  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.
