Solving low-density subset sum problems
Journal of the ACM (JACM)
Sphere-packings, lattices, and groups
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A hierarchy of polynomial time lattice basis reduction algorithms
Theoretical Computer Science
A more efficient algorithm for lattice basis reduction
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Improved low-density subset sum algorithms
Computational Complexity
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Mathematical Programming: Series A and B
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
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Improved algorithms for integer programming and related lattice problems
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
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Let B = [b1, …, bn] (with column vectors bi) be a basis of ℝn. Then L = ∑biℤ is a lattice in ℝn and A = B⊤B is the Gram matrix of B. The reciprocal lattice L* of L has basis B* = (B−1)⊤ with Gram matrix A−1. For any nonsingular matrix A = (ai,j) with inverse A−1 = (a*i,j), let τ(A) = max1≤i≤n {∑nj =1∣ai,j ·a*j,j∣}. Then τ(A), τ(A−1)≥1 holds, with equality for an orthogonal basis. We will show that for any lattice L there is a basis with Gram matrix A such that τ(A), τ(A−1) = exp (O((ln n)2)). This generalizes a result in [8] and [20].For any basis transformation A→Ā with Ā = T⊤AT, T = (ti,j)∈SLn(ℤ), we will show ∣ti,j∣≤τ(A−1) ·τ(Ā). This implies that every integral matrix representation of a finite group is equivalent to a representation where the coefficients of the matrices representing group elements are bounded by exp (O((ln n)2)). This new bound is considerably smaller than the known (exponential) bounds for automorphisms of Minkowski-reduced lattice bases: see, for example, [6].The quantities τ(A), τ(A−1) have the following geometric interpretation. Let V(L) ∶= {x∈ℝn∣∀λ∈L ∶∣x∣≤∣x−λ∣} be the Voronoi cell (also called the Dirichlet region) of the lattice L. For a basis B of L, we call C(B) = {∑xibi, ∣xi∣≤1/2} the basis cell of B. Both cells define a lattice tiling of ℝn (see [6]); they coincide for an orthogonal basis. For a general basis B of L with Gram matrix A we will show V(L)≤τ(A−1)·C(B) and C(B)≤n·τ(A)·V(L).