TY - JOUR
T1 - A physical study of the LLL algorithm
AU - Ding, Jintai
AU - Kim, Seungki
AU - Takagi, Tsuyoshi
AU - Wang, Yuntao
AU - Yang, Bo yin
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2023/3
Y1 - 2023/3
N2 - This paper presents a study of the LLL algorithm from the perspective of statistical physics. Based on our experimental and theoretical results, we suggest that interpreting LLL as a sandpile model may help understand much of its mysterious behavior. In the language of physics, our work presents evidence that LLL and certain 1-d sandpile models with simpler toppling rules belong to the same universality class. This paper consists of three parts. First, we introduce sandpile models whose statistics imitate those of LLL with compelling accuracy, which leads to the idea that there must exist a meaningful connection between the two. Indeed, on those sandpile models, we are able to prove the analogues of some of the most desired statements for LLL, such as the existence of the gap between the theoretical and the experimental RHF bounds. Furthermore, we test the formulas from finite-size scaling theory (FSS) against the LLL algorithm itself, and find that they are in excellent agreement. This in particular explains and refines the geometric series assumption (GSA), and allows one to extrapolate various quantities of interest to the dimension limit. In particular, we obtain the estimate that the empirical average RHF converges to ≈1.02265 as the dimension goes to infinity.
AB - This paper presents a study of the LLL algorithm from the perspective of statistical physics. Based on our experimental and theoretical results, we suggest that interpreting LLL as a sandpile model may help understand much of its mysterious behavior. In the language of physics, our work presents evidence that LLL and certain 1-d sandpile models with simpler toppling rules belong to the same universality class. This paper consists of three parts. First, we introduce sandpile models whose statistics imitate those of LLL with compelling accuracy, which leads to the idea that there must exist a meaningful connection between the two. Indeed, on those sandpile models, we are able to prove the analogues of some of the most desired statements for LLL, such as the existence of the gap between the theoretical and the experimental RHF bounds. Furthermore, we test the formulas from finite-size scaling theory (FSS) against the LLL algorithm itself, and find that they are in excellent agreement. This in particular explains and refines the geometric series assumption (GSA), and allows one to extrapolate various quantities of interest to the dimension limit. In particular, we obtain the estimate that the empirical average RHF converges to ≈1.02265 as the dimension goes to infinity.
KW - Finite-size scaling theory
KW - Lattice reduction
KW - LLL algorithm
KW - Sandpile models
UR - http://www.scopus.com/inward/record.url?scp=85141399640&partnerID=8YFLogxK
U2 - 10.1016/j.jnt.2022.09.013
DO - 10.1016/j.jnt.2022.09.013
M3 - Article
AN - SCOPUS:85141399640
SN - 0022-314X
VL - 244
SP - 339
EP - 368
JO - Journal of Number Theory
JF - Journal of Number Theory
ER -