The Lenstra–Lenstra–Lovász (LLL) algorithm is an algorithm that efficiently
transforms a “bad” basis for a lattice
L into a “pretty good” basis for the
same lattice. This transformation of a bad basis into a better basis is known
as lattice reduction, and it has useful applications. For example, there
is attack against ECDSA implementations that leverage biased
that can lead to private key recovery. However, my experience learning why LLL
works has been pretty rough. Most material covering LLL seems targeted towards
mathematicians and I had to (I guess I wanted to) spend a lot of time trying
to weasel out the intuition and mechanics of the algorithm. This blog post is a
semi-organized brain dump of that process. My goal is to cover LLL in such a
way that slowly ratchets down the hand-waving, so feel free to read until you
are happy with your level of understanding.
As an aside: if I’m wrong anywhere, please correct me! Hitting a nice balance of mathematical accuracy and intuition is tough, but I don’t aim to be blatantly wrong. Please feel free to tweet at me if I screwed up. Its super possible I did.
I don’t think LLL will make much sense if you don’t have a decent grasp on (at least):
vector spaces and bases
what a lattice is
why lattice basis reduction is useful
vector projections and the Gram-Schmidt process
I wrote another blog post on lattices and how they can be used to form an asymmetric cryptosystem. I think it would be a good thing to read first as it covers some of the suggested background.
LLL In-Relation to Euclid’s Algorithm
LLL often gets compared to Euclid’s Algorithm for finding GCDs. This is an imperfect analogy, but at a high-level they have a core similarity. Namely, both LLL and Euclid’s algorithm could be broken down into two steps: “reduction” and “swap”. To illustrate consider the following pseudocode for both algorithms:
def euclid_gcd(a, b): if b == 0: # base case return a x = a mod b # reduction step return euclid_gcd(b, x) # swap step
# dont try to grok this yet... def lll(basis): while k <= n: for j in reverse(range(k-1, 0)): # reduction step loop m = mu(k,j) basis[k] = basis[k] - mu*basis[j] # vector reduction # update orthogonalized basis if lovasz_condition: k += 1 else: basis[k], basis[k+1] = basis[k+1], basis[k] # swap step # update orthogonalized basis k = max(k-1,1) return basis
If you squint a bit you can see the similarities in the pseudocode, but I think
the core idea is that both algorithms use a reduce-and-then-swap technique to
achieve their goals. So at this point, we can roughly say that LLL is an
extension of Euclid’s algorithm that applies to a set of
n-vectors instead of
Personally, I don’t find that explanation to be satisfying but I can see why it could be a useful in developing understanding.
LLL In-Relation to Gram-Schmidt
Another algorithm that is similar to LLL is the Gram-Schmidt orthogonalization process. At a high-level, Gram-Schmidt (GS) takes in an input basis for a vector space and returns an orthogonalized (i.e. all vectors in the basis are orthogonal to one another) basis that spans the same space. It does this by leveraging vector projections to “decompose” each vector into related components and removing redundant components from all vectors. If GS doesn’t make too much sense to you, I suggest checking it out before going too much further in this post. Not only is the algorithm similar to LLL, but LLL uses Gram-Schmidt as a subroutine.
You may say: “Why don’t we use GS to reduce our lattice basis?” and we cant because life sucks sometimes. GS may get us closer to the basis we want, but GS is not guaranteed to produce orthogonal vectors that form a basis for our lattice. Check it:
sage: from mage import matrix_utils # https://github.com/kelbyludwig/mage; use the install.sh script to install sage: b1 = vector(ZZ, [3,5]) sage: b2 = vector(ZZ, [8,3]) sage: B = Matrix(ZZ, [b1,b2]) sage: Br,_ = B.gram_schmidt() sage: pplot = matrix_utils.plot_2d_lattice(B, B) sage: pplot += plot(Br, color='grey', linestyle='-.', legend_label='unmodified', legend_color='blue') sage: pplot += plot(Br, color='grey', linestyle='-.', legend_label='orthogonalized', legend_color='grey') sage: pplot
Notice how not all orthogonalized (grey) vectors are touching a lattice “point”? Fortunately for us, GS is still pretty useful to understanding LLL so this is not a total loss.
LLL In-Relation to Gaussian Lattice Reduction
Unless you are basically a sorceress, I imagine you may find starting with 2-dimensional lattice basis reduction useful. Thankfully, we have Gauss’ algorithm for reducing bases in dimension 2. This is great for a couple reasons:
Gaussian lattice reduction is also pretty similar to LLL
Gaussian lattice reduction is analogous to both Euclid’s algorithm and LLL so it can act as a bridge between these two analogies
We can use 2D vectors which are easy to graph and visualize
Gauss’ algorithm is defined as follows:
def gauss_reduction(v1, v2): while True: if v2.norm() < v1.norm(): v1, v2 = v2, v1 # swap step m = round( (v1 * v2) / (v1 * v1) ) if m == 0: return (v1, v2) v2 = v2 - m*v1 # reduction step
Let’s break this down a bit.
gauss_reduction takes in two vectors that
represent our lattice basis. Ignoring the
while loop for a second, the
first step is the swap step. The swap step ensures that the length of
is smaller than
v2. Among other things, this will ensure that the result
gauss_reduction will be ordered according to length which helps with
some of the proofs that ensure this algorithm works well.
m is the scalar projection of
longer vector onto the shorter vector). This is the same scalar produced during
the GS process but in Gauss’s algorithm we round it to the nearest integer so
we ensure we are still working with vectors within the lattice. This is a
important idea, so lets visualize this process.
We first project
v1. Here is what the projected vector looks
like before and after rounding.
We then compute our new reduced vector
v2 - m*v1. Here is the reduction step
before and after rounding
Cool, huh? By rounding
m prior to reduction we have sorta “knocked over” our
new reduced vector
v2 so it becomes a vector in the basis. What is
interesting is that the reduced
v2 appears to have a shorter length than
v2. That is not a coincidence, that is a guarantee of the reduction
step! Additionally, the resultant basis vectors will be “nearly” orthogonal,
which is exactly what we want. I don’t want to bog anyone down with details for
why this works just yet, but if you are impatient you can jump to the more
For now, lets just wave our hands and say
gauss_reduction will terminate, and
return a “good” (i.e. short and nearly orthogonal) basis for dimension 2 bases.
LLL extends Gauss’ algorithm for reduction to work with
n vectors. At a
high-level, LLL iterates through the input basis vectors and performs a length
reduction to each vector (this is close to Gauss’ algorithm at this point).
However, we are dealing with
n vectors instead of just two so we need a way
to ensure that the ordering of the input basis doesn’t affect our result.
To assist with ordering the reduced basis by length, LLL uses a heuristic
called the Lovász condition to determine if vectors in the input basis need to
be swapped. LLL returns after all basis vectors have gone through at least one
reduction, and the new basis is roughly ordered by length.
A Deeper Dive into LLL
To help understand the mechanics of LLL, we can dig into an implementation of the algorithm. Here is some python pseudocode repurposed from wikipedia:
def LLL(B, delta): Q = gram_schmidt(B) def mu(i,j): v = B[i] u = Q[j] return (v*u) / (u*u) n, k = B.nrows(), 1 while k < n: # length reduction step for j in reversed(range(k)): if abs(mu(k,j)) > .5: B[k] = B[k] - round(mu(k,j))*B[j] Q = gram_schmidt(B) # swap step if Q[k]*Q[k] >= (delta - mu(k,k-1)**2)*(Q[k-1]*Q[k-1]): k = k + 1 else: B[k], B[k-1] = B[k-1], B[k] Q = gram_schmidt(B) k = max(k-1, 1) return B
LLL is pretty concise, but there is definitely some seemingly magical logic at
first glance. Let’s start with the
mu is a function that produces a scalar for vector reduction. Its
similar to the scalar projections from Gram-Schmidt orthogonalization and
Gaussian reduction. The only major difference is
mu is not projecting a
lattice vector onto a vector that is also in the lattice (like Gauss’ algorithm
mu is projecting a lattice vector onto a GS orthogonalized vector.
In other words, suppose
B is our input basis and
Q is the result of
Gram-Schmidt applied to
B (without normalization). The constant produced by
mu(i,j) is the scalar projection of the
ith lattice basis vector (
jth Gram-Schmidt orthogonalized basis vector (
Q[j]). The GIF below
is a brief demonstration of
We already know that just applying GS won’t necessarily give us a basis for our
lattice, but it does give us the “ideal” orthogonalized matrix. Since we
cannot use the “ideal” matrix directly, I like to think of
mu as leveraging
the GS orthogonalized matrix as a reference to how successful reduction is.
Isolating the length reduction pseudocode from LLL, we have:
1. n, k = B.nrows(), 1 2. # outer loop condition 3. while k < n: 4. # length reduction loop 5. for j in reversed(range(k)): 6. if abs(mu(k,j)) > .5: 7. # reduce B[k] 8. B[k] = B[k] - round(mu(k,j))*B[j] 9. # re-calculate GS with new basis B 10. Q = gram_schmidt(B)
At line 1, we establish two variables:
n: which is a constant that holds the number of rows in the basis
k: which keeps track of the index of the vector we are focusing on
The outer loop condition is less of a concern during the length reduction step,
we can head straight to the length reduction loop. The length reduction loop
iterates through the
k-1th basis vector towards the
0th basis vector and
checks if the absolute value of
mu(k,j) is greater than
significant for since we are rounding the value of
mu(k,j), if its absolute
value is less than
1/2 then we would be subtracting a zero vector which
wouldn’t reduce the vector. We could remove that
if statement on line 6 but
we would then have superfluous assignments (i.e.
B[k] = B[k]) for some
The length reduction step is basically Gram-Schmidt’s reduction step with
minor modifications. Here is another way to write the LLL length reduction step
if condition) for each vector in a length reduction loop:
B = B B = B - round(mu(1, 0))*B B = B - round(mu(2, 1))*B - round(mu(2, 0))*B ... B[k] = B[k] - round(mu(k, k-1))*B[k-1] - round(mu(k, k-2))*B[k-2] - ... - round(mu(k, 0))*B
Just for comparison here is how Gram-Schmidt vectors are calculated:
Q = B Q = B - mu(1, 0)*Q Q = B - mu(2, 1)*Q - mu(2, 0)*Q ... Q[k] = B[k] - mu(k, k-1)*Q[k-1] - mu(k, k-2)*Q[k-2] - ... - mu(k, 0)*Q
And finally, after we modify our basis
B, we need to keep our orthogonalized
basis up-to-date. In line 10, we update
Q. This is definitely not the
most efficient way to keep
Q up-to-date, but I just copied Wikipedia here so ¯\(ツ)/¯.
The Lovász Condition and the Swap Step
Extracting the “swap” step from our Wikipedia pseudocode gets us:
1. # swap step 2. if Q[k]*Q[k] >= (delta - mu(k,k-1)**2)*(Q[k-1]*Q[k-1]): 3. k = k + 1 4. else: 5. B[k], B[k-1] = B[k-1], B[k] 6. Q = gram_schmidt(B) 7. k = max(k-1, 1) 8. 9. return B
At this point, the algorithm has finished a round of length-reduction. From
there LLL must determine what vector to focus on next. The Lovász condition
will tell us whether to continue on to the next basis vector (Line 3), or
whether to place the
k‘th basis vector in position
k-1 (Lines 5-7).
Putting the meaning of the Lovász condition aside for now, this swap step is
reminiscent of a sorting algorithm. Recall that
k is the index of the basis
vector that LLL is “focusing on”. Suppose LLL is at the
kth vector and the
Lovász condition is true, from there LLL just moves onto the
(Line 3). At this stage, LLL is basically saying that the
vectors are roughly sorted by length (this may change after the next round of
reductions though). If the Lovász condition is false, the
kth basis vector is
placed at position
k-1 (Line 5), and LLL then re-focuses (Line 7) on the same
vector which is now at position
k-1. Another reduction round is done, and
then back to the swap step. This brings about another way to describe LLL: LLL
is a vector sorting algorithm that occasionally screws up the ordering by
making vectors smaller so it has to re-sort.
What about the Lovász condition, itself? As I said earlier, it’s basically a heuristic that determines if the vectors are in a “good” order. Aside from that, I don’t have too much to add here unfortunately as my intuition is still shaky. There are multiple equivalent representations for the Lovász and none of them have really felt 100% right to me. The best explanations that I have found thus far are:
“An Introduction to Mathematical Cryptography” discusses an equivalent representation of the Lovász condition that is a comparison of the lengths of the projection of basis vectors onto the orthogonal complement of a space spanned by a subset of basis vectors.
If anyone reading this has a link to a decently intuitive explanation or can describe the Lovász in a memorable way please let me know!
Things that Stumped Me When Learning LLL
Is the Gaussian length-reduction step guaranteed to provide short and nearly orthogonal vectors?
If you have not made the connection yet, the
m value from Gaussian reduction
is the nearest integer of the
mu value from Gram-Schmidt. It may not seem
obvious that rounding the result of
mu and using as a scalar would produce a
good basis. In Gram-Schmidt using the exact value of
mu allows for the ideal
decomposition of a vector, which can be used to orthogonalize the vector in
relation to the other basis vectors. With Gaussian reduction, we are rounding
that result which could affect the orthogonality. Fortunately, even after
mu the length reduction step will produce “nearly orthogonal”
In fact, a new length-reduced vector
B[k] = B[k] -
round(mu(i,j))*B[j] will always have an angle with
B[j] that lies between 60
and 120 degrees. This is guarantee that stems from another fact that
B[j] will always lie between
Why is the latter fact true? Intuitively, we have removed all possible
integer components of
B[k], therefore, the resultant
B[j] must lie between
Suppose we have just reduced
B[k], so we know the projection of
B[j] will lie between
1/2*B[j]. Using the definition of
vector projection (see image below), we can re-write our inequality:
|B[k]|*cos(theta) = proj B[k] onto B[j] => |B[k]|*abs(cos(theta)) <= 1/2*|B[j]| (|B[k]|/|B[j]|)*abs(cos(theta)) <= 1/2
I alluded to this earlier, but Gaussian two dimensional reduction will make it
such that the reduced vector
B[k] is shorter than
B[j] (there is a proof of
this in “Introduction to Mathematical Cryptography”). According to this
these two facts prove the “nearly” orthogonal property of the output vectors.
I am actually not 100% sure these proofs are correct (I think they made a
mistake but I don’t have a proof either) so I cannot say much more than that.
Why doesn’t Gaussian lattice reduction easily generalize to work with lattices with more than 2 vectors?
I found the explanation from “Mathematics of Public Key Cryptography” to explain this well.
Finally, we remark that there is a natural analogue of [Gaussian Reduction] for any dimension. Hence, it is natural to try to generalise the Lagrange-Gauss algorithm to higher dimensions. Generalisations to dimension three have been given by Vall´ee and Semaev. Generalising to higher dimensions introduces problems. For example, choosing the right linear combination to size reduce bn using b1,...,bn−1 is solving the CVP in a sublattice (which is a hard problem). Furthermore, there is no guarantee that the resulting basis actually has good properties in high dimension. We refer to Nguyen and Stehl´e for a full discussion of these issues and an algorithm that works in dimensions 3 and 4
How does LLL overcome this? I like to think that LLL breaks an n-dimensional problem down into a bunch of 2-dimensional cases and works a little at a time. That technique, plus length-sorting approximations, seems to work well in practice.
How does LLL use GS as a guide?
Again, quoting others that are smarter than I am:
As we have noted in Example 16.3.3, computational problems in lattices can be easy if one has a basis that is orthogonal, or “sufficiently close to orthogonal”. A simple but important observation is that one can determine when a basis is close to orthogonal by considering the lengths of the Gram-Schmidt vectors. More precisely, a lattice basis is “close to orthogonal” if the lengths of the Gram-Schmidt vectors do not decrease too rapidly.