On the geometric ergodicity of Gibbs algorithm for lattice Gaussian sampling | IEEE Conference Publication | IEEE Xplore
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On the geometric ergodicity of Gibbs algorithm for lattice Gaussian sampling


Abstract:

Sampling from the lattice Gaussian distribution is emerging as an important problem in coding and cryptography. In this paper, the conventional Gibbs sampling algorithm i...Show More

Abstract:

Sampling from the lattice Gaussian distribution is emerging as an important problem in coding and cryptography. In this paper, the conventional Gibbs sampling algorithm is demonstrated to be geometrically ergodic in tackling with lattice Gaussian sampling, which means its induced Markov chain converges exponentially fast to the stationary distribution. Moreover, as the exponential convergence rate is dominated by the spectral radius of the forward operator of the Markov chain, a comprehensive analysis is given and we show that the convergence performance can be further enhanced by usages of blocked sampling strategy and choices of selection probabilities.
Date of Conference: 06-10 November 2017
Date Added to IEEE Xplore: 01 February 2018
ISBN Information:
Conference Location: Kaohsiung, Taiwan

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