fixanddive.m
function fixanddive(filename) % % Copyright 2021, Gurobi Optimization, LLC % % Implement a simple MIP heuristic. Relax the model, % sort variables based on fractionality, and fix the 25% of % the fractional variables that are closest to integer variables. % Repeat until either the relaxation is integer feasible or % linearly infeasible. % Read model fprintf('Reading model %s\n', filename); model = gurobi_read(filename); cols = size(model.A, 2); ivars = find(model.vtype ~= 'C'); if length(ivars) <= 0 fprintf('All variables of the model are continuous, nothing to do\n'); return; end % save vtype and set all variables to continuous vtype = model.vtype; model.vtype = repmat('C', cols, 1); params.OutputFlag = 0; result = gurobi(model, params); % Perform multiple iterations. In each iteration, identify the first % quartile of integer variables that are closest to an integer value % in the relaxation, fix them to the nearest integer, and repeat. frac = zeros(cols, 1); for iter = 1:1000 % See if status is optimal if ~strcmp(result.status, 'OPTIMAL') fprintf('Model status is %s\n', result.status); fprintf('Can not keep fixing variables\n'); break; end % collect fractionality of integer variables fracs = 0; for j = 1:cols if vtype(j) == 'C' frac(j) = 1; % indicating not integer variable else t = result.x(j); t = t - floor(t); if t > 0.5 t = t - 0.5; end if t > 1e-5 frac(j) = t; fracs = fracs + 1; else frac(j) = 1; % indicating not fractional end end end fprintf('Iteration %d, obj %g, fractional %d\n', iter, result.objval, fracs); if fracs == 0 fprintf('Found feasible solution - objective %g\n', result.objval); break; end % sort variables based on fractionality [~, I] = sort(frac); % fix the first quartile to the nearest integer value nfix = max(fracs/4, 1); for i = 1:nfix j = I(i); t = floor(result.x(j) + 0.5); model.lb(j) = t; model.ub(j) = t; end % use warm start basis and reoptimize model.vbasis = result.vbasis; model.cbasis = result.cbasis; result = gurobi(model, params); end
