105 lines
3.6 KiB
Mathematica
105 lines
3.6 KiB
Mathematica
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function Fderandomized = derandomize(F)
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chanceDeclarations = find(is(F,'chance'));
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randomDeclarations = find(is(F,'random'));
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if isempty(randomDeclarations)
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error('Cannot derandomize, cannot find declaration of random variables');
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end
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keep = ones(length(F),1);
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keep(randomDeclarations)=0;
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keep(chanceDeclarations)=0;
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randomVariables = extractRandomDefinitions(F(randomDeclarations));
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groupedChanceConstraints = groupchanceconstraints(F);
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Fderandomized = deriveChanceModel(groupedChanceConstraints,randomVariables)
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Fderandomized = Fderandomized + F(find(keep));
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function Fderandomized = deriveChanceModel(groupedChanceConstraints,randomVariables);
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Fderandomized = [];
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for ic = 1:length(groupedChanceConstraints)
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if length(groupedChanceConstraints{ic})>1
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error('Joint chance still not supported');
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end
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if ~is(groupedChanceConstraints{ic},'elementwise')
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error('Only elementwise chance constraints supported')
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end
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X = sdpvar(groupedChanceConstraints{ic});
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if length(X)>1
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error('Only single elementwise chance constraints supported')
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end
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% OK, simple linear inequality
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allVars = depends(X);
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wVars = [];for j = 1:length(randomVariables);wVars = [wVars getvariables(randomVariables{j}.variables)];end
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xVars = setdiff(allVars,wVars);
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x = recover(xVars);
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w = recover(wVars);
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b = [];
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A = [];
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% Some pre-calc
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xw = [x;w];
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xind = find(ismembc(getvariables(xw),getvariables(x)));
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wind = find(ismembc(getvariables(xw),getvariables(w)));
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[Qs,cs,fs,dummy,nonquadratic] = vecquaddecomp(X,xw);
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c_wTbase = [];
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AAA = [];
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ccc = [];
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for i = 1:length(X)
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Q = Qs{i};
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c = cs{i};
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f = fs{i};
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if nonquadratic
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error('Constraints can be at most quadratic, with the linear term uncertain');
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end
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Q_ww = Q(wind,wind);
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Q_xw = Q(xind,wind);
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Q_xx = Q(xind,xind);
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c_x = c(xind);
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c_w = c(wind);
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%b = [b;f + c_w'*w];
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%A = [A;-c_x'-w'*2*Q_xw'];
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% A = [A -c_x-2*Q_xw*w];
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AAA = [AAA;sparse(-2*Q_xw)];
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ccc = [ccc;-sparse(c_x)];
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b = [b;f];
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c_wTbase = [c_wTbase;c_w'];
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end
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% b = b + c_wTbase*w;
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% A = reshape(ccc + AAA*w,size(c_x,1),[]);
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j = 1;
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confidencelevel = struct(groupedChanceConstraints{ic}).clauses{1}.confidencelevel;
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theMean = randomVariables{j}.distribution.parameters{1};
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covariance = randomVariables{j}.distribution.parameters{2};
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gamma = icdf('normal',confidencelevel,0,1);
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switch randomVariables{j}.distribution.name
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case 'normal'
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theMean = randomVariables{j}.distribution.parameters{1};
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covariance = randomVariables{j}.distribution.parameters{2};
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gamma = icdf('normal',confidencelevel,0,1);
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if isa(covariance,'sdpvar')
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error('Covariance cannot be an SDPVAR in normal distribution. Maybe you meant to use factorized covariance in ''normalf''');
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end
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Fderandomized = [Fderandomized, b + c_wTbase*theMean - (ccc + AAA*theMean)'*x >= gamma*norm(chol(covariance)*(AAA'*x+c_wTbase'))];
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case 'normalf'
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theMean = randomVariables{j}.distribution.parameters{1};
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covariance = randomVariables{j}.distribution.parameters{2};
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gamma = icdf('normal',confidencelevel,0,1);
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Fderandomized = [Fderandomized, b + c_wTbase*theMean - (ccc + AAA*theMean)'*x >= gamma*norm(covariance*(AAA'*x+c_wTbase'))];
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otherwise
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error('Distribution not supported');
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end
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end
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