Improving on the Voronoi cell based techniques of [28, 24], we give a Las Vegas eO (2n) expected time and space algo- rithm for CVPP (the preprocessing version of the Closest Vector Problem, CVP). This improves on the eO (4n) deter- ministic runtime of the Micciancio Voulgaris algorithm [24] (henceforth MV) for CVPP 1 at the cost of a polynomial amount of randomness (which only a ects runtime, not cor- rectness). As in MV, our algorithm proceeds by computing a short path on the Voronoi graph of the lattice, where lattice points are adjacent if their Voronoi cells share a common facet, from the origin to a closest lattice vector. Our main technical contribution is a randomized procedure that, given the Voronoi relevant vectors of a lattice { the lattice vectors inducing facets of the Voronoi cell { as preprocessing, and any \close enough" lattice point to the target, computes a path to a closest lattice vector of expected polynomial size. This improves on the eO (2n) path length given by the MV algorithm. Furthermore, as in MV, each edge of the path can be computed using a single iteration over the Voronoi relevant vectors. As a byproduct of our work, we also give an optimal relationship between geometric and path distance on the Voronoi graph, which we believe to be of independent interest.
ACM-SIAM Symposium on Discrete Algorithms
Networks and Optimization

Dadush, D.N, & Bonifas, N. (2015). Short Paths on the Voronoi Graph and Closest Vector Problem with Preprocessing. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms 2015 (SODA) (pp. 295–314).