updating lmmcp.m from RECS
Michel Juillard
parent
1d9aee20f2
commit
214610be1e
|
|
@ -22,9 +22,10 @@ function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
|
|||
% preprocess : activate preprocessor for phase I (default = 1)
|
||||
% presteps : number of iterations in phase I (default = 20)
|
||||
% Termination parameters
|
||||
% epsilon2 : termination value of the merit function (default = 1E-16)
|
||||
% MaxIter : maximum number of iterations (default = 500)
|
||||
% MaxIter : Maximum number of iterations (default = 500)
|
||||
% tmin : safeguard stepsize (default = 1E-12)
|
||||
% TolFun : Termination tolerance on the function value, a positive
|
||||
% scalar (default = sqrt(eps))
|
||||
% Stepsize parameters
|
||||
% m : number of previous function values to use in the nonmonotone
|
||||
% line search rule (default = 10)
|
||||
|
|
@ -85,7 +86,7 @@ function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
|
|||
% e-mail: kanzow@mathematik.uni-wuerzburg.de
|
||||
% petra@mathematik.uni-wuerzburg.de
|
||||
|
||||
% ------------Initialization----------------
|
||||
%% Initialization
|
||||
defaultopt = struct(...
|
||||
'beta', 0.55,...
|
||||
'Big', 1e10,...
|
||||
|
|
@ -93,7 +94,6 @@ defaultopt = struct(...
|
|||
'deltamin', 1,...
|
||||
'Display', 'none',...
|
||||
'epsilon1', 1e-6,...
|
||||
'epsilon2', 1e-16,...
|
||||
'eta', 0.95,...
|
||||
'kwatch', 20,...
|
||||
'lambda1', 0.1,...
|
||||
|
|
@ -106,6 +106,7 @@ defaultopt = struct(...
|
|||
'sigma1', 0.5,...
|
||||
'sigma2', 2,...
|
||||
'tmin', 1e-12,...
|
||||
'TolFun', sqrt(eps),...
|
||||
'watchdog', 1);
|
||||
|
||||
if nargin < 4
|
||||
|
|
@ -122,6 +123,7 @@ else
|
|||
options = catstruct(defaultopt,options);
|
||||
end
|
||||
|
||||
warning('off','MATLAB:rankDeficientMatrix')
|
||||
|
||||
switch options.Display
|
||||
case {'off','none'}
|
||||
|
|
@ -136,7 +138,7 @@ end
|
|||
|
||||
% parameter settings
|
||||
eps1 = options.epsilon1;
|
||||
eps2 = options.epsilon2;
|
||||
eps2 = 0.5*options.TolFun^2;
|
||||
null = options.null;
|
||||
Big = options.Big;
|
||||
|
||||
|
|
@ -219,7 +221,7 @@ if watchdog==1
|
|||
DPhibest = DPhix;
|
||||
DPsibest = DPsix;
|
||||
normDPsibest = normDPsix;
|
||||
end;
|
||||
end
|
||||
|
||||
% initial output
|
||||
if verbosity > 1
|
||||
|
|
@ -229,9 +231,7 @@ if verbosity > 1
|
|||
fprintf('%4.0f %24.5e %24.5e\n',k,Psix,normDPsix);
|
||||
end
|
||||
|
||||
%
|
||||
% Preprocessor using local method
|
||||
%
|
||||
%% Preprocessor using local method
|
||||
|
||||
if preprocess==1
|
||||
|
||||
|
|
@ -247,17 +247,17 @@ if preprocess==1
|
|||
% the condition estimator for large-scale problems, although this
|
||||
% may cause numerical problems in some examples
|
||||
|
||||
i=0;
|
||||
i = false;
|
||||
mu = 0;
|
||||
if n<100
|
||||
i=1;
|
||||
i = true;
|
||||
mu = 1e-16;
|
||||
if condest(DPhix'*DPhix)>1e25
|
||||
mu = 1e-6/(k+1);
|
||||
end
|
||||
end
|
||||
if i==1
|
||||
pLM= [ DPhix ; sqrt(mu)*speye(n)]\[-Phix;sparse(n,1)];
|
||||
if i
|
||||
pLM = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
|
||||
else
|
||||
pLM = -DPhix\Phix;
|
||||
end
|
||||
|
|
@ -326,9 +326,7 @@ elseif preprocess==1 && Psix>=eps2
|
|||
end
|
||||
end
|
||||
|
||||
%
|
||||
% Main algorithm
|
||||
%
|
||||
%% Main algorithm
|
||||
|
||||
if verbosity > 1
|
||||
disp('************************** Main program ****************************')
|
||||
|
|
@ -340,9 +338,9 @@ while (k < kmax) && (Psix > eps2)
|
|||
% the condition estimator for large-scale problems, although this
|
||||
% may cause numerical problems in some examples
|
||||
|
||||
i=0;
|
||||
i = false;
|
||||
if n<100
|
||||
i=1;
|
||||
i = true;
|
||||
mu = 1e-16;
|
||||
if condest(DPhix'*DPhix)>1e25
|
||||
mu = 1e-1/(k+1);
|
||||
|
|
@ -351,8 +349,8 @@ while (k < kmax) && (Psix > eps2)
|
|||
|
||||
% compute a Levenberg-Marquard direction
|
||||
|
||||
if i==1
|
||||
d= [ DPhix ; sqrt(mu)*eye(n)]\[-Phix;zeros(n,1)];
|
||||
if i
|
||||
d = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
|
||||
else
|
||||
d = -DPhix\Phix;
|
||||
end
|
||||
|
|
@ -396,7 +394,7 @@ while (k < kmax) && (Psix > eps2)
|
|||
else
|
||||
aux(mod(k_main,m)+1) = Psix;
|
||||
MaxPsi = max(aux);
|
||||
end;
|
||||
end
|
||||
|
||||
% updatings for the watchdog strategy
|
||||
if watchdog ==1
|
||||
|
|
@ -416,16 +414,16 @@ while (k < kmax) && (Psix > eps2)
|
|||
DPsix=DPsibest;
|
||||
normDPsix=normDPsibest;
|
||||
MaxPsi=Psix;
|
||||
end;
|
||||
end;
|
||||
end
|
||||
end
|
||||
|
||||
if verbosity > 1
|
||||
% output at each iteration
|
||||
fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
|
||||
end
|
||||
end;
|
||||
end
|
||||
|
||||
% final output
|
||||
%% Final output
|
||||
if Psix<=eps2
|
||||
EXITFLAG = 1;
|
||||
if verbosity > 0, disp('Approximate solution found.'); end
|
||||
|
|
@ -446,9 +444,10 @@ OUTPUT.Psix = Psix;
|
|||
OUTPUT.normDPsix = normDPsix;
|
||||
JACOB = DFx;
|
||||
|
||||
% Subfunctions
|
||||
%% Subfunctions
|
||||
|
||||
function y = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset)
|
||||
%% PHI
|
||||
|
||||
y = zeros(2*n,1);
|
||||
phi_u = sqrt((ub-x).^2+Fx.^2)-ub+x+Fx;
|
||||
|
|
@ -472,7 +471,7 @@ y([LZ; I3]) = lambda2*(max(0,x(I3)-lb(I3)).*max(0,Fx(I3))+max(0,ub(I3)-x(I3)).*m
|
|||
|
||||
|
||||
function H = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset)
|
||||
% DPHI evaluates an element of the C-subdifferential of operator Phi
|
||||
%% DPHI evaluates an element of the C-subdifferential of operator Phi
|
||||
|
||||
null = 1e-8;
|
||||
beta_l = zeros(n,1);
|
||||
|
|
@ -527,8 +526,8 @@ end
|
|||
|
||||
I1a = I(Indexset==1 & alpha_l==1);
|
||||
if any(I1a)
|
||||
H2(I1a,:) = repmat(x(I1a)-lb(I1a),1,n).*DFx(I1a,:)+sparse(1:length(I1a),I1a, ...
|
||||
Fx(I1a),length(I1a),n,length(I1a));
|
||||
H2(I1a,:) = bsxfun(@times,x(I1a)-lb(I1a),DFx(I1a,:))+...
|
||||
sparse(1:length(I1a),I1a,Fx(I1a),length(I1a),n,length(I1a));
|
||||
end
|
||||
|
||||
I2 = Indexset==2;
|
||||
|
|
@ -550,8 +549,8 @@ end
|
|||
|
||||
I2a = I(Indexset==2 & alpha_u==1);
|
||||
if any(I2a)
|
||||
H2(I2a,:) = repmat(x(I2a)-ub(I2a),1,n).*DFx(I2a,:)+sparse(1:length(I2a),I2a, ...
|
||||
Fx(I2a),length(I2a),n,length(I2a));
|
||||
H2(I2a,:) = bsxfun(@times,x(I2a)-ub(I2a),DFx(I2a,:))+...
|
||||
sparse(1:length(I2a),I2a,Fx(I2a),length(I2a),n,length(I2a));
|
||||
end
|
||||
|
||||
I3 = Indexset==3;
|
||||
|
|
@ -595,21 +594,21 @@ Db(I3) = bi(I3).*di(I3);
|
|||
|
||||
I3a = I(Indexset==3 & alpha_l==1 & alpha_u==1);
|
||||
if any(I3a)
|
||||
H2(I3a,:) = repmat(-lb(I3a)-ub(I3a)+2*x(I3a),1,n).*DFx(I3a,:)+...
|
||||
H2(I3a,:) = bsxfun(@times,-lb(I3a)-ub(I3a)+2*x(I3a),DFx(I3a,:))+...
|
||||
2*sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
|
||||
end
|
||||
I3a = I(Indexset==3 & alpha_l==1 & alpha_u~=1);
|
||||
if any(I3a)
|
||||
H2(I3a,:) = repmat(x(I3a)-lb(I3a),1,n).*DFx(I3a,:)+...
|
||||
H2(I3a,:) = bsxfun(@times,x(I3a)-lb(I3a),DFx(I3a,:))+...
|
||||
sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
|
||||
end
|
||||
I3a = I(Indexset==3 & alpha_l~=1 & alpha_u==1);
|
||||
if any(I3a)
|
||||
H2(I3a,:) = repmat(x(I3a)-ub(I3a),1,n).*DFx(I3a,:)+...
|
||||
H2(I3a,:) = bsxfun(@times,x(I3a)-ub(I3a),DFx(I3a,:))+...
|
||||
sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
|
||||
end
|
||||
|
||||
%H1 = sparse(1:n,1:n,Da,n,n,n)+Db(:,ones(n,1)).*DFx;
|
||||
H1 = bsxfun(@times,Db,DFx);
|
||||
H1 = spdiags(diag(H1)+Da,0,H1);
|
||||
|
||||
H = [lambda1*H1; lambda2*H2];
|
||||
|
|
|
|||
Loading…
Reference in New Issue