Dynamic-Calibration/utils/YALMIP-master/extras/@ncvar/or.m

71 lines
2.2 KiB
Matlab
Executable File

function varargout = or(varargin)
%OR (overloaded)
%
% z = or(x,y)
% z = x | y
%
% The OR operator is implemented using the concept of nonlinear operators
% in YALMIP. X|Y defines a new so called derived variable that can be
% treated as any other variable in YALMIP. When SOLVESDP is issued,
% constraints are added to the problem to model the OR operator. The new
% constraints add constraints to ensure that z,x and y satisfy the
% truth-table for OR.
%
% The model for OR is (z>x) + (z>y) + (z<x+y) + (binary(z))
%
% It is assumed that x and y are binary variables (either explicitely
% declared using BINVAR, or constrained using BINARY.)
%
% See also SDPVAR/AND, BINVAR, BINARY
% Models OR using a nonlinear operator definition
switch class(varargin{1})
case 'char'
z = varargin{2};
x = varargin{3};
y = varargin{4};
% *******************************************************
% For *some* efficiency,we merge expressions like A|B|C|D
xvars = getvariables(x);
yvars = getvariables(y);
allextvars = yalmip('extvariables');
if (length(xvars)==1) & ismembc(xvars,allextvars)
x = expandor(x,allextvars);
end
if (length(yvars)==1) & ismembc(yvars,allextvars)
y = expandor(y,allextvars);
end
% *******************************************************
xy=[x y];
varargout{1} = (sum(xy) > z) + (z > xy) +(binary(z)) ;
varargout{2} = struct('convexity','milp','monotonicity','milp','definiteness','milp');
varargout{3} = xy;
case 'sdpvar'
x = varargin{1};
y = varargin{2};
varargout{1} = yalmip('addextendedvariable','or',varargin{:});
otherwise
end
function x = expandor(x,allextvars)
xmodel = yalmip('extstruct',getvariables(x));
if isequal(xmodel.fcn,'or')
x1 = xmodel.arg{1};
x2 = xmodel.arg{2};
if ismembc(getvariables(xmodel.arg{1}),allextvars)
x1 = expandor(xmodel.arg{1},allextvars);
end
if ismembc(getvariables(xmodel.arg{2}),allextvars)
x2 = expandor(xmodel.arg{2},allextvars);
end
x = [x1 x2];
end