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The Kullback–Leibler Divergence Between Lattice Gaussian Distributions


A lattice Gaussian distribution of given mean and covariance matrix is a discrete distribution supported on a lattice maximizing Shannon’s entropy under these mean and covariance constraints. Lattice Gaussian distributions find applications in cryptography and in machine learning. The set of Gaussian distributions on a given lattice can be handled as a discrete exponential family whose partition function is related to the Riemann theta function. In this paper, we first report a formula for the Kullback–Leibler divergence between two lattice Gaussian distributions and then show how to efficiently approximate it numerically either via Rényi’s \(\alpha \)-divergences or via the projective \(\gamma \)-divergences. We illustrate how to use the Kullback-Leibler divergence to calculate the Chernoff information on the dually flat structure of the manifold of lattice Gaussian distributions.

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  1. Definition: n univariate functions \(f_1(x),\ldots , f_n(x)\) are said to be linearly dependent if there exists n constants \(c_1,\ldots , c_n\), not all zero, such that \(\sum _{i=1}^n c_i f_i(x)=0\) for some x belonging to an interval \(I\subset \mathbb {R}\). Otherwise, the functions are said linearly independent.


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We thank the reviewers for the constructive and helpful suggestions on this paper.

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Correspondence to Frank Nielsen.

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Nielsen, F. The Kullback–Leibler Divergence Between Lattice Gaussian Distributions. J Indian Inst Sci (2022).

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  • Lattice Gaussian distribution
  • Discrete exponential family
  • Riemann theta function
  • Statistical divergence
  • Information geometry