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function [x,f,exitflag,n_f_evals,n_grad_evals,n_constraint_evals,n_constraint_gradient_evals]=solvopt(x,fun,grad,func,gradc,optim,varargin)
% [x,f,options]=solvopt(x,fun,grad,func,gradc,options,varargin)
%
% The function SOLVOPT, developed by Alexei Kuntsevich and Franz Kappe,
% performs a modified version of Shor's r-algorithm in
% order to find a local minimum resp. maximum of a nonlinear function
% defined on the n-dimensional Euclidean space or % a solution of a nonlinear
% constrained problem:
% min { f(x): g(x) (<)= 0, g(x) in R(m), x in R(n) }
%
% Inputs:
% x n-vector (row or column) of the coordinates of the starting
% point,
% fun name of an M-file (M-function) which computes the value
% of the objective function <fun> at a point x,
% synopsis: f=fun(x)
% grad name of an M-file (M-function) which computes the gradient
% vector of the function <fun> at a point x,
% synopsis: g=grad(x)
% func name of an M-file (M-function) which computes the MAXIMAL
% RESIDUAL(!) for a set of constraints at a point x,
% synopsis: fc=func(x)
% gradc name of an M-file (M-function) which computes the gradient
% vector for the maximal residual constraint at a point x,
% synopsis: gc=gradc(x)
% optim Options structure with fields:
% optim.minimizer_indicator= H, where sign(H)=-1 resp. sign(H)=+1 means minimize
% resp. maximize <fun> (valid only for unconstrained problem)
% and H itself is a factor for the initial trial step size
% (optim.minimizer_indicator=-1 by default),
% optim.TolX= relative error for the argument in terms of the
% infinity-norm (1.e-4 by default),
% optim.TolFun= relative error for the function value (1.e-6 by default),
% optim.MaxIter= limit for the number of iterations (15000 by default),
% optim.verbosity= control of the display of intermediate results and error
% resp. warning messages (default value is 0, i.e., no intermediate
% output but error and warning messages, see more in the manual),
% optim.TolXConstraint= admissible maximal residual for a set of constraints
% (optim.TolXConstraint=1e-8 by default, see more in the manual),
% *optim.SpaceDilation= the coefficient of space dilation (2.5 by default),
% *optim.LBGradientStep= lower bound for the stepsize used for the difference
% approximation of gradients (1e-12 by default, see more in the manual).
% (* ... changes should be done with care)
%
% Outputs:
% x optimal parameter vector (row resp. column),
% f optimum function value
% exitflag: the number of iterations, if positive,
% or an abnormal stop code, if negative (see more in the manual),
% n_f_evals: number of objective evaluations
% n_grad_evals: number of gradient evaluations,
% n_constraint_evals: number of constraint function evaluations,
% n_constraint_gradient_evals number of constraint gradient evaluations.
%
%
% Algorithm: Kuntsevich, A.V., Kappel, F., SolvOpt - The solver for local nonlinear optimization problems
% (version 1.1, Matlab, C, FORTRAN). University of Graz, Graz, 1997.
%
%
% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
% Copyright (C) 2008-2015 Giovanni Lombardo
% Copyright (C) 2015-2017 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/>.
% strings: ----{
errmes='SolvOpt error:';
wrnmes='SolvOpt warning:';
error1='No function name and/or starting point passed to the function.';
error2='Argument X has to be a row or column vector of dimension > 1.';
error30='<fun> returns an empty string.';
error31='Function value does not exist (NaN is returned).';
error32='Function equals infinity at the point.';
error40='<grad> returns an improper matrix. Check the dimension.';
error41='Gradient does not exist (NaN is returned by <grad>).';
error42='Gradient equals infinity at the starting point.';
error43='Gradient equals zero at the starting point.';
error50='<func> returns an empty string.';
error51='<func> returns NaN at the point.';
error52='<func> returns infinite value at the point.';
error60='<gradc> returns an improper vector. Check the dimension';
error61='<gradc> returns NaN at the point.';
error62='<gradc> returns infinite vector at the point.';
error63='<gradc> returns zero vector at an infeasible point.';
error5='Function is unbounded.';
error6='Choose another starting point.';
warn1= 'Gradient is zero at the point, but stopping criteria are not fulfilled.';
warn20='Normal re-setting of a transformation matrix.' ;
warn21='Re-setting due to the use of a new penalty coefficient.' ;
warn4= 'Iterations limit exceeded.';
warn31='The function is flat in certain directions.';
warn32='Trying to recover by shifting insensitive variables.';
warn09='Re-run from recorded point.';
warn08='Ravine with a flat bottom is detected.';
termwarn0='SolvOpt: Normal termination.';
termwarn1='SolvOpt: Termination warning:';
appwarn='The above warning may be reasoned by inaccurate gradient approximation';
endwarn=[...
'Premature stop is possible. Try to re-run the routine from the obtained point. ';...
'Result may not provide the optimum. The function apparently has many extremum points. ';...
'Result may be inaccurate in the coordinates. The function is flat at the optimum. ';...
'Result may be inaccurate in a function value. The function is extremely steep at the optimum.'];
% ----}
% ARGUMENTS PASSED ----{
if nargin<2 % Function and/or starting point are not specified
exitflag=-1;
disp(errmes);
disp(error1);
return
end
if nargin<3
app=1; % No user-supplied gradients
elseif isempty(grad)
app=1;
else
app=0; % Exact gradients are supplied
end
% OPTIONS ----{
doptions.minimizer_indicator=1;
doptions.TolX=1e-6; %accuracy of argument
doptions.TolFun=1e-6; %accuracy of function (see Solvopt p.29)
doptions.MaxIter=15000;
doptions.verbosity=1;
doptions.TolXConstraint=1.e-8;
doptions.SpaceDilation=2.5;
doptions.LBGradientStep=1.e-11;
if nargin<4
optim=doptions;
elseif isempty(optim)
optim=doptions;
end
% Check the values:
optim.TolX=max(optim.TolX,1.e-12);
optim.TolFun=max(optim.TolFun,1.e-12);
optim.TolX=max(optim.LBGradientStep*1.e2,optim.TolX);
optim.TolX=min(optim.TolX,1);
optim.TolFun=min(optim.TolFun,1);
optim.TolXConstraint=max(optim.TolXConstraint,1e-12);
optim.SpaceDilation=max([optim.SpaceDilation,1.5]);
optim.LBGradientStep=max(optim.LBGradientStep,1e-11);
% ----}
if isempty(func)
constr=0;
else
constr=1; % Constrained problem
if isempty(gradc)
appconstr=1;
else
appconstr=0; % Exact gradients of constraints are supplied
end
end
% ----}
% STARTING POINT ----{
if max(size(x))<=1
disp(errmes);
disp(error2);
exitflag=-2;
return
elseif size(x,2)==1
n=size(x,1);
x=x';
trx=1;
elseif size(x,1)==1
n=size(x,2);
trx=0;
else
disp(errmes);
disp(error2);
exitflag=-2;
return
end
% ----}
% WORKING CONSTANTS AND COUNTERS ----{
n_f_evals=0; n_grad_evals=0; % function and gradient calculations
if constr
n_constraint_evals=0;
n_constraint_gradient_evals=0; % same for constraints
end
epsnorm=1.e-15;
epsnorm2=1.e-30; % epsilon & epsilon^2
if constr, h1=-1 % NLP: restricted to minimization
cnteps=optim.TolXConstraint; % Max. admissible residual
else
h1=sign(optim.minimizer_indicator); % Minimize resp. maximize a function
end
k=0; % Iteration counter
wdef=1/optim.SpaceDilation-1; % Default space transf. coeff.
%Gamma control ---{
ajb=1+.1/n^2; % Base I
ajp=20;
ajpp=ajp; % Start value for the power
ajs=1.15; % Base II
knorms=0; gnorms=zeros(1,10); % Gradient norms stored
%---}
%Display control ---{
if optim.verbosity<=0, dispdata=0
if optim.verbosity==-1
dispwarn=0;
else
dispwarn=1;
end
else
dispdata=round(optim.verbosity);
dispwarn=1;
end
ld=dispdata;
%---}
%Stepsize control ---{
dq=5.1; % Step divider (at f_{i+1}>gamma*f_{i})
du20=2;du10=1.5;du03=1.05; % Step multipliers (at certain steps made)
kstore=3;nsteps=zeros(1,kstore); % Steps made at the last 'kstore' iterations
if app
des=6.3; % Desired number of steps per 1-D search
else
des=3.3;
end
mxtc=3; % Number of trial cycles (steep wall detect)
%---}
termx=0; limxterm=50; % Counter and limit for x-criterion
ddx =max(1e-11,optim.LBGradientStep); % stepsize for gradient approximation
low_bound=-1+1e-4; % Lower bound cosine used to detect a ravine
ZeroGrad=n*1.e-16; % Lower bound for a gradient norm
nzero=0; % Zero-gradient events counter
% Lower bound for values of variables taking into account
lowxbound=max([optim.TolX,1e-3]);
% Lower bound for function values to be considered as making difference
lowfbound=optim.TolFun^2;
krerun=0; % Re-run events counter
detfr=optim.TolFun*100; % relative error for f/f_{record}
detxr=optim.TolX*10; % relative error for norm(x)/norm(x_{record})
warnno=0; % the number of warn.mess. to end with
kflat=0; % counter for points of flatness
stepvanish=0; % counter for vanished steps
stopf=0;
% ----} End of setting constants
% ----} End of the preamble
% COMPUTE THE FUNCTION ( FIRST TIME ) ----{
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if isempty(f)
if dispwarn
disp(errmes)
disp(error30)
end
exitflag=-3;
if trx
x=x';
end
return
elseif isnan(f)
if dispwarn
disp(errmes)
disp(error31)
disp(error6)
end
exitflag=-3;
if trx
x=x';
end
return
elseif abs(f)==Inf
if dispwarn
disp(errmes)
disp(error32)
disp(error6)
end
exitflag=-3;
if trx
x=x';
end
return
end
xrec=x; frec=f; % record point and function value
% Constrained problem
if constr, fp=f; kless=0
if trx
fc=feval(func,x');
else
fc=feval(func,x);
end
if isempty(fc)
if dispwarn
disp(errmes)
disp(error50)
end
exitflag=-5;
if trx
x=x';
end
return
elseif isnan(fc)
if dispwarn
disp(errmes)
disp(error51)
disp(error6)
end
exitflag=-5;
if trx
x=x';
end
return
elseif abs(fc)==Inf
if dispwarn
disp(errmes)
disp(error52)
disp(error6)
end
exitflag=-5;
if trx
x=x';
end
return
end
n_constraint_evals=n_constraint_evals+1;
PenCoef=1; % first rough approximation
if fc<=cnteps
FP=1; fc=0; % feasible point
else
FP=0; % infeasible point
end
f=f+PenCoef*fc;
end
% ----}
% COMPUTE THE GRADIENT ( FIRST TIME ) ----{
if app
deltax=h1*ddx*ones(size(x));
if constr
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,f,fun,deltax,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1, g=g'; end
ng=norm(g);
if size(g,2)~=n
if dispwarn
disp(errmes)
disp(error40)
end
exitflag=-4;
if trx
x=x';
end
return
elseif isnan(ng)
if dispwarn
disp(errmes)
disp(error41)
disp(error6)
end
exitflag=-4;
if trx
x=x';
end
return
elseif ng==Inf
if dispwarn
disp(errmes)
disp(error42)
disp(error6)
end
exitflag=-4;
if trx
x=x';
end
return
elseif ng<ZeroGrad
if dispwarn
disp(errmes)
disp(error43)
disp(error6)
end
exitflag=-4;
if trx
x=x';
end
return
end
if constr
if ~FP
if appconstr
deltax=sign(x); idx=find(deltax==0);
deltax(idx)=ones(size(idx));
deltax=ddx*deltax;
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
gc=feval(gradc,x);
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
if size(gc,2)==1
gc=gc';
end
ngc=norm(gc);
if size(gc,2)~=n
if dispwarn
disp(errmes)
disp(error60)
end
exitflag=-6;
if trx
x=x';
end
return
elseif isnan(ngc)
if dispwarn
disp(errmes)
disp(error61)
disp(error6)
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc==Inf
if dispwarn
disp(errmes)
disp(error62)
disp(error6)
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc<ZeroGrad
if dispwarn
disp(errmes)
disp(error63)
end
exitflag=-6;
if trx
x=x';
end
return
end
g=g+PenCoef*gc; ng=norm(g);
end
end
grec=g; nng=ng;
% ----}
% INITIAL STEPSIZE
h=h1*sqrt(optim.TolX)*max(abs(x)); % smallest possible stepsize
if abs(optim.minimizer_indicator)~=1
h=h1*max(abs([optim.minimizer_indicator,h])); % user-supplied stepsize
else
h=h1*max(1/log(ng+1.1),abs(h)); % calculated stepsize
end
% RESETTING LOOP ----{
while 1
kcheck=0; % Set checkpoint counter.
kg=0; % stepsizes stored
kj=0; % ravine jump counter
B=eye(n); % re-set transf. matrix to identity
fst=f; g1=g; dx=0;
% ----}
% MAIN ITERATIONS ----{
while 1
k=k+1;kcheck=kcheck+1;
laststep=dx;
% ADJUST GAMMA --{
gamma=1+max([ajb^((ajp-kcheck)*n),2*optim.TolFun]);
gamma=min([gamma,ajs^max([1,log10(nng+1)])]);
% --}
gt=g*B; w=wdef;
% JUMPING OVER A RAVINE ----{
if (gt/norm(gt))*(g1'/norm(g1))<low_bound
if kj==2
xx=x;
end
if kj==0
kd=4
end
kj=kj+1; w=-.9; h=h*2; % use large coef. of space dilation
if kj>2*kd
kd=kd+1;
warnno=1;
if any(abs(x-xx)<epsnorm*abs(x)) % flat bottom is detected
if dispwarn
disp(wrnmes)
disp(warn08)
end
end
end
else
kj=0;
end
% ----}
% DILATION ----{
z=gt-g1;
nrmz=norm(z);
if(nrmz>epsnorm*norm(gt))
z=z/nrmz;
g1=gt+w*(z*gt')*z; B=B+w*(B*z')*z;
else
z=zeros(1,n);
nrmz=0;
g1=gt;
end
d1=norm(g1); g0=(g1/d1)*B';
% ----}
% RESETTING ----{
if kcheck>1
idx=find(abs(g)>ZeroGrad); numelem=size(idx,2);
if numelem>0, grbnd=epsnorm*numelem^2
if all(abs(g1(idx))<=abs(g(idx))*grbnd) || nrmz==0
if dispwarn
disp(wrnmes)
disp(warn20)
end
if abs(fst-f)<abs(f)*.01
ajp=ajp-10*n;
else
ajp=ajpp;
end
h=h1*dx/3;
k=k-1;
break
end
end
end
% ----}
% STORE THE CURRENT VALUES AND SET THE COUNTERS FOR 1-D SEARCH
xopt=x;fopt=f; k1=0;k2=0;ksm=0;kc=0;knan=0; hp=h;
if constr, Reset=0; end
% 1-D SEARCH ----{
while 1
x1=x;f1=f;
if constr
FP1=FP;
fp1=fp;
end
x=x+hp*g0;
% FUNCTION VALUE
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if h1*f==Inf
if dispwarn
disp(errmes)
disp(error5)
end
exitflag=-7;
if trx
x=x';
end
return
end
if constr, fp=f;
if trx
fc=feval(func,x');
else
fc=feval(func,x);
end
n_constraint_evals=n_constraint_evals+1;
if isnan(fc)
if dispwarn
disp(errmes)
disp(error51)
disp(error6)
end
exitflag=-5;
if trx
x=x';
end
return
elseif abs(fc)==Inf
if dispwarn
disp(errmes)
disp(error52)
disp(error6)
end
exitflag=-5;
if trx
x=x';
end
return
end
if fc<=cnteps
FP=1;
fc=0;
else
FP=0;
fp_rate=(fp-fp1);
if fp_rate<-epsnorm
if ~FP1
PenCoefNew=-15*fp_rate/norm(x-x1);
if PenCoefNew>1.2*PenCoef
PenCoef=PenCoefNew; Reset=1; kless=0; f=f+PenCoef*fc; break
end
end
end
end
f=f+PenCoef*fc;
end
if abs(f)==Inf || isnan(f)
if dispwarn, disp(wrnmes)
if isnan(f)
disp(error31)
else
disp(error32)
end
end
if ksm || kc>=mxtc
exitflag=-3;
if trx
x=x';
end
return
else
k2=k2+1;
k1=0;
hp=hp/dq;
x=x1;
f=f1;
knan=1;
if constr
FP=FP1;
fp=fp1;
end
end
% STEP SIZE IS ZERO TO THE EXTENT OF EPSNORM
elseif all(abs(x-x1)<abs(x)*epsnorm)
stepvanish=stepvanish+1;
if stepvanish>=5
exitflag=-14;
if dispwarn
disp(termwarn1)
disp(endwarn(4,:))
end
if trx
x=x';
end
return
else
x=x1;
f=f1;
hp=hp*10;
ksm=1;
if constr
FP=FP1;
fp=fp1;
end
end
% USE SMALLER STEP
elseif h1*f<h1*gamma^sign(f1)*f1
if ksm
break
end
k2=k2+1;k1=0; hp=hp/dq; x=x1;f=f1;
if constr
FP=FP1;
fp=fp1;
end
if kc>=mxtc, break, end
% 1-D OPTIMIZER IS LEFT BEHIND
else
if h1*f<=h1*f1
break
end
% USE LARGER STEP
k1=k1+1;
if k2>0
kc=kc+1;
end
k2=0;
if k1>=20
hp=du20*hp;
elseif k1>=10
hp=du10*hp;
elseif k1>=3
hp=du03*hp;
end
end
end
% ----} End of 1-D search
% ADJUST THE TRIAL STEP SIZE ----{
dx=norm(xopt-x);
if kg<kstore
kg=kg+1;
end
if kg>=2
nsteps(2:kg)=nsteps(1:kg-1);
end
nsteps(1)=dx/(abs(h)*norm(g0));
kk=sum(nsteps(1:kg).*[kg:-1:1])/sum([kg:-1:1]);
if kk>des
if kg==1
h=h*(kk-des+1);
else
h=h*sqrt(kk-des+1);
end
elseif kk<des
h=h*sqrt(kk/des);
end
stepvanish=stepvanish+ksm;
% ----}
% COMPUTE THE GRADIENT ----{
if app
deltax=sign(g0); idx=find(deltax==0);
deltax(idx)=ones(size(idx)); deltax=h1*ddx*deltax;
if constr
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,f,fun,deltax ,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1
g=g'
end
ng=norm(g);
if isnan(ng)
if dispwarn
disp(errmes)
disp(error41)
end
exitflag=-4;
if trx
x=x';
end
return
elseif ng==Inf
if dispwarn
disp(errmes)
disp(error42)
end
exitflag=-4;
if trx
x=x';
end
return
elseif ng<ZeroGrad
if dispwarn
disp(wrnmes)
disp(warn1)
end
ng=ZeroGrad;
end
% Constraints:
if constr
if ~FP
if ng<.01*PenCoef
kless=kless+1;
if kless>=20
PenCoef=PenCoef/10;
Reset=1;
kless=0;
end
else
kless=0;
end
if appconstr
deltax=sign(x); idx=find(deltax==0);
deltax(idx)=ones(size(idx)); deltax=ddx*deltax;
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
gc=feval(gradc,x );
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
if size(gc,2)==1
gc=gc';
end
ngc=norm(gc);
if isnan(ngc)
if dispwarn
disp(errmes)
disp(error61)
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc==Inf
if dispwarn
disp(errmes)
disp(error62)
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc<ZeroGrad && ~appconstr
if dispwarn
disp(errmes)
disp(error63)
end
exitflag=-6;
if trx
x=x';
end
return
end
g=g+PenCoef*gc; ng=norm(g);
if Reset
if dispwarn
disp(wrnmes)
disp(warn21)
end
h=h1*dx/3; k=k-1; nng=ng; break
end
end
end
if h1*f>h1*frec
frec=f;
xrec=x;
grec=g;
end
% ----}
if ng>ZeroGrad
if knorms<10
knorms=knorms+1;
end
if knorms>=2
gnorms(2:knorms)=gnorms(1:knorms-1);
end
gnorms(1)=ng;
nng=(prod(gnorms(1:knorms)))^(1/knorms);
end
% DISPLAY THE CURRENT VALUES ----{
if k==ld
disp('Iter.# ..... Function ... Step Value ... Gradient Norm ');
disp(sprintf('%5i %13.5e %13.5e %13.5e',k,f,dx,ng));
ld=k+dispdata;
end
%----}
% CHECK THE STOPPING CRITERIA ----{
termflag=1;
if constr
if ~FP
termflag=0;
end
end
if kcheck<=5
termflag=0;
end
if knan
termflag=0
end
if kc>=mxtc
termflag=0;
end
% ARGUMENT
if termflag
idx=find(abs(x)>=lowxbound);
if isempty(idx) || all(abs(xopt(idx)-x(idx))<=optim.TolX*abs(x(idx)))
termx=termx+1;
% FUNCTION
if abs(f-frec)> detfr * abs(f) && ...
abs(f-fopt)<=optim.TolFun*abs(f) && ...
krerun<=3 && ...
~constr
if any(abs(xrec(idx)-x(idx))> detxr * abs(x(idx)))
if dispwarn
disp(wrnmes)
disp(warn09)
end
x=xrec;
f=frec;
g=grec;
ng=norm(g);
krerun=krerun+1;
h=h1*max([dx,detxr*norm(x)])/krerun;
warnno=2;
break
else
h=h*10;
end
elseif abs(f-frec)> optim.TolFun*abs(f) && ...
norm(x-xrec)<optim.TolX*norm(x) && constr
elseif abs(f-fopt)<=optim.TolFun*abs(f) || ...
abs(f)<=lowfbound || ...
(abs(f-fopt)<=optim.TolFun && termx>=limxterm )
if stopf
if dx<=laststep
if warnno==1 && ng<sqrt(optim.TolFun)
warnno=0;
end
if ~app
if any(abs(g)<=epsnorm2)
warnno=3;
end
end
if warnno~=0
exitflag=-warnno-10;
if dispwarn, disp(termwarn1)
disp(endwarn(warnno,:))
if app
disp(appwarn);
end
end
else
exitflag=k;
if dispwarn
disp(termwarn0);
end
end
if trx
x=x';
end
return
end
else
stopf=1;
end
elseif dx<1.e-12*max(norm(x),1) && termx>=limxterm
exitflag=-14;
if dispwarn
disp(termwarn1)
disp(endwarn(4,:))
if app
disp(appwarn)
end
end
x=xrec; f=frec;
if trx
x=x';
end
return
else
stopf=0;
end
end
end
% ITERATIONS LIMIT
if(k==optim.MaxIter)
exitflag=-9;
if trx
x=x';
end
if dispwarn
disp(wrnmes)
disp(warn4)
end
return
end
% ----}
% ZERO GRADIENT ----{
if constr
if ng<=ZeroGrad
if dispwarn
disp(termwarn1)
disp(warn1)
end
exitflag=-8;
if trx
x=x';
end
return
end
else
if ng<=ZeroGrad
nzero=nzero+1;
if dispwarn
disp(wrnmes)
disp(warn1)
end
if nzero>=3
exitflag=-8;
if trx
x=x';
end
return
end
g0=-h*g0/2;
for i=1:10
x=x+g0;
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f)==Inf
if dispwarn
disp(errmes)
disp(error32)
end
exitflag=-3;
if trx
x=x';
end
return
elseif isnan(f)
if dispwarn
disp(errmes)
disp(error32)
end
exitflag=-3;
if trx
x=x';
end
return
end
if app
deltax=sign(g0);
idx=find(deltax==0);
deltax(idx)=ones(size(idx));
deltax=h1*ddx*deltax;
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x,f,fun,deltax,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1
g=g';
end
ng=norm(g);
if ng==Inf
if dispwarn
disp(errmes)
disp(error42)
end
exitflag=-4;
if trx
x=x';
end
return
elseif isnan(ng)
if dispwarn
disp(errmes)
disp(error41)
end
exitflag=-4;
if trx
x=x';
end
return
end
if ng>ZeroGrad
break
end
end
if ng<=ZeroGrad
if dispwarn
disp(termwarn1)
disp(warn1)
end
exitflag=-8;
if trx
x=x';
end
return
end
h=h1*dx;
break
end
end
% ----}
% FUNCTION IS FLAT AT THE POINT ----{
if ~constr && abs(f-fopt)<abs(fopt)*optim.TolFun && kcheck>5 && ng<1
idx=find(abs(g)<=epsnorm2);
ni=size(idx,2);
if ni>=1 && ni<=n/2 && kflat<=3
kflat=kflat+1;
if dispwarn
disp(wrnmes)
disp(warn31)
end
warnno=1;
x1=x; fm=f;
for j=idx
y=x(j); f2=fm;
if y==0
x1(j)=1;
elseif abs(y)<1
x1(j)=sign(y);
else
x1(j)=y;
end
for i=1:20
x1(j)=x1(j)/1.15;
if trx
f1=feval(fun,x1',varargin{:});
else
f1=feval(fun,x1,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f1)~=Inf && ~isnan(f1)
if h1*f1>h1*fm
y=x1(j);
fm=f1;
elseif h1*f2>h1*f1
break
elseif f2==f1
x1(j)=x1(j)/1.5;
end
f2=f1;
end
end
x1(j)=y;
end
if h1*fm>h1*f
if app
deltax=h1*ddx*ones(size(deltax));
if trx
gt=apprgrdn(x1',fm,fun,deltax',1,varargin{:});
else
gt=apprgrdn(x1 ,fm,fun,deltax ,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
if trx
gt=feval(grad,x1',varargin{:});
else
gt=feval(grad,x1,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(gt,2)==1
gt=gt';
end
ngt=norm(gt);
if ~isnan(ngt) && ngt>epsnorm2
if dispwarn
disp(warn32)
end
optim.TolFun=optim.TolFun/5;
x=x1;
g=gt;
ng=ngt;
f=fm;
h=h1*dx/3;
break
end
end
end
end
% ----}
end % iterations
end % restart
% end of the function
%
end
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