In this paper, we present a heuristic algorithm for solving exact,
as well as approximate, shortest vector and closest vector problems on lattices. The algorithm can be seen
as a modified sieving algorithm for which the vectors of the intermediate
sets lie in overlattices or translated cosets of overlattices.
The key idea is hence to no longer work with a single lattice but to move the problems around in
a tower of related lattices. We initiate the algorithm by sampling
very short vectors in an overlattice of the original lattice that
admits a quasi-orthonormal basis and hence an efficient enumeration
of vectors of bounded norm. Taking sums of vectors in the sample, we
construct short vectors in the next lattice. Finally, we
obtain solution vector(s) in the initial lattice as a sum of vectors
of an overlattice. The complexity analysis relies on
the Gaussian heuristic. This heuristic is backed by experiments in
low and high dimensions that closely reflect these estimates when
solving hard lattice problems in the average case.
This new approach allows us
to solve not only shortest vector problems, but also closest vector
problems, in lattices of dimension $n$ in time $2^{0.3774\,n}$ using
memory $2^{0.2925\,n}$. Moreover, the algorithm is straightforward to
parallelize on most computer architectures.
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