Lattice reduction
Overview
Having introduced the LLL reduction, we now provide a more general notions of a reduced basis for a lattice as well as provide bounds for the basis vectors. The key idea behind introducing these definitions is that once we know some basis vector is []-reduced, we can bound the sizes of the basis, which is important when algorithms require short vectors in a lattice. For fast algorithms, LLL-reduction is typically the most important notion as it can be computed quickly. Two main definitions appear often when discussing lattice reductions, which we will provide here.
Definitions
A basis{bi}i=1dis size-reduced if ∣μi,j∣≤21. Intuitively this captures the idea that a reduced basis being "almost orthogonal".
Let Lbe a lattice, 1≤i≤dimL=d, we define the ithsuccessive minimaλi(L) as
Intuitively, λi(L)is the length of the "ith shortest lattice vector". This intuition is illustrated by the definition of λ1:
However this is not precise as if vis the shortest lattice vector, then −vis also the shortest lattice vector.
Unfortunately, a basisbifor Lwhere λi(L)=∥bi∥for dimensions 5 and above. This tells us that we can't actually define "the most reduced basis" in contrast to the 2D case (see Lagrange's algorithm) and we would need some other definition to convey this intuition.
An alternate definition ofλi(L)that will be helpful is the radius of the smallest ball centered at the origin such that the ball contains at leastilinearly independent vectors inL.
Exercises
1) Show that both definitions of λi are equivalent
2) Consider the lattice L=2000102001002010002100001. Show that the successive minima are all2but no basisbican satisfy ∥bi∥=λi.
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