Noah Stephens-Davidowitz: Solving SVP (and CVP) in 2^n Time via Discrete Gaussian Sampling

Friday, September 25, 2015 - 10:30am to 12:00pm
Hewlett G882
Noah Stephens-Davidowitz
Abstract: We show a $2^{n+o(n)}$-time algorithm for the Shortest Vector Problem on n-dimensional lattices (improving on the previous best-known algorithm of Micciancio and Voulgaris, which runs in time $4^{n+o(n)}$). The algorithm uses the elementary yet powerful observation that, by properly combining samples from a Gaussian distribution over the lattice, we can produce exact samples from a narrower Gaussian distribution on the lattice. We use such a procedure repeatedly to obtain samples from an arbitrarily narrow Gaussian distribution over the lattice, allowing us to find a shortest vector. 
Both the algorithm and the analysis are quite simple in hindsight. (The main technical tool is an identity on Gaussian measures with a simple geometric proof originally due to Riemann.) If time allows and interest merits, we will discuss a more technical variant of this algorithm that solves the Closest Vector Problem (a seemingly harder problem) in the same asymptotic running time.
Based on joint work with Divesh Aggarwal, Daniel Dadush, and Oded Regev. (See and