In this section
@inproceedings{Wang:2017:10.1109/ITW.2017.8278001,
author = {Wang, Z and Ling, C},
doi = {10.1109/ITW.2017.8278001},
pages = {269--273},
publisher = {IEEE},
title = {On the geometric ergodicity of Gibbs algorithm for lattice gaussian sampling},
url = {http://dx.doi.org/10.1109/ITW.2017.8278001},
year = {2017}
}
TY - CPAPER
AB - 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.
AU - Wang,Z
AU - Ling,C
DO - 10.1109/ITW.2017.8278001
EP - 273
PB - IEEE
PY - 2017///
SN - 2475-420X
SP - 269
TI - On the geometric ergodicity of Gibbs algorithm for lattice gaussian sampling
UR - http://dx.doi.org/10.1109/ITW.2017.8278001
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000426901500055&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
ER -
