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| [[Image:Lattice-reduction.svg|thumb|right|300px|Lattice reduction in two dimensions: the black vectors are the given basis for the lattice (represented by blue dots), the red vectors are the reduced basis]]
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| In mathematics, the goal of '''lattice basis reduction''' is given an integer [[lattice (group)|lattice]] basis as input, to find a [[basis (linear algebra)|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.
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| ==Nearly Orthogonal==
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| 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.
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| Any particular basis of <math>n</math> vectors may be represented by a [[Matrix (mathematics)|matrix]] <math>B</math>, whose columns are the basis vectors <math>b_i, i = 1, \ldots, n</math>. In the '''fully dimensional''' case where the number of basis vectors is equal to the dimension of the space they occupy, this matrix is square, and the volume of the fundamental parallelepiped is simply the absolute value of the [[determinant]] of this matrix <math>\det(B)</math>. If the number of vectors is less than the dimension of the underlying space, then volume is <math>\sqrt{\det(B^T B)}</math>. For a given lattice <math>\Lambda</math>, this volume is the same (up to sign) for any basis, and hence is referred to as the determinant of the lattice <math>\det(\Lambda)</math> or '''lattice constant''' <math>d(\Lambda)</math>.
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| The orthogonality defect is the product of the basis vector lengths divided by the parallelepiped volume;
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| :<math>\delta(B) = \frac{\Pi_{i=1}^n ||b_i||}{\sqrt{\det(B^T B)}} = \frac{\Pi_{i=1}^n ||b_i||}{d(\Lambda)}</math>
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| From the geometric definition it may be appreciated that <math>\delta(B) \ge 1</math> with equality if and only if the basis is orthogonal.
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| If the lattice reduction problem is defined as finding the basis with the smallest possible defect, then the problem is [[NP complete]]. However, there exist [[polynomial time]] algorithms to find a basis with defect <math>\delta(B) \le c</math>
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| where ''c'' is some constant depending only on the number of basis vectors and the dimension of the underlying space (if different). This is a good enough solution in many practical applications.
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| ==In two dimensions==
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| For a basis consisting of just two vectors, there is a simple and efficient method of reduction{{Specify|date=September 2011}} 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.
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| ==Applications==
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| Lattice reduction algorithms are used in a number of modern number theoretical applications, including in the discovery of a [[spigot algorithm]] for pi. Although determining the shortest basis is possibly an NP-complete problem, algorithms such as the [[Lenstra–Lenstra–Lovász lattice basis reduction algorithm|LLL algorithm]] <ref>{{cite journal | author = [[A. K. Lenstra|Lenstra, A. K.]]; [[H. W. Lenstra, Jr.|Lenstra, H. W., Jr.]]; [[Lovász|Lovász, L.]] | title = Factoring polynomials with rational coefficients | journal = [[Mathematische Annalen]] | volume = 261 | year = 1982 | issue = 4 | pages = 515–534 | id = {{hdl|1887/3810}} | doi = 10.1007/BF01457454 | mr = 0682664 }}</ref> can find a short (not necessarily shortest) basis in polynomial time with guaranteed worst-case performance. [[Lenstra–Lenstra–Lovász lattice basis reduction algorithm|LLL]] is widely used in the [[cryptanalysis]] of [[Public-key cryptography|public key]] cryptosystems.
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| When used to find integer relations, a typical input to the algorithm consists of an augmented nxn identity matrix with the entries in the last column consisting of the n elements (multiplied by a large positive constant w to penalize vectors that do not sum to zero) between which the relation is sought.
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| The [[LLL algorithm]] for computing a nearly-orthogonal basis was used to show that [[integer programming]] in any fixed dimension can be done in [[P (complexity)|polynomial time]].<ref>{{cite journal|
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| doi = 10.1287/moor.8.4.538|
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| author = Lenstra, H.W.|
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| title = Integer programming with a fixed number of variables|
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| journal = Math. Oper. Res.|
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| year = 1983|
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| volume = 8|
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| pages = 538–548
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| }}
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| </ref>
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| ==Algorithms==
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| The following algorithms reduce lattice bases. They can be compared in terms of runtime and approximation to an optimal solution, always relative to the dimension of the given lattice. If there are public implementations of these algorithms this should also be noted here.
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| {|
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| |-
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| ! Year
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| ! Algorithm
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| ! Name
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| ! Implementation
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| |-
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| | 1982
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| | [[Lenstra–Lenstra–Lovász lattice basis reduction algorithm|LLL]]
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| | Lenstra Lenstra Lovász
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| | [[Number Theory Library|NTL]]
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| |-
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| | 1987
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| | BKZ
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| | Block Korkine Zolotarev
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| | [[Number Theory Library|NTL]]
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| |-
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| | 2002
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| | RSR
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| | Random Sampling Reduction
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| |-
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| | 2002
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| | PDR
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| | Primal Dual Reduction
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| |}
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| ==References==
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| <references/>
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| [[Category:Theory of cryptography]]
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| [[Category:Computational number theory]]
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| [[Category:Lattice points]]
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| [[Category:Linear algebra]]
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50 yr old Print Journalist Brice Kneip from Canada, has many hobbies including football, ganhando dinheiro na internet and digital photography. Has in recent years finished a travel to Western Australia.
my website :: ganhe dinheiro