📄 sort.m
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function varargout=sort(varargin)
%SORT (overloaded)
%
% [t,loc] = sort(x)
%
% The variable t will be the sorted version of x.
%
% SORT is implemented in the nonlinear operator framework using a big-M
% model.
% Author Johan L鰂berg
% $Id: sort.m,v 1.6 2006/09/01 09:46:03 joloef Exp $
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/SORT CALLED WITH DOUBLE. Report error')
case 'sdpvar' % Overloaded operator for SDPVAR objects. Pass on args and save them.
if nargin > 1 | min(size(varargin{1}))>1
error('SDPVAR/SORT only supports simple 1-D sorting'),
end
x = varargin{1};
data.D = binvar(length(x),length(x),'full');
data.V = sdpvar(length(x),length(x),'full');
y = [];
for i = 1:length(x)
data.i = i;
data.isthisloc = 0;
y = [y;yalmip('addextendedvariable',mfilename,x,data)];%i,P,V)];
end
loc = [];
for i = 1:length(x)
data.i = i;
data.isthisloc = 1;
loc = [loc;yalmip('addextendedvariable',mfilename,x,data)];
end
varargout{1} = y;
varargout{2} = loc;
case 'char' % YALMIP send 'graph' when it wants the epigraph or hypograph
switch varargin{1}
case {'milp','graph'}
% Description using epigraphs
t = varargin{2};
X = varargin{3};
data = varargin{4};
% Call external to allow subsrefs in classs
[F,vars] = sort_internal(t,X,data);
varargout{1} = F;
varargout{2} = struct('convexity','milp','monotonicity','milp','definiteness','milp');
varargout{3} = X;
% Inofficial way to model several nonlinear variables in
% one call
varargout{2}.models = vars;
otherwise
error('SDPVAR/SORT called with CHAR argument?');
end
otherwise
error('Strange type on first argument in SDPVAR/SORT');
end
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