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mip2.m
function mip2(filename) % Copyright 2022, Gurobi Optimization, LLC % % This example reads a MIP model from a file, solves it and prints % the objective values from all feasible solutions generated while % solving the MIP. Then it creates the associated fixed model and % solves that model. % Read model fprintf('Reading model %s\n', filename); model = gurobi_read(filename); cols = size(model.A, 2); ivars = find(model.vtype ~= 'C'); ints = length(ivars); if ints <= 0 fprintf('All variables of the model are continuous, nothing to do\n'); return; end % Optimize params.poolsolutions = 20; result = gurobi(model, params); % Capture solution information if ~strcmp(result.status, 'OPTIMAL') fprintf('This model cannot be solved because its optimization status is %s\n', ... result.status); return; end % Iterate over the solutions if isfield(result, 'pool') && ~isempty(result.pool) solcount = length(result.pool); for k = 1:solcount fprintf('Solution %d has objective %g\n', k, result.pool(k).objval); end else fprintf('Solution 1 has objective %g\n', result.objval); end % Convert to fixed model for j = 1:cols if model.vtype(j) ~= 'C' t = floor(result.x(j) + 0.5); model.lb(j) = t; model.ub(j) = t; end end % Solve the fixed model result2 = gurobi(model, params); if ~strcmp(result.status, 'OPTIMAL') fprintf('Error: fixed model is not optimal\n'); return; end if abs(result.objval - result2.objval) > 1e-6 * (1 + abs(result.objval)) fprintf('Error: Objective values differ\n'); end % Print values of non-zero variables for j = 1:cols if abs(result2.x(j)) > 1e-6 fprintf('%s %g\n', model.varnames{j}, result2.x(j)); end end