116 lines
2.8 KiB
Mathematica
116 lines
2.8 KiB
Mathematica
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function Z = replace(X,Y,W,expand)
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%REPLACE Substitutes variables
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%
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%Z = REPLACE(Y,X,W) Replaces any occurence of the SDPVAR object Y
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% in the SDPVAR object X with the double W
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%
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% Example
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% x = sdpvar(1,1);
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% t = sdpvar(1,1);
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% Y = [1+t;1+x+t];
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% Y = replace(Y,x,2) generates Y=[1+t;3+t]
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% if nargin<4
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% expand = 1;
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% end
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%
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% if ~isa(X,'sdpvar')
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% Z = X;
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% return
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% end
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% if ~isa(Y,'sdpvar')
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% error('Second arguments must be an sdpvar object')
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% end
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%
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% if ~is(Y,'linear')
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% error('Second arguments must be linear')
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% end
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%
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% if prod(size(W)) == 1
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% W = repmat(W,size(Y));
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% end
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%
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% if ~isequal(size(Y),size(W))
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% if isequal(fliplr(size(Y)),size(W))
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% W = W';
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% else
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% error('Both arguments must have same size')
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% end
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% end
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% if isa(W,'sdpvar')
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% % This is tricky...
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% Z = variable_replace(X,Y,W);
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% return
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% end
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%
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% if ~isa(W,'double')
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% error('Third arguments must be a double')
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% end
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% Replace with NaN destroys everything, assume it should be cleared
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W(isnan(W)) = 0;
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y_lmi_variables = Y.lmi_variables;
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% b = W(:)-Y.basis(:,1);
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% A = Y.basis(:,2:end);
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% feas_var = A\b;
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% if norm(A*feas_var-b)>sqrt(eps)
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% error('Inconsistent assignment')
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% end
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x_lmi_variables = X.lmi_variables;
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n = X.dim(1);
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m = X.dim(2);
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[monomtable,variabletype] = yalmip('monomtable');
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if all(variabletype(x_lmi_variables)==0) % is(X,'linear')
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Z = X.basis(:,1);
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for i = 1:length(x_lmi_variables)
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j = find(x_lmi_variables(i) == y_lmi_variables);
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if isempty(j)
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Z = Z + recover(x_lmi_variables(i))*X.basis(:,i+1);
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else
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Z = Z + feas_var(j)*X.basis(:,i+1);
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end
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end
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else
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replaced_vars = depends(Y);
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% used_variables = getvariables(X);
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used_variables = x_lmi_variables;
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% monomtable = yalmip('monomtable');
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local_monom = monomtable(used_variables,replaced_vars);
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Wall = W;
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Z = [];
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local_monoms_left = monomtable(used_variables,:);
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local_monoms_left(:,replaced_vars) = 0;
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used_left = find(sum(local_monoms_left,1));
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base = recovermonoms(local_monoms_left(:,used_left),recover(used_left));
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for rep = 1:size(Wall,2)
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W = Wall(:,rep);
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W = W(:)';
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gain = zeros(length(used_variables),1);
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for i = 1:length(used_variables)
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% F**N 6.5 0^sparse(0) and 0^0 differ
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gain(i) = prod(W.^full(local_monom(i,:)));
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end
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base = base.*gain(:);
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Ztemp = X.basis(:,1);
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Ztemp = Ztemp + X.basis(:,2:end)*base;
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Z = [Z;Ztemp];
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end
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end
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if isa(Z,'sdpvar')
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Z.dim(1) = n*size(Wall,2);
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Z.dim(2) = m;
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Z.typeflag = X.typeflag;
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% Reset info about conic terms
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Z.conicinfo = [0 0];
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else
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Z = reshape(full(Z),n,m);
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end
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