sensitivity.m
function sensitivity(filename) % Copyright 2021, Gurobi Optimization, LLC % % A simple sensitivity analysis example which reads a MIP model % from a file and solves it. Then each binary variable is set % to 1-X, where X is its value in the optimal solution, and % the impact on the objective function value is reported. % 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 % Optimize result = gurobi(model); % Capture solution information if result.status ~= 'OPTIMAL' fprintf('Model status is %d, quit now\n', result.status); end origx = result.x; origobjval = result.objval; params.OutputFlag = 0; % Iterate through unfixed binary variables in the model for j = 1:cols if model.vtype(j) ~= 'B' && model.vtype(j) ~= 'I' continue; end if model.vtype(j) == 'I' if model.lb(j) ~= 0.0 || model.ub(j) ~= 1.0 continue; end else if model.lb(j) > 0.0 || model.ub(j) < 1.0 continue; end end % Update MIP start for all variables model.start = origx; % Set variable to 1-X, where X is its value in optimal solution if origx(j) < 0.5 model.start(j) = 1; model.lb(j) = 1; else model.start(j) = 0; model.ub(j) = 0; end % Optimize result = gurobi(model, params); % Display result if ~strcmp(result.status, 'OPTIMAL') gap = inf; else gap = result.objval - origobjval; end fprintf('Objective sensitivity for variable %s is %g\n', ... model.varnames{j}, gap); % Restore original bounds model.lb(j) = 0; model.ub(j) = 1; end
