Basis Reduction, and the Complexity of Branch-and-Bound

TL;DR

This paper demonstrates that reformulating integer feasibility problems using basis reduction techniques significantly reduces the complexity of branch-and-bound algorithms, often solving problems at the root node especially with large coefficient bounds.

Contribution

It provides theoretical bounds and practical insights showing that basis reduction leads to efficient solutions for reformulated integer programs, with empirical validation.

Findings

Branch-and-bound solves most reformulated instances at the root node.

Large coefficient bounds M improve the likelihood of root-node solutions.

Numerical bounds on M guarantee high success rates for moderate problem sizes.

Abstract

The classical branch-and-bound algorithm for the integer feasibility problem has exponential worst case complexity. We prove that it is surprisingly efficient on reformulated problems, in which the columns of the constraint matrix are short, and near orthogonal, i.e. a reduced basis of the generated lattice; when the entries of A (the dense part of the constraint matrix) are from {1, ..., M} for a large enough M, branch-and-bound solves almost all reformulated instances at the rootnode. We also prove an upper bound on the width of the reformulations along the last unit vector. The analysis builds on the ideas of Furst and Kannan to bound the number of integral matrices for which the shortest vectors of certain lattices are long, and also uses a bound on the size of the branch-and-bound tree based on the norms of the Gram-Schmidt vectors of the constraint matrix. We explore practical aspects of these results. First, we compute numerical values of M which guarantee that 90, and 99 percent of the reformulated problems solve at the root: these turn out to be surprisingly small when the problem size is moderate. Second, we confirm with a computational study that random integer programs become easier, as the coefficients grow.