Given an integer lattice basis as input, to find a basis with short, nearly orthogonal vectors
In mathematics, the goal of lattice basis reduction is given an integer lattice basis as input, to find a basis with short, nearly orthogonal vectors. This is realized using different algorithms, whose running time is usually at least exponential in the dimension of the lattice.
Lattice reduction Intro articles: 3
One measure of nearly orthogonal is the orthogonality defect. This compares the product of the lengths of the basis vectors with the volume of the parallelepiped they define. For perfectly orthogonal basis vectors, these quantities would be the same.
Any particular basis of
The orthogonality defect is the product of the basis vector lengths divided by the parallelepiped volume;
From the geometric definition it may be appreciated that
If the lattice reduction problem is defined as finding the basis with the smallest possible defect, then the problem is NP-hard. However, there exist polynomial time algorithms to find a basis with defect
Lattice reduction Nearly orthogonal articles: 5
In two dimensions
For a basis consisting of just two vectors, there is a simple and efficient method of reduction closely analogous to the Euclidean algorithm for the greatest common divisor of two integers. As with the Euclidean algorithm, the method is iterative; at each step the larger of the two vectors is reduced by adding or subtracting an integer multiple of the smaller vector.
The pseudocode of the algorithm, often known as Lagrange's algorithm or the Lagrange-Gauss algorithm, is as follows:
a basis for the lattice . Assume that , otherwise swap them. Output: A basis with .
See the section on Lagrange's algorithm in  for further details.
Lattice reduction In two dimensions articles: 2
Lattice reduction algorithms are used in a number of modern number theoretical applications, including in the discovery of a spigot algorithm for
When used to find integer relations, a typical input to the algorithm consists of an augmented
Lattice reduction Applications articles: 7
The following algorithms reduce lattice bases; several public implementations of these algorithms are also listed.
|1773||Lagrange/Gauss reduction for 2D lattices|
|1982||Lenstra–Lenstra–Lovász reduction||NTL, fplll (GitHub link)|
|1987||Block Korkine Zolotarev||NTL, fplll (GitHub link)|
Overview of "Number Theory Library" article
- Nguyen, Phong Q. (2009). "Hermite's Constant and Lattice Algorithms". The LLL Algorithm. Information Security and Cryptography. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 19–69. doi:10.1007/978-3-642-02295-1_2. ISBN 978-3-642-02294-4. ISSN 1619-7100.
- Lenstra, A. K.; Lenstra, H. W. Jr.; Lovász, L. (1982). "Factoring polynomials with rational coefficients". Mathematische Annalen. 261 (4): 515–534. CiteSeerX 10.1.1.310.318. doi:10.1007/BF01457454. hdl:1887/3810. MR 0682664. S2CID 5701340.
- Lenstra, H.W. (1983). "Integer programming with a fixed number of variables". Math. Oper. Res. 8 (4): 538–548. CiteSeerX 10.1.1.431.5444. doi:10.1287/moor.8.4.538.
- Hanrot, Guillaume; Stehlé, Damien (2008). "Worst-Case Hermite-Korkine-Zolotarev Reduced Lattice Bases". arXiv:0801.3331 [math.NT].
- Seysen, Martin (September 1993). "Simultaneous reduction of a lattice basis and its reciprocal basis". Combinatorica. 13 (3): 363–376. doi:10.1007/BF01202355. S2CID 206791637.