Thesis – Littlewood’s Conjecture

Lattice reduction – Wikipedia

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Lattice reduction From Wikipedia, the free encyclopedia Jump to navigation Jump to search 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 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.

Source: Lattice reduction – Wikipedia

Lattice reduction in two dimensions: analyses under realistic probabilistic models – document

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Forn≥3, the LLL algorithm [13] due to Lenstra, Lenstra andLov ́asz, computes a reduced basis of ann-dimensional lattice in polynomial time. However, the notion ofreduction is weaker than in the casen=2, and the exact complexity of the algorithm (even in the worst-case, and for small dimensions) is not precisely known. The LLL algorithm uses as a main procedure theGauss Algorithm.

Source: Lattice reduction in two dimensions: analyses under realistic probabilistic models – document

A 3-Dimensional Lattice Reduction Algorithm | SpringerLink

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The aim of this paper is a reduction algorithm for a basis b1,b2, b3 of a 3-dimensional lattice in ℝn for fixed n ≥ 3. We give a definition of the reduced basis which is equivalent to that of the Minkowski reduced basis of a 3-dimensional lattice. We prove that for b 1 , b2, b3 ε ℤn, n ≥ 3 and |b1|, |b2|, |b3 | ≤ M, our algorithm takes O(log2 M) binary operations, without using fast integer arithmetic, to reduce this basis and so to find the shortest vector in the lattice.

Source: A 3-Dimensional Lattice Reduction Algorithm | SpringerLink

Lattice reduction – Wikipedia

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In two dimensionsFor 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.

Source: Lattice reduction – Wikipedia

mathematica graphing a 2 by 2 matrix as an object

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is there a possibility to “transform” a matrix (inheriting the input-output “weights”) into a graph object inheriting the matrix relations?

you can convert it to an edge-weighted graph using WeightedAdjacencyGraph. This will give you a complete graph (a Graph expression) in which each vertex is also connected to itself. I am not sure how much sense it makes treat this matrix as a graph, given the full connectivity.

Since you have a fully connected graph, it does not matter in your case, but be aware that WeightedAdjacencyGraph represents missing edges with Infinity, not with zero. This is somewhat annoying because it is inconsistent with WeightedAdjacencyMatrix, which uses 0. While Infinity does make sense in some (not all) applications, it is much more common to see data that uses 0.

 

 

 

via [✓] Convert matrix into “Graph object”? – Online Technical Discussion Groups—Wolfram Community

Geometry of Linear Transformations of the Plane – HMC Calculus Tutorial

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If V=R2 and W=R2, then T:R2R2 is a linear transformation if and only if there exists a 22 matrix A such that T(v)=Av for all vR2. Matrix A is called the standard matrix for T. The columns of A are T01 and T10 , respectively. Since each linear transformation of the plane has a unique standard matrix, we will identify linear transformations of the plane by their standard matrices. It can be shown that if A is invertible, then the linear transformation defined by A maps parollelograms to parallelograms. We will often illustrate the action of a linear transformation T:R2R2 by looking at the image of a unit square under T

Source: Geometry of Linear Transformations of the Plane – HMC Calculus Tutorial

Affine transformation – Wikipedia

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the image T(P) of any point P is determined by noting that T(A) = A′, T applied to the line segment AB is A′B′, T applied to the line segment AC is A′C′, and T respects scalar multiples of vectors based at A. [If A, E, F are collinear then the ratio length(AF)/length(AE) is equal to length(A′F′)/length(A′E′).] Geometrically T transforms the grid based on ABCD to that based in A′B′C′D′.Affine transformations do not respect lengths or angles; they multiply area by a constant factor

Source: Affine transformation – Wikipedia

Solving Linear Systems—Wolfram Language Documentation

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In some cases, however, you may prefer to convert the system of linear equations into a matrix equation, and then apply matrix manipulation operations to solve it. This approach is often useful when the system of equations arises as part of a general algorithm, and you do not know in advance how many variables will be involved.

Source: Solving Linear Systems—Wolfram Language Documentation