Let us now turn our attention to an example of using Gurobi to solve a simple MIP model. Our example optimizes the following model:
| maximize | x | + | y | + | 2 z | ||
| subject to | x | + | 2 y | + | 3 z | 4 | |
| x | + | y | 1 | ||||
| x, y, z binary | |||||||
This is the complete source code for our example (also available in
<installdir>/examples/matlab/mip1.m)...
names = {'x'; 'y'; 'z'};
try
clear model;
model.A = sparse([1 2 3; 1 1 0]);
model.obj = [1 1 2];
model.rhs = [4; 1];
model.sense = '<>';
model.vtype = 'B';
model.modelsense = 'max';
clear params;
params.outputflag = 0;
params.resultfile = 'mip1.lp';
result = gurobi(model, params);
disp(result)
for v=1:length(names)
fprintf('%s %d\n', names{v}, result.x(v));
end
fprintf('Obj: %e\n', result.objval);
catch gurobiError
fprintf('Error reported\n');
end