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opttoolbox_mip1.m
function opttoolbox_mip1() % Copyright 2022, Gurobi Optimization, LLC % % This example uses Matlab 2017b problem based modeling feature, which % requires Optimization Toolbox, to formulate and solve the following % simple MIP model, the same model as for mip1.m % % maximize % x + y + 2 z % subject to % x + 2 y + 3 z <= 4 % x + y >= 1 % x, y, z binary % % To use Gurobi with this example, intlinprog.m must be in the current % directory or added to Matlab path x = optimvar('x', 'Type','integer','LowerBound',0,'UpperBound',1); y = optimvar('y', 'Type','integer','LowerBound',0,'UpperBound',1); z = optimvar('z', 'Type','integer','LowerBound',0,'UpperBound',1); prob = optimproblem('ObjectiveSense','maximize'); prob.Objective = x + y + 2 * z; prob.Constraints.cons1 = x + 2 * y + 3 * z <= 4; prob.Constraints.cons2 = x + y >= 1; options = optimoptions('intlinprog'); % For Matlab R2017b use the following % sol = solve(prob, options) % Syntax for R2018a and later sol = solve(prob, 'Options', options); end