Tensor-based hardness of the shortest vector problem to within almost polynomial factors

We show that unless NP RTIME (2^{poly(log n)}), for any > 0 there is no polynomial-time algorithm approximating the Shortest Vector Problem (SVP) on n-dimensional lattices inthe l_p norm (1 q p<) to within a factor of 2(log n)^1-. This improves the previous best factor of 2(logn)^1/2- under the same complexity assumption due to Khot. Under the stronger assumption NP RSUBEXP, we obtain a hardness factor of n ^{c/log log n} for some c > 0. Our proof starts with Khot's SVP instances from that are hard to approximate to within some constant. To boost the hardness factor we simply apply the standard tensor product oflattices. The main novel part is in the analysis, where we show that Khot's lattices behave nicely under tensorization. At the heart of the analysis is a certain matrix inequality which was first used in the context of lattices by de Shalit and Parzanchevski.