Abstract
Asymptotically, the best known algorithms for solving the Shortest Vector Problem (SVP) in a lattice of dimension n are sieve algorithms, which have heuristic complexity estimates ranging from \((4/3)^{n+o(n)}\) down to \((3/2)^{n/2 +o(n)}\) when Locality Sensitive Hashing techniques are used. Sieve algorithms are however outperformed by pruned enumeration algorithms in practice by several orders of magnitude, despite the larger super-exponential asymptotical complexity \(2^{\varTheta (n \log n)}\) of the latter.
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