Lattice Sparsification and the Approximate Closest Vector Problem

TL;DR

This paper presents a deterministic algorithm for approximating the Closest Vector Problem in lattices with improved complexity, using lattice sparsification techniques to offer an alternative to existing sieve-based methods.

Contribution

It introduces a novel lattice sparsification method that maintains metric structure, enabling a deterministic approximation algorithm for CVP with competitive complexity.

Findings

Deterministic (1+eps)-approximate CVP algorithm with 2^{O(n)}(1+1/eps)^n time

Reduction of space complexity to polynomial under certain assumptions

Lattice sparsification technique preserves metric structure approximately

Abstract

We give a deterministic algorithm for solving the (1+eps)-approximate Closest Vector Problem (CVP) on any n dimensional lattice and any norm in 2^{O(n)}(1+1/eps)^n time and 2^n poly(n) space. Our algorithm builds on the lattice point enumeration techniques of Micciancio and Voulgaris (STOC 2010) and Dadush, Peikert and Vempala (FOCS 2011), and gives an elegant, deterministic alternative to the "AKS Sieve" based algorithms for (1+eps)-CVP (Ajtai, Kumar, and Sivakumar; STOC 2001 and CCC 2002). Furthermore, assuming the existence of a poly(n)-space and 2^{O(n)} time algorithm for exact CVP in the l_2 norm, the space complexity of our algorithm can be reduced to polynomial. Our main technical contribution is a method for "sparsifying" any input lattice while approximately maintaining its metric structure. To this end, we employ the idea of random sublattice restrictions, which was first employed by Khot (FOCS 2003) for the purpose of proving hardness for Shortest Vector Problem (SVP) under l_p norms.