140 lines
4.5 KiB
Matlab
Executable File
140 lines
4.5 KiB
Matlab
Executable File
function [p,feasible,lower] = lpbmitighten(p,lower,upper,lpsolver,xmin,improvethese)
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if nargin<6
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improvethese = ones(length(p.lb),1);
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end
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% Don't use LP to propagate variables which only enter as x + other in one
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% single (in)equality.
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ind = sum(p.F_struc(1:p.K.f+p.K.l,:) | p.F_struc(1:p.K.f+p.K.l,:),1);
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oneterm = find(ind(2:end) == 1);
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for i = 1:length(oneterm)
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a = p.F_struc(1:p.K.f+p.K.l,oneterm(i)+1);
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j = find(a);
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if j <= p.K.f
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b = p.F_struc(j,:);
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if nnz(b(2:end))==2
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improvethese(oneterm(i))=0;
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end
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end
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end
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% Construct problem with only linear terms
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% and add cuts from lower/ upper bounds
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p_test = p;
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p_test.F_struc = p.lpcuts;
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p_test.K.l = size(p.lpcuts,1);
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p_test.K.f = 0;
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p_test.K.s = 0;
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p_test.K.q = 0;
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if ~isnan(lower) & ~isinf(lower)
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p_test.F_struc = [-(p.lower-abs(p.lower)*0.01)+p.f p_test.c';p_test.F_struc];
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if p.diagonalized
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n = length(p.c)/2;
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f = p.f;
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c = p.c(1:n);
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d = p.c(n+1:end);
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neg = find(d<0);
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if length(neg)>0
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f = f + sum(d(neg).*xmin(neg).^2 - 2*d(neg).*xmin(neg).^2);
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c(neg) = c(neg) + 2*d(neg).*xmin(neg);
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d(neg) = 0;
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p_test.F_struc = [-(p.lower-abs(p.lower)*0.01)+f c' d';p_test.F_struc];
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p_test.K.l = p_test.K.l + 1;
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end
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end
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end
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if upper < inf & ~(nnz(p.c)==0 & nnz(p.Q)==0)
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if p.diagonalized
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n = length(p.c)/2;
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f = p.f;
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c = p.c(1:n);
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d = p.c(n+1:end);
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pos = find(d>0);
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if length(pos)>0
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f = f + sum(d(pos).*xmin(pos).^2 - 2*d(pos).*xmin(pos).^2);
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c(pos) = c(pos) + 2*d(pos).*xmin(pos);
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d(pos) = 0;
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p_test.F_struc = [upper+abs(upper)*0.01-f -c' -d';p_test.F_struc];
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p_test.F_struc = [upper+abs(upper)*0.01-p.f -p_test.c';p_test.F_struc];
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p_test.K.l = p_test.K.l + 2;
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end
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end
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p_test.F_struc = [upper+abs(upper)*0.01-p.f -p_test.c';p_test.F_struc];
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p_test.K.l = p_test.K.l + 1;
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end
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if p.options.bmibnb.cut.evalvariable
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p_test = addBilinearVariableCuts(p_test);
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end
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if p.options.bmibnb.cut.evalvariable
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p_test = addEvalVariableCuts(p_test);
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end
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if p.options.bmibnb.cut.multipliedequality
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p_test = addMultipliedEqualityCuts(p_test);
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end
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% Try to get rid of numerical noise (this is far from stringent, but it
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% works, and help GLPK from crashing in some instances)
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%p_test.F_struc = unique(round(p_test.F_struc*1e12)/1e12,'rows');
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p_test.K.l = size(p_test.F_struc,1);
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p_test.F_struc = [p.F_struc(1:1:p.K.f,:);p_test.F_struc];
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p_test.K.f = p.K.f;
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feasible = 1;
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% Perform reduction on most violating approximations at current solution
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if p.options.bmibnb.lpreduce ~= 1
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n = ceil(max(p.options.bmibnb.lpreduce*length(p_test.linears),1));
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res = zeros(length(p.lb),1);
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for i = 1:size(p.bilinears,1)
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res(p.bilinears(i,2)) = res(p.bilinears(i,2)) + abs( p.x0(p.bilinears(i,1))-p.x0(p.bilinears(i,2)).*p.x0(p.bilinears(i,3)));
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res(p.bilinears(i,3)) = res(p.bilinears(i,3)) + abs( p.x0(p.bilinears(i,1))-p.x0(p.bilinears(i,2)).*p.x0(p.bilinears(i,3)));
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end
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res = res(p.linears);
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[ii,jj] = sort(abs(res));
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jj = jj(end-n+1:end);
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else
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jj=1:length(p_test.linears);
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end
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j = 1;
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while feasible & j<=length(jj)
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i = p_test.linears(jj(j));
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if abs(p.ub(i)-p.lb(i)>p.options.bmibnb.vartol) & improvethese(i)
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p_test.c = eyev(length(p_test.c),i);
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output = feval(lpsolver,removenonlinearity(p_test));
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p.counter.lpsolved = p.counter.lpsolved + 1;
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if output.problem == 0 | output.problem == 2 | output.problem == 12
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if output.problem == 0
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if p_test.lb(i) < output.Primal(i)-1e-5
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p_test.lb(i) = output.Primal(i);
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p_test = updateonenonlinearbound(p_test,i);
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end
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end
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p_test.c = -p_test.c;
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output = feval(lpsolver,removenonlinearity(p_test));
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p.counter.lpsolved = p.counter.lpsolved + 1;
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if output.problem == 0
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if p_test.ub(i) > output.Primal(i)+1e-5
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p_test.ub(i) = output.Primal(i);
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p_test = updateonenonlinearbound(p_test,i);
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end
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end
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if output.problem == 1
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feasible = 0;
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end
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end
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if output.problem == 1
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feasible = 0;
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end
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end
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j = j + 1;
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end
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p_test = clean_bounds(p_test);
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p.lb = p_test.lb;
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p.ub = p_test.ub;
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% Save these for re-use
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p.evalMap = p_test.evalMap; |