6 - Complexity of Convex Optimization with Integer Variables

Summary

This chapter presents the theory of mixed-integer convex optimization, i.e., minimizing a convex function subject to convex constraints where some of the decision variables have to take integer values. State-of-the-art results on information and algorithmic complexity of mixed-integer convex optimization are established. The basics of continuous convex optimization are presented as the special case where no variable is integer constrained.

Information complexity of classical continuous optimization has been well understood since the 1970s. The information complexity in the presence of integer variables was not well developed until research work done in the past decade and is covered in complete detail here. On the algorithmic side, the best known upper bound of $2^{n\log(n)}$ on the complexity of deterministic algorithms for convex integer optimization is presented, which does not appear outside specialized, technical research articles. Moreover, a general mixed-integer complexity bound allowing for both integer and continuous variables is presented that does not explicitly appear anywhere in the literature. A complete theory of branch-and-cut methods is also covered.