Computing the theta function

TL;DR

This paper introduces a fully polynomial randomized approximation scheme for computing theta functions associated with positive definite quadratic forms, enabling efficient estimation and sampling related to lattices and Gaussian distributions.

Contribution

It develops a novel FPRAS for theta sums under eigenvalue conditions, utilizing integral representations and reciprocity, with applications in lattice analysis and sampling.

Findings

Efficient approximation of theta sums for certain eigenvalue ranges.

Application of the scheme to lattice vector problems and Gaussian sampling.

Demonstrates practical utility in lattice-based computations.

Abstract

Let $f: {\Bbb R}^n \longrightarrow {\Bbb R}$ be a positive definite quadratic form and let $y \in {\Bbb R}^n$ be a point. We present a fully polynomial randomized approximation scheme (FPRAS) for computing $\sum_{x \in {\Bbb Z}^n} e^{-f(x)}$, provided the eigenvalues of $f$ lie in the interval roughly between $s$ and $e^{s}$ and for computing $\sum_{x \in {\Bbb Z}^n} e^{-f(x-y)}$, provided the eigenvalues of $f$ lie in the interval roughly between $e^{-s}$ and $s^{-1}$ for some $s \geq 3$. To compute the first sum, we represent it as the integral of an explicit log-concave function on ${\Bbb R}^n$, 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 $L \subset {\Bbb R}^n$, to estimate the distance from a given point to a lattice, and to sample a random lattice point from the discrete Gaussian distribution.