English

Computing the theta function

Numerical Analysis 2023-06-06 v2 Computational Geometry Data Structures and Algorithms Numerical Analysis Combinatorics

Abstract

Let f:RnRf: {\Bbb R}^n \longrightarrow {\Bbb R} be a positive definite quadratic form and let yRny \in {\Bbb R}^n be a point. We present a fully polynomial randomized approximation scheme (FPRAS) for computing xZnef(x)\sum_{x \in {\Bbb Z}^n} e^{-f(x)}, provided the eigenvalues of ff lie in the interval roughly between ss and ese^{s} and for computing xZnef(xy)\sum_{x \in {\Bbb Z}^n} e^{-f(x-y)}, provided the eigenvalues of ff lie in the interval roughly between ese^{-s} and s1s^{-1} for some s3s \geq 3. To compute the first sum, we represent it as the integral of an explicit log-concave function on Rn{\Bbb R}^n, and to compute the second sum, we use the reciprocity relation for theta functions. We then apply our results to test the existence of many short integer vectors in a given subspace LRnL \subset {\Bbb R}^n, to estimate the distance from a given point to a lattice, and to sample a random lattice point from the discrete Gaussian distribution.

Keywords

Cite

@article{arxiv.2208.05405,
  title  = {Computing the theta function},
  author = {Alexander Barvinok},
  journal= {arXiv preprint arXiv:2208.05405},
  year   = {2023}
}

Comments

29 pages, various improvements

R2 v1 2026-06-25T01:37:38.922Z