73 lines
2.2 KiB
Mathematica
73 lines
2.2 KiB
Mathematica
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function x = uncertain(x,varargin)
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%UNCERTAIN Declares a variable as uncertain
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%
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% F = UNCERTAIN(W) is used to describe the set of uncertain variables
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% in an uncertain program
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%
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% INPUT
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% W : SDPVAR object or list of constraints
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% S : Optional distribution information for random uncertainty
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%
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% OUTPUT
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% F : Constraint object
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%
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% Uncertain is used to declare uncertain variables in robust
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% deterministic worst-case optimization. It can also be used to specify
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% uncertain random variables, and their associated distribution, to be
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% used in OPTIMIZER objects with the SAMPLE command.
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%
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% EXAMPLE
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%
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% Robust worst-case optimization
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%
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% sdpvar x w
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% F = [x + w <= 1], W = [-0.5 <= w <= 0.5];
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% optimize([F,W,uncertain(w)],-x)
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%
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% sdpvar x w
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% F = [x + w <= 1], W = [-0.5 <= w <= 0.5];
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% optimize([F,uncertain(W)],-x)
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%
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% To specify random uncertainties, you specify the distribution, and all
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% distribution parameters following the syntax n the RANDOM command in
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% the Statistics Toolbox
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%
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% sdpvar x w
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% F = [x + w <= 1, uncertain(w, 'uniform',0,1)];
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% P = optimizer([F,W,uncertain(w)],-x,[],w,x)
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% S = sample(P,10); % Sample ten instances and concatenate models
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% S([]) % Solve and return optimal x
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%
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% Alternatively, you can specify a function handle which generates
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% samples. YALMIP will always send a trailing argument with dimensions
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%
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% F = [x + w <= 1, uncertain(w,@mysampler,myarguments1,...)];
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%
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% The standard random case above would thus be recovered with
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%
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% F = [x + w <= 1, uncertain(w,@random,0,1)];
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%
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%
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% See also OPTIMIZE, ROBUSTMODEL, OPTIMIZER, SAMPLE
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if nargin == 1 || ((nargin == 2) && strcmpi(varargin{1},'deterministic'))
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x.typeflag = 15;
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x.extra.distribution.name = 'deterministic';
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x = lmi(x);
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else
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x.typeflag = 16;
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if isa(varargin{1},'function_handle')
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temp = {varargin{:},x.dim};
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else
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temp = {@random,varargin{:},x.dim};
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end
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x.extra.distribution.name = temp{1};
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x.extra.distribution.parameters = {temp{2:end-1}};
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try
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temp = feval(temp{:});
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catch
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disp(lasterr);
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error('Trial evaluation of attached sample generator failed.')
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end
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x = lmi(x);
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end
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