International audience For , let be independent random vectors in with the same distribution invariant by rotation and without mass at the origin. Almost surely these vectors form a basis for the Euclidean lattice they generate. The topic of this paper is the property of reduction of this random basis in the sense of Lenstra-Lenstra-Lovász (LLL). If is the basis obtained from by Gram-Schmidt orthogonalization, the quality of the reduction depends upon the sequence of ratios of squared lengths of consecutive vectors , . We show that as the process tends in distribution in some sense to an explicit process ; some properties of the latter are provided. The probability that a random random basis is -LLL-reduced is then showed to converge for , ...
The Lenstra-Lenstra-Lovász basis reduction algorithm, also known as LLL algorithm, is an algorithm t...
Lenstra, Lenstra, and Lov´asz in [7] proved several inequalities showing that the vectors in an LLL-...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
For p ≤ n, let b1(n),...,bp(n) be independent random vectors in $\mathbb{R}^n$ with the same distrib...
Abstract. For p ≤ n, let b(n)1,..., b(n)p be independent random vectors in Rn with the same dis-trib...
AbstractTwo new lattice reduction algorithms are presented and analyzed. These algorithms, called th...
Lattice reduction algorithms such as LLL and its floating-point variants have a very wide range of a...
Abstract. Lattice reduction algorithms behave much better in prac-tice than their theoretical analys...
International audienceAs a typical application, the Lenstra-Lenstra-Lovász lattice basis reduction a...
This note deals with a problem of the probabilistic Ramsey theory in functional analysis. G...
Let B be a basis of a Euclidean lattice, and B ̃ an approxima-tion thereof. We give a sufficient con...
International audienceThe general behavior of lattice reduction algorithms is far from beingwell und...
Abstract:Development of efficient solvers of the (approximated) shortest vector problem over lattice...
The Gaussian algorithm for lattice reduction in dimension 2 is precisely analysed under a class of r...
International audienceIn 1982, Arjen Lenstra, Hendrik Lenstra Jr. and László Lovász introduced an ef...
The Lenstra-Lenstra-Lovász basis reduction algorithm, also known as LLL algorithm, is an algorithm t...
Lenstra, Lenstra, and Lov´asz in [7] proved several inequalities showing that the vectors in an LLL-...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
For p ≤ n, let b1(n),...,bp(n) be independent random vectors in $\mathbb{R}^n$ with the same distrib...
Abstract. For p ≤ n, let b(n)1,..., b(n)p be independent random vectors in Rn with the same dis-trib...
AbstractTwo new lattice reduction algorithms are presented and analyzed. These algorithms, called th...
Lattice reduction algorithms such as LLL and its floating-point variants have a very wide range of a...
Abstract. Lattice reduction algorithms behave much better in prac-tice than their theoretical analys...
International audienceAs a typical application, the Lenstra-Lenstra-Lovász lattice basis reduction a...
This note deals with a problem of the probabilistic Ramsey theory in functional analysis. G...
Let B be a basis of a Euclidean lattice, and B ̃ an approxima-tion thereof. We give a sufficient con...
International audienceThe general behavior of lattice reduction algorithms is far from beingwell und...
Abstract:Development of efficient solvers of the (approximated) shortest vector problem over lattice...
The Gaussian algorithm for lattice reduction in dimension 2 is precisely analysed under a class of r...
International audienceIn 1982, Arjen Lenstra, Hendrik Lenstra Jr. and László Lovász introduced an ef...
The Lenstra-Lenstra-Lovász basis reduction algorithm, also known as LLL algorithm, is an algorithm t...
Lenstra, Lenstra, and Lov´asz in [7] proved several inequalities showing that the vectors in an LLL-...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...