Branch-and-bound for integer optimization typically uses single-variable disjunctions. Enumerative methods for integer optimization with theoretical guarantees use a non-binary search tree with general disjunctions based on lattice structure. These disjunctions are expensive to compute and challenging to implement. Here we compare two lattice reformulations that can be used to heuristically obtain general disjunctions in the original space, we develop a new lattice-based variant, and compare the derived disjunctions computationally with those produced by the algorithm of Lovász and Scarf.

In this survey we address three of the principal algebraic approaches to integer programming. After introducing lattices and basis reduction, we first survey their use in integer programming, presenting among others Lenstra's algorithm that is polynomial in fixed dimension, and the solution of diophanine equations using basis reduction. The second topic concerns augmentation algorithms and test sets, including the role played by Hilbert and Gröbner bases in the development of a primal approach to solve a family of problems for all right-hand sides. Thirdly we survey the group approach of Gomory, showing the importance of subadditivity in integer programming and the generation of valid inequalities, as well the relation to the parametric problem cited above of solving for all right-hand sides.

We examine the polyhedral structure of the convex hull of feasible solutions of the capacitated facility location problem. In particular we derive necessary and sufficient conditions for a family of "effective capacity" inequalities to be facet-defining, and further results on a more general family called "submodular" inequalities.