbose)
+ disp_verbose(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))],Verbose);
+ disp_verbose(['dg''*dx = ' num2str(dgdx)],Verbose)
+ disp_verbose(['|H*dg| = ' num2str(Hdg'*Hdg)],Verbose)
H=H0;
end
-save('H.dat','H')
+if Save_files
+ save('H.dat','H')
+end
diff --git a/matlab/optimization/csminit1.m b/matlab/optimization/csminit1.m
index b6574b005ad8653d03cf5379ed709e1d58e11a02..428c704c60bb7470dc19ede6a4ad308b21fb2e1c 100644
--- a/matlab/optimization/csminit1.m
+++ b/matlab/optimization/csminit1.m
@@ -1,4 +1,4 @@
-function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,varargin)
+function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,Verbose,varargin)
% [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,varargin)
%
% Inputs:
@@ -101,7 +101,7 @@ else
% toc
dxnorm = norm(dx);
if dxnorm > 1e12
- disp('Near-singular H problem.')
+ disp_verbose('Near-singular H problem.',Verbose)
dx = dx*FCHANGE/dxnorm;
end
dfhat = dx'*g0;
@@ -115,10 +115,10 @@ else
dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
dfhat = dx'*g;
dxnorm = norm(dx);
- disp(sprintf('Correct for low angle: %g',a))
+ disp_verbose(sprintf('Correct for low angle: %g',a),Verbose)
end
end
- disp(sprintf('Predicted improvement: %18.9f',-dfhat/2))
+ 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.
@@ -141,7 +141,7 @@ else
%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(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ))
+ 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
@@ -176,7 +176,11 @@ else
if abs(lambda) < MINLAMB
if (lambda > 0) && (f0 <= fhat)
% try going against gradient, which may be inaccurate
- lambda = -lambda*factor^6
+ if Verbose
+ lambda = -lambda*factor^6
+ else
+ lambda = -lambda*factor^6;
+ end
else
if lambda < 0
retcode = 6;
@@ -222,4 +226,5 @@ else
end
end
end
-disp(sprintf('Norm of dx %10.5g', dxnorm))
+
+disp_verbose(sprintf('Norm of dx %10.5g', dxnorm),Verbose)
diff --git a/matlab/optimization/csminwel1.m b/matlab/optimization/csminwel1.m
index dcc579f50d4076b3a563217c56483b65ab90b552..5c641ba9e3da0d166f09a61c66d5ee915cb9e3d2 100644
--- a/matlab/optimization/csminwel1.m
+++ b/matlab/optimization/csminwel1.m
@@ -1,4 +1,4 @@
-function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
+function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,Verbose,Save_files,varargin)
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
% Inputs:
% fcn: [string] string naming the objective function to be minimized
@@ -65,7 +65,6 @@ fh = [];
xh = [];
[nx,no]=size(x0);
nx=max(nx,no);
-Verbose=1;
NumGrad= isempty(grad);
done=0;
itct=0;
@@ -87,7 +86,7 @@ retcodeh = [];
[f0,junk1,junk2,cost_flag] = feval(fcn,x0,varargin{:});
if ~cost_flag
- disp('Bad initial parameter.')
+ disp_verbose('Bad initial parameter.',Verbose)
return
end
@@ -110,15 +109,13 @@ while ~done
g1=[]; g2=[]; g3=[];
%addition fj. 7/6/94 for control
- if Verbose
- disp('-----------------')
- disp(sprintf('f at the beginning of new iteration, %20.10f',f))
- end
+ disp_verbose('-----------------',Verbose)
+ disp_verbose(sprintf('f at the beginning of new iteration, %20.10f',f),Verbose)
%-----------Comment out this line if the x vector is long----------------
- % disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
+ % disp_verbose([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
%-------------------------
itct=itct+1;
- [f1, x1, fc, retcode1] = csminit1(fcn,x,f,g,badg,H,varargin{:});
+ [f1, x1, fc, retcode1] = csminit1(fcn,x,f,g,badg,H,Verbose,varargin{:});
fcount = fcount+fc;
% erased on 8/4/94
% if (retcode == 1) || (abs(f1-f) < crit)
@@ -142,7 +139,9 @@ while ~done
end
wall1=badg1;
% g1
- save g1.mat g1 x1 f1 varargin;
+ if Save_files
+ save g1.mat g1 x1 f1 varargin;
+ end
end
if wall1 % && (~done) by Jinill
% Bad gradient or back and forth on step length. Possibly at
@@ -150,10 +149,8 @@ while ~done
%
%fcliff=fh;xcliff=xh;
Hcliff=H+diag(diag(H).*rand(nx,1));
- if Verbose
- disp('Cliff. Perturbing search direction.')
- end
- [f2, x2, fc, retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,varargin{:});
+ disp_verbose('Cliff. Perturbing search direction.',Verbose)
+ [f2, x2, fc, retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,Verbose,varargin{:});
fcount = fcount+fc; % put by Jinill
if f2 < f
if retcode2==2 || retcode2==4
@@ -169,11 +166,15 @@ while ~done
end
wall2=badg2;
% g2
- badg2
- save g2.mat g2 x2 f2 varargin
+ if Verbose
+ badg2
+ end
+ if Save_files
+ save g2.mat g2 x2 f2 varargin
+ end
end
if wall2
- disp('Cliff again. Try traversing')
+ disp_verbose('Cliff again. Try traversing',Verbose)
if norm(x2-x1) < 1e-13
f3=f; x3=x; badg3=1;retcode3=101;
else
@@ -181,7 +182,7 @@ while ~done
if(size(x0,2)>1)
gcliff=gcliff';
end
- [f3, x3, fc, retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),varargin{:});
+ [f3, x3, fc, retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),Verbose,varargin{:});
fcount = fcount+fc; % put by Jinill
if retcode3==2 || retcode3==4
wall3=1;
@@ -197,7 +198,9 @@ while ~done
end
wall3=badg3;
% g3
- save g3.mat g3 x3 f3 varargin;
+ if Save_files
+ save g3.mat g3 x3 f3 varargin;
+ end
end
end
else
@@ -225,7 +228,7 @@ while ~done
fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
else
[fh,ih] = min([f1,f2,f3]);
- %disp(sprintf('ih = %d',ih))
+ %disp_verbose(sprintf('ih = %d',ih))
%eval(['xh=x' num2str(ih) ';'])
switch ih
case 1
@@ -259,18 +262,16 @@ while ~done
%end of picking
stuck = (abs(fh-f) < crit);
if (~badg) && (~badgh) && (~stuck)
- H = bfgsi1(H,gh-g,xh-x);
- end
- if Verbose
- disp('----')
- disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
+ H = bfgsi1(H,gh-g,xh-x,Verbose,Save_files);
end
+ disp_verbose('----',Verbose)
+ disp_verbose(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh),Verbose)
% if Verbose
if itct > nit
- disp('iteration count termination')
+ disp_verbose('iteration count termination',Verbose)
done = 1;
elseif stuck
- disp('improvement < crit termination')
+ disp_verbose('improvement < crit termination',Verbose)
done = 1;
end
rc=retcodeh;
@@ -278,19 +279,19 @@ while ~done
if rc ==0
%do nothing, just a normal step
elseif rc == 1
- disp('zero gradient')
+ disp_verbose('zero gradient',Verbose)
elseif rc == 6
- disp('smallest step still improving too slow, reversed gradient')
+ disp_verbose('smallest step still improving too slow, reversed gradient',Verbose)
elseif rc == 5
- disp('largest step still improving too fast')
+ disp_verbose('largest step still improving too fast',Verbose)
elseif (rc == 4) || (rc==2)
- disp('back and forth on step length never finished')
+ disp_verbose('back and forth on step length never finished',Verbose)
elseif rc == 3
- disp('smallest step still improving too slow')
+ disp_verbose('smallest step still improving too slow',Verbose)
elseif rc == 7
- disp('warning: possible inaccuracy in H matrix')
+ disp_verbose('warning: possible inaccuracy in H matrix',Verbose)
else
- error('Unaccounted Case, please contact the developers')
+ error('Unaccounted Case, please contact the developers',Verbose)
end
end
diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index bf2ed53adf4001bf8895de3cb907823665899f9d..368003421f87d3c4a9c4bc240c0a2a085f62fb56 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -112,13 +112,15 @@ switch minimizer_algorithm
end
npar=length(start_par_value);
[LB, UB]=set_bounds_to_finite_values(bounds, options_.huge_number);
- fprintf('\nNumber of parameters= %d, initial temperatur= %4.3f \n', npar,sa_options.initial_temperature);
- fprintf('rt= %4.3f; TolFun= %4.3f; ns= %4.3f;\n',sa_options.rt,sa_options.TolFun,sa_options.ns);
- fprintf('nt= %4.3f; neps= %4.3f; MaxIter= %d\n',sa_options.nt,sa_options.neps,sa_options.MaxIter);
- fprintf('Initial step length(vm): %4.3f; step_length_c: %4.3f\n', sa_options.initial_step_length,sa_options.step_length_c);
- fprintf('%-20s %-6s %-6s %-6s\n','Name:', 'LB;','Start;','UB;');
- for pariter=1:npar
- fprintf('%-20s %6.4f; %6.4f; %6.4f;\n',parameter_names{pariter}, LB(pariter),start_par_value(pariter),UB(pariter));
+ if sa_options.verbosity
+ fprintf('\nNumber of parameters= %d, initial temperatur= %4.3f \n', npar,sa_options.initial_temperature);
+ fprintf('rt= %4.3f; TolFun= %4.3f; ns= %4.3f;\n',sa_options.rt,sa_options.TolFun,sa_options.ns);
+ fprintf('nt= %4.3f; neps= %4.3f; MaxIter= %d\n',sa_options.nt,sa_options.neps,sa_options.MaxIter);
+ fprintf('Initial step length(vm): %4.3f; step_length_c: %4.3f\n', sa_options.initial_step_length,sa_options.step_length_c);
+ fprintf('%-20s %-6s %-6s %-6s\n','Name:', 'LB;','Start;','UB;');
+ for pariter=1:npar
+ fprintf('%-20s %6.4f; %6.4f; %6.4f;\n',parameter_names{pariter}, LB(pariter),start_par_value(pariter),UB(pariter));
+ end
end
sa_options.initial_step_length= sa_options.initial_step_length*ones(npar,1); %bring step length to correct vector size
sa_options.step_length_c= sa_options.step_length_c*ones(npar,1); %bring step_length_c to correct vector size
@@ -152,6 +154,8 @@ switch minimizer_algorithm
nit = options_.csminwel.maxiter;
numgrad = options_.gradient_method;
epsilon = options_.gradient_epsilon;
+ Verbose = options_.csminwel.verbosity;
+ Save_files = options_.csminwel.Save_files;
% Change some options.
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
@@ -167,6 +171,10 @@ switch minimizer_algorithm
numgrad = options_list{i,2};
case 'NumgradEpsilon'
epsilon = options_list{i,2};
+ case 'verbosity'
+ Verbose = options_list{i,2};
+ case 'SaveFiles'
+ Save_files = options_list{i,2};
otherwise
warning(['csminwel: Unknown option (' options_list{i,1} ')!'])
end
@@ -180,7 +188,7 @@ switch minimizer_algorithm
end
% Call csminwell.
[fval,opt_par_values,grad,inverse_hessian_mat,itct,fcount,exitflag] = ...
- csminwel1(objective_function, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, varargin{:});
+ csminwel1(objective_function, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, Verbose, Save_files, varargin{:});
hessian_mat=inv(inverse_hessian_mat);
case 5
if options_.analytic_derivation==-1 %set outside as code for use of analytic derivation
@@ -193,6 +201,8 @@ switch minimizer_algorithm
newratflag = options_.newrat.hess; %default
end
nit=options_.newrat.maxiter;
+ Verbose = options_.newrat.verbosity;
+ Save_files = options_.newrat.Save_files;
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
@@ -208,12 +218,16 @@ switch minimizer_algorithm
end
case 'TolFun'
crit = options_list{i,2};
+ case 'verbosity'
+ Verbose = options_list{i,2};
+ case 'SaveFiles'
+ Save_files = options_list{i,2};
otherwise
warning(['newrat: Unknown option (' options_list{i,1} ')!'])
end
end
end
- [opt_par_values,hessian_mat,gg,fval,invhess] = newrat(objective_function,start_par_value,analytic_grad,crit,nit,0,varargin{:});
+ [opt_par_values,hessian_mat,gg,fval,invhess] = newrat(objective_function,start_par_value,analytic_grad,crit,nit,0,Verbose, Save_files,varargin{:});
%hessian_mat is the plain outer product gradient Hessian
case 6
[opt_par_values, hessian_mat, Scale, fval] = gmhmaxlik(objective_function, start_par_value, ...
@@ -255,6 +269,8 @@ switch minimizer_algorithm
simplexOptions.maxfcallfactor = options_list{i,2};
case 'InitialSimplexSize'
simplexOptions.delta_factor = options_list{i,2};
+ case 'verbosity'
+ simplexOptions.verbose = options_list{i,2};
otherwise
warning(['simplex: Unknown option (' options_list{i,1} ')!'])
end
@@ -278,6 +294,17 @@ switch minimizer_algorithm
cmaesOptions.TolX = options_list{i,2};
case 'MaxFunEvals'
cmaesOptions.MaxFunEvals = options_list{i,2};
+ case 'verbosity'
+ if options_list{i,2}==0
+ cmaesOptions.DispFinal = 'off'; % display messages like initial and final message';
+ cmaesOptions.DispModulo = '0'; % [0:Inf], disp messages after every i-th iteration';
+ end
+ case 'SaveFiles'
+ if options_list{i,2}==0
+ cmaesOptions.SaveVariables='off';
+ cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
+ cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
+ end
otherwise
warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
end
@@ -309,6 +336,12 @@ switch minimizer_algorithm
simpsaOptions.TEMP_END = options_list{i,2};
case 'MaxFunEvals'
simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
+ case 'verbosity'
+ if options_list{i,2} == 0
+ simpsaOptions.DISPLAY = 'none';
+ else
+ simpsaOptions.DISPLAY = 'iter';
+ end
otherwise
warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
end
diff --git a/matlab/optimization/mr_gstep.m b/matlab/optimization/mr_gstep.m
index 82c8d70f42cd3e2e15a2e4e2d6aefa103fcc8db0..51bfb09c447dcf7c7bd5c8a983fe1ace03be45e0 100644
--- a/matlab/optimization/mr_gstep.m
+++ b/matlab/optimization/mr_gstep.m
@@ -1,4 +1,4 @@
-function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
+function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,Verbose,Save_files,varargin)
% function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
%
% Gibbs type step in optimisation
@@ -69,14 +69,19 @@ while i<n
gg(i)=(f1(i)'-f_1(i)')./(2.*h1(i));
hh(i) = 1/max(1.e-9,abs( (f1(i)+f_1(i)-2*f0)./(h1(i)*h1(i)) ));
if gg(i)*(hh(i)*gg(i))/2 > htol
- [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),varargin{:});
+ [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),Verbose,varargin{:});
ig(i)=1;
- fprintf(['Done for param %s = %8.4f\n'],varargin{6}.name{i},x(i))
+ if Verbose
+ fprintf(['Done for param %s = %8.4f\n'],varargin{6}.name{i},x(i))
+ end
end
xh1=x;
end
+ if Save_files
+ save gstep.mat x h1 f0
+ end
+end
+if Save_files
save gstep.mat x h1 f0
end
-save gstep.mat x h1 f0
-
diff --git a/matlab/newrat.m b/matlab/optimization/newrat.m
similarity index 65%
rename from matlab/newrat.m
rename to matlab/optimization/newrat.m
index 424db0c1c4f5c410ed1c3551db6ae72a10b1a7bb..52676db9bebff7018464c3386cbc51495748e605 100644
--- a/matlab/newrat.m
+++ b/matlab/optimization/newrat.m
@@ -1,4 +1,4 @@
-function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, varargin)
+function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, Verbose, Save_files, varargin)
% [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
%
% Optimiser with outer product gradient and with sequences of univariate steps
@@ -49,6 +49,7 @@ function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ft
global objective_function_penalty_base
+
icount=0;
nx=length(x);
xparam1=x;
@@ -95,14 +96,19 @@ else
h1=[];
end
H = igg;
-disp(['Gradient norm ',num2str(norm(gg))])
+disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
-disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
-disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
+disp_verbose(['Minimum Hessian eigenvalue ',num2str(min(ee))],Verbose)
+disp_verbose(['Maximum Hessian eigenvalue ',num2str(max(ee))],Verbose)
g=gg;
check=0;
-if max(eig(hh))<0, disp('Negative definite Hessian! Local maximum!'), pause, end,
-save m1.mat x hh g hhg igg fval0
+if max(eig(hh))<0
+ disp_verbose('Negative definite Hessian! Local maximum!',Verbose)
+ pause
+end
+if Save_files
+ save m1.mat x hh g hhg igg fval0
+end
igrad=1;
igibbs=1;
@@ -116,13 +122,13 @@ while norm(gg)>gtol && check==0 && jit<nit
tic
icount=icount+1;
objective_function_penalty_base = fval0(icount);
- disp([' '])
- disp(['Iteration ',num2str(icount)])
- [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,varargin{:});
+ disp_verbose([' '],Verbose)
+ disp_verbose(['Iteration ',num2str(icount)],Verbose)
+ [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,Verbose,varargin{:});
if igrad
- [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,varargin{:});
+ [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,Verbose,varargin{:});
if (fval-fval1)>1
- disp('Gradient step!!')
+ disp_verbose('Gradient step!!',Verbose)
else
igrad=0;
end
@@ -139,27 +145,27 @@ while norm(gg)>gtol && check==0 && jit<nit
end
iggx=eye(length(gg));
iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
- [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,varargin{:});
+ [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,Verbose,varargin{:});
end
- [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,varargin{:});
+ [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,Verbose,Save_files,varargin{:});
nig=[nig ig];
- disp('Sequence of univariate steps!!')
+ disp_verbose('Sequence of univariate steps!!',Verbose)
fval=fvala;
if (fval0(icount)-fval)<ftol && flagit==0
- disp('Try diagonal Hessian')
+ disp_verbose('Try diagonal Hessian',Verbose)
ihh=diag(1./(diag(hhg)));
- [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,varargin{:});
+ [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,Verbose,varargin{:});
if (fval-fval2)>=ftol
- disp('Diagonal Hessian successful')
+ disp_verbose('Diagonal Hessian successful',Verbose)
end
fval=fval2;
end
if (fval0(icount)-fval)<ftol && flagit==0
- disp('Try gradient direction')
+ disp_verbose('Try gradient direction',Verbose)
ihh0=inx.*1.e-4;
- [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,varargin{:});
+ [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,Verbose,varargin{:});
if (fval-fval3)>=ftol
- disp('Gradient direction successful')
+ disp_verbose('Gradient direction successful',Verbose)
end
fval=fval3;
end
@@ -167,7 +173,7 @@ while norm(gg)>gtol && check==0 && jit<nit
x(:,icount+1)=xparam1;
fval0(icount+1)=fval;
if (fval0(icount)-fval)<ftol
- disp('No further improvement is possible!')
+ disp_verbose('No further improvement is possible!',Verbose)
check=1;
if analytic_derivation,
[fvalx,gg,hh]=feval(func0,xparam1,varargin{:});
@@ -189,29 +195,33 @@ while norm(gg)>gtol && check==0 && jit<nit
end
end
end
- 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(htol0)])
- disp(['Gradient norm ',num2str(norm(gg))])
+ 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(htol0)],Verbose)
+ disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
- disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
- disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
+ disp_verbose(['Minimum Hessian eigenvalue ',num2str(min(ee))],Verbose)
+ disp_verbose(['Maximum Hessian eigenvalue ',num2str(max(ee))],Verbose)
g(:,icount+1)=gg;
else
df = fval0(icount)-fval;
- disp(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))])
- disp(['FVAL ',num2str(fval)])
- disp(['Improvement ',num2str(df)])
- disp(['Ftol ',num2str(ftol)])
- disp(['Htol ',num2str(htol0)])
+ disp_verbose(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))],Verbose)
+ disp_verbose(['FVAL ',num2str(fval)],Verbose)
+ disp_verbose(['Improvement ',num2str(df)],Verbose)
+ disp_verbose(['Ftol ',num2str(ftol)],Verbose)
+ disp_verbose(['Htol ',num2str(htol0)],Verbose)
htol=htol_base;
if norm(x(:,icount)-xparam1)>1.e-12 && analytic_derivation==0,
try
- save m1.mat x fval0 nig -append
+ if Save_files
+ save m1.mat x fval0 nig -append
+ end
catch
- save m1.mat x fval0 nig
+ if Save_files
+ save m1.mat x fval0 nig
+ end
end
[dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,xparam1,func0,flagit,htol,varargin{:});
if isempty(dum),
@@ -220,12 +230,12 @@ while norm(gg)>gtol && check==0 && jit<nit
if htol0>htol
htol=htol0;
skipline()
- disp('Numerical noise in the likelihood')
- disp('Tolerance has to be relaxed')
+ disp_verbose('Numerical noise in the likelihood',Verbose)
+ disp_verbose('Tolerance has to be relaxed',Verbose)
skipline()
end
if ~outer_product_gradient,
- H = bfgsi1(H,gg-g(:,icount),xparam1-x(:,icount));
+ H = bfgsi1(H,gg-g(:,icount),xparam1-x(:,icount),Verbose,Save_files);
hh=inv(H);
hhg=hh;
else
@@ -246,39 +256,44 @@ while norm(gg)>gtol && check==0 && jit<nit
hhg=hh;
H = inv(hh);
end
- disp(['Gradient norm ',num2str(norm(gg))])
+ disp_verbose(['Gradient norm ',num2str(norm(gg))],Verbose)
ee=eig(hh);
- disp(['Minimum Hessian eigenvalue ',num2str(min(ee))])
- disp(['Maximum Hessian eigenvalue ',num2str(max(ee))])
- if max(eig(hh))<0, disp('Negative definite Hessian! Local maximum!'), pause(1), end,
+ 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)
+ end
t=toc;
- disp(['Elapsed time for iteration ',num2str(t),' s.'])
+ disp_verbose(['Elapsed time for iteration ',num2str(t),' s.'],Verbose)
g(:,icount+1)=gg;
-
- save m1.mat x hh g hhg igg fval0 nig H
+ if Save_files
+ save m1.mat x hh g hhg igg fval0 nig H
+ end
end
end
-
-save m1.mat x hh g hhg igg fval0 nig
+if Save_files
+ save m1.mat x hh g hhg igg fval0 nig
+end
if ftol>ftol0
skipline()
- disp('Numerical noise in the likelihood')
- disp('Tolerance had to be relaxed')
+ disp_verbose('Numerical noise in the likelihood',Verbose)
+ disp_verbose('Tolerance had to be relaxed',Verbose)
skipline()
end
if jit==nit
skipline()
- disp('Maximum number of iterations reached')
+ disp_verbose('Maximum number of iterations reached',Verbose)
skipline()
end
if norm(gg)<=gtol
- disp(['Estimation ended:'])
- disp(['Gradient norm < ', num2str(gtol)])
+ disp_verbose(['Estimation ended:'],Verbose)
+ disp_verbose(['Gradient norm < ', num2str(gtol)],Verbose)
end
if check==1,
- disp(['Estimation successful.'])
+ disp_verbose(['Estimation successful.'],Verbose)
end
-return
\ No newline at end of file
+return
diff --git a/matlab/optimization/simplex_optimization_routine.m b/matlab/optimization/simplex_optimization_routine.m
index 33843cf0d589caef3391eef246f5a3fc39cbdc9c..58b3ff1b097a9a633d74dc8bfbc4cf03a01c6e27 100644
--- a/matlab/optimization/simplex_optimization_routine.m
+++ b/matlab/optimization/simplex_optimization_routine.m
@@ -507,12 +507,12 @@ fval = fv(1);
exitflag = 1;
if func_count>= max_func_calls
- disp('Maximum number of objective function calls has been exceeded!')
+ disp_verbose('Maximum number of objective function calls has been exceeded!',verbose)
exitflag = 0;
end
if iter_count>= max_iterations
- disp('Maximum number of iterations has been exceeded!')
+ disp_verbose('Maximum number of iterations has been exceeded!',verbose)
exitflag = 0;
end
diff --git a/matlab/utilities/general/disp_verbose.m b/matlab/utilities/general/disp_verbose.m
new file mode 100644
index 0000000000000000000000000000000000000000..09c09e4d7e66ad001945c13aceadd84158f447c0
--- /dev/null
+++ b/matlab/utilities/general/disp_verbose.m
@@ -0,0 +1,25 @@
+function disp_verbose(input_string,Verbose)
+% function disp_verbose(input_string,Verbose)
+% Prints input_string unless Verbose=0 is requested
+%
+% Copyright (C) 2015 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+
+if Verbose
+ disp(input_string)
+end
\ No newline at end of file
diff --git a/tests/optimizers/optimizer_function_wrapper.m b/tests/optimizers/optimizer_function_wrapper.m
index a7992bab3e980c18478d6554229aa30d2f11198e..262f3d86d527ad8a07623023202df9d111dc0122 100644
--- a/tests/optimizers/optimizer_function_wrapper.m
+++ b/tests/optimizers/optimizer_function_wrapper.m
@@ -9,8 +9,9 @@ crit = 1e-7;
numgrad = 2;
epsilon = 1e-6;
analytic_grad=[];
-
+Verbose=1;
+Save_files=1;
%call optimizer
[fval,opt_par_values,grad,hessian_mat,itct,fcount,exitflag] = ...
- csminwel1(objective_function_handle, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, varargin{:});
+ csminwel1(objective_function_handle, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, Verbose,Save_files, varargin{:});
end
\ No newline at end of file