Abstract
The LLL algorithm aims at finding a “reduced” basis of a Euclidean lattice and plays a primary role in many areas of mathematics and computer science. However, its general behaviour is far from being well understood. There are already many experimental observations about the number of iterations or the geometry of the output, that raise challenging questions which remain unanswered and lead to natural conjectures which are yet to be proved. However, until now, there exist few experimental observations about the precise execution of the algorithm. Here, we provide experimental results which precisely describe an essential parameter of the execution, namely the “logarithm of the decreasing ratio”. These experiments give arguments towards a “regularity” hypothesis (R). Then, we propose a simplified model for the LLL algorithm based on the hypothesis (R), which leads us to discrete dynamical systems, namely sandpiles models. It is then possible to obtain a precise quantification of the main parameters of the LLL algorithm. These results fit the experimental results performed on general input bases, which indirectly substantiates the validity of such a regularity hypothesis and underlines the usefulness of such a simplified model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ajtai, M.: Optimal lower bounds for the Korkine-Zolotareff parameters of a lattice and for Schnorr’s algorithm for the shortest vector problem. Theory of Computing 4(1), 21–51 (2008)
Akhavi, A.: Random lattices, threshold phenomena and efficient reduction algorithms. Theoret. Comput. Sci. 287(2), 359–385 (2002)
Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: An explanation of the 1/f noise. Phys. Rev. Lett. 59(4), 381–384 (1987)
Boneh, D., Durfee, G.: Cryptanalysis of RSA with private key d less than N ≤ 0.292. IEEE Trans. Inform. Theory 46(4), 1339–1349 (2000)
Daudé, H., Vallée, B.: An upper bound on the average number of iterations of the LLL algorithm. Theoretical Computer Science 123(1), 95–115 (1994)
Gama, N., Nguyen, P.: Predicting Lattice Reduction. In: Smart, N.P. (ed.) EUROCRYPT 2008. LNCS, vol. 4965, pp. 31–51. Springer, Heidelberg (2008)
Georgieva, M.: Étude expérimentale de l’algorithme LLL sur certaines bases de Coppersmith, Master Thesis, University of Caen (2009)
Goles, E., Kiwi, M.A.: Games on line graphs and sandpiles. Theoret. Comput. Sci. 115(2), 321–349 (1993)
Jensen, H.J.: Self-organized criticality. In: Emergent complex behavior in physical and biological systems. Cambridge Lecture Notes in Physics, vol. 10. Cambridge University Press, Cambridge (1998)
Lenstra, A.K., Lenstra Jr., H.W., Lovász, L.: Factoring polynomials with rational coefficients. Math. Ann. 261(4), 515–534 (1982)
Nguyen, P., Stehlé, D.: LLL on the average. In: Hess, F., Pauli, S., Pohst, M. (eds.) ANTS 2006. LNCS, vol. 4076, pp. 238–256. Springer, Heidelberg (2006)
Vallée, B.: Euclidean Dynamics. Discrete and Continuous Dynamical Systems 15(1), 281–352 (2006)
Vallée, B., Vera, A.: Probabilistic analyses of lattice reduction algorithms. In: ch.3. The LLL Algorithm. Collection Information Security and Cryptography Series. Springer, Heidelberg (2009)
Vera, A.: Analyses de l’algorithme de Gauss. Applications à l’analyse de l’algorithme LLL, PhD Thesis, Universiy of Caen (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Madritsch, M., Vallée, B. (2010). Modelling the LLL Algorithm by Sandpiles. In: López-Ortiz, A. (eds) LATIN 2010: Theoretical Informatics. LATIN 2010. Lecture Notes in Computer Science, vol 6034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12200-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-12200-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12199-9
Online ISBN: 978-3-642-12200-2
eBook Packages: Computer ScienceComputer Science (R0)