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A sieve algorithm for the shortest lattice vector problem

Published: 06 July 2001 Publication History

Abstract

We present a randomized 2^{O(n)} time algorithm to compute a shortest non-zero vector in an n-dimensional rational lattice. The best known time upper bound for this problem was 2^{O(n\log n)} first given by Kannan [7] in 1983. We obtain several consequences of this algorithm for related problems on lattices and codes, including an improvement for polynomial time approximations to the shortest vector problem. In this improvement we gain a factor of log log n in the exponent of the approximating factor.

References

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M. Ajtai. The shortest vector problem in L2 is NP-hard for randomized reductions. Proc. 30th ACM Symposium on Theory of Computing, pp. 10-19, 1998.
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A. Blum, A. Kalai, and H. Wasserman. Noise-tolerant learning, the parity problem, and the statistical query model. Proc. 32nd ACM Symposium on Theory of Computing, pp. 435-440, 2000.
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P. van Emde Boas. Another NP-complete partition problem and the complexity of computing short vectors in lattices. Mathematics Department, University of Amsterdam, TR 81-04, 1981.
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O. Goldreich and S. Goldwasser. On the limits of nonapproximability of lattice problems. Journal of Computer and System Sciences, 60(3):540-563, 2000.
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B. Helfrich. Algorithms to construct Minkowski reduced and Hermite reduced bases. Theoretical Computer Science, 41:125-139, 1985.
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R. Kannan. Minkowski's convex body theorem and integer programming. Mathematics of Operations Research, 12:415-440, 1987. Preliminary version in ACM Symposium on Theory of Computing 1983.
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P. Klein. Finding the closest lattice vector when it's unusually close. Proc. 11th Symposium on Discrete Algorithms, 2000.
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R. Kumar and D. Sivakumar. On polynomial approximations to the shortest lattice vector length. Proc. 12th Symposium on Discrete Algorithms, 2001. To appear.
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A. K. Lenstra, H. W. Lenstra, and L. Lovasz. Factoring polynomials with rational coefficients. Mathematische Annalen, 261:515-534, 1982.
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L. Lovasz. An Algorithmic Theory of Numbers, Graphs and Convexity. CBMS-NSF Regional Conference Series on Applied Mathematics, SIAM, 1986.
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D. Micciancio. The shortest vector in a lattice is hard to approximate to within some constant. Proc. 39th IEEE Symposium on Foundations of Computer Science, pp. 92-98, 1998.
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Published In

cover image ACM Conferences
STOC '01: Proceedings of the thirty-third annual ACM symposium on Theory of computing
July 2001
755 pages
ISBN:1581133499
DOI:10.1145/380752
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 July 2001

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STOC '01 Paper Acceptance Rate 83 of 230 submissions, 36%;
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  • (2024)A Lattice-Based Method for Recovering the Unknown Parameters of Truncated Multiple Recursive Generators with ConstantChinese Journal of Electronics10.23919/cje.2022.00.38733:6(1458-1467)Online publication date: Nov-2024
  • (2024)Quantum-Resistant Encryption For Secure End-to-End Communication2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)10.23919/ITUK62727.2024.10772922(1-8)Online publication date: 21-Oct-2024
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  • (2024)A Novel Approximation Algorithm for the Shortest Vector ProblemIEEE Access10.1109/ACCESS.2024.346936812(141026-141031)Online publication date: 2024
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