108 lines
4.3 KiB
Mathematica
108 lines
4.3 KiB
Mathematica
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function p = preprocess_bilinear_bounds(p)
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if ~isempty(p.integer_variables)
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for i = 1:size(p.bilinears,1)
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if ismember(p.bilinears(i,2),p.integer_variables)
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if ismember(p.bilinears(i,3),p.integer_variables)
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p.integer_variables = [p.integer_variables p.bilinears(i,1)];
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end
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end
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end
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if p.K.f > 0
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for i = 1:p.K.f
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if all(ismember(p.F_struc(i,:),[0 1 -1]))
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involved = find(p.F_struc(i,2:end));
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% One variable is linear combination of integer variables
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if (nnz(ismember(involved,p.integer_variables)) == length(involved)-1) & length(involved)>1
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p.integer_variables = [p.integer_variables involved];
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end
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end
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end
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p.integer_variables = unique(p.integer_variables );
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end
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end
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if ~isempty(p.binary_variables)
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for i = 1:size(p.bilinears,1)
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if ismember(p.bilinears(i,2),p.binary_variables)
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if ismember(p.bilinears(i,3),p.binary_variables)
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p.binary_variables = [p.binary_variables p.bilinears(i,1)];
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end
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end
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end
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for i = 1:p.K.f
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if all(p.F_struc(i,:) == fix(p.F_struc(i,:)))
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involved = find(p.F_struc(i,2:end));
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% One variable is linear combination of binary variables
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if (nnz(ismember(involved,p.binary_variables)) == length(involved)-1) & length(involved)>1
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p.integer_variables = [p.integer_variables involved];
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end
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end
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end
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p.binary_variables = unique(p.binary_variables );
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end
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if isempty(p.ub)
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p.ub = repmat(inf,length(p.c),1);
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end
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if isempty(p.lb)
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p.lb = repmat(-inf,length(p.c),1);
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end
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if ~isempty(p.F_struc)
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[lb,ub,used_rows_eq,used_rows_lp] = findulb(p.F_struc,p.K);
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if ~isempty([used_rows_eq;used_rows_lp])
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lower_defined = find(~isinf(lb));
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if ~isempty(lower_defined)
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p.lb(lower_defined) = max(p.lb(lower_defined),lb(lower_defined));
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end
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upper_defined = find(~isinf(ub));
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if ~isempty(upper_defined)
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p.ub(upper_defined) = min(p.ub(upper_defined),ub(upper_defined));
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end
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% Remove linear bound inequalities
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if ~isempty(used_rows_lp)
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used_rows_lp = used_rows_lp(find(~any(p.F_struc(p.K.f+used_rows_lp,1+p.nonlinears),2)));
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not_used_rows = setdiff(1:p.K.l,used_rows_lp);
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newKCutl = [];
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for i = 1:length(p.KCut.l)
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newKCutl = [newKCutl find(not_used_rows==p.KCut.l(i))];
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% p.KCut.l(i) = find(not_used_rows == p.KCut.l(i));
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% p.originalModel.KCut.l(i) = find(not_used_rows == p.originalModel.KCut.l(i) );
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end
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p.KCut.l = newKCutl;
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if ~isempty(used_rows_lp)
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p.F_struc(p.K.f+used_rows_lp,:)=[];
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% p.originalModel.F_struc(p.originalModel.K.f+used_rows_lp,:)=[];
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p.K.l = p.K.l - length(used_rows_lp);
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% p.originalModel.K.l = p.originalModel.K.l - length(used_rows_lp);
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end
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end
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% Remove linear bound inequalities
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if ~isempty(used_rows_eq)
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used_rows_eq = used_rows_eq(find(~any(p.F_struc(used_rows_eq,1+p.nonlinears),2)));
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not_used_rows = setdiff(1:p.K.f,used_rows_eq);
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newKCutf = [];
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for i = 1:length(p.KCut.f)
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newKCutf = [newKCutf find(not_used_rows==p.KCut.f(i))];
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% p.KCut.f(i) = find(not_used_rows==p.KCut.f(i));
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% p.originalModel.KCut.f(i) = find(not_used_rows==p.originalModel.KCut.f(i));
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end
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p.KCut.f = newKCutf;
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if ~isempty(used_rows_eq)
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p.F_struc(used_rows_eq,:)=[];
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% p.originalModel.F_struc(used_rows_eq,:)=[];
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p.K.f = p.K.f - length(used_rows_eq);
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% p.originalModel.K.f = p.originalModel.K.f - length(used_rows_eq);
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end
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end
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end
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end
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p.lb(p.binary_variables) = max(0,p.lb(p.binary_variables));
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p.ub(p.binary_variables) = min(1,p.ub(p.binary_variables));
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p.lb(p.integer_variables) = ceil(p.lb(p.integer_variables));
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p.ub(p.integer_variables) = floor(p.ub(p.integer_variables));
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p = clean_bounds(p);
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if ~isempty(p.bilinears)
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p = updatemonomialbounds(p);
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end
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