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### sensitivity.m

function sensitivity(filename)
% Copyright 2019, 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.

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


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