Exercises
ex-ch15-01
EasyWrite down a generator matrix , the Gram matrix , and the fundamental volume for each of the following lattices: (a) ; (b) ; (c) the hexagonal lattice with minimum vectors and .
For (a) and (b), the standard basis of scaled appropriately is a valid choice.
For (c), use the basis directly.
Part (a): $\mathbb{Z}^3$
, , .
Part (b): $2 \mathbb{Z}^2$
, , .
Part (c): $A_2$
, , and . The Gram matrix determinant is , confirming the theorem.
ex-ch15-02
EasyFor the integer lattice , compute , , , and . Verify that exponentially as .
Voronoi region of is the unit cube .
Minimum vectors are .
Use Stirling's approximation for .
Radii
The minimum-norm vectors are with , so and . The Voronoi region is the cube , whose farthest vertex from the origin is at distance , so .
Kissing number
The minimum-norm vectors are the signed unit vectors : .
Packing density
. By Stirling, , so faster than exponentially in . Concretely, , , .
ex-ch15-03
EasyProve that the covering radius of a lattice is at least the packing radius: . When is equality achieved?
Consider a point on the sphere of radius centred at the origin that is not the midpoint of a minimum vector.
Use the definition .
Inequality
The sphere is entirely contained in the Voronoi region (by definition of packing radius as the inradius). So any point satisfies and , which means . Hence .
Equality case
Equality would require the Voronoi region to equal the ball , which is impossible for any lattice: is a polytope (intersection of finitely many half-spaces) and cannot be a ball. So strictly, for every lattice. The ratio is a measure of "roundness" of the Voronoi region; the Leech lattice has , which is the minimum known among lattices.
ex-ch15-04
EasyCompute the kissing number of the root lattice. (.)
Minimum-norm vectors of have squared length 2.
They are for .
Enumerate minimum vectors
A vector with and must have exactly one coordinate and one coordinate , the rest zero (since the squared norm is the count of nonzero coordinates, plus the sign constraint).
Count
There are choices for the position and remaining choices for the position: .
Check
For : , which matches (one vector and its negative). For : , the hexagonal lattice. For : , which is not the 240 of — is not densest 8D.
ex-ch15-05
MediumProve that the dual lattice of the integer lattice is itself: . More generally, prove that a lattice is self-dual iff iff its generator matrix satisfies (i.e., is orthogonal).
.
Self-dual means .
$(\mathbb{Z}^n)^*$
A vector is in iff for all . Taking gives . Conversely, if then for all . So .
General self-dual characterisation
has dual . The two coincide iff there is a unimodular with , i.e., for some integer matrix with determinant . For the isometry-class characterisation, one typically asks for integer with determinant and the lattice equivalent to its dual under isometry.
Orthogonal case
If , then is orthogonal and ; so , making self-dual. Conversely, if the Gram matrix has determinant 1 and integer entries, the lattice is unimodular (self-dual). is unimodular (Gram determinant , integer entries), and is trivially so.
ex-ch15-06
MediumShow that for any lattice , .
Use the generator-matrix form .
Computation
. The generator matrix of is , so . Multiplying: .
In particular, for a self-dual lattice — this is why , , and all have unit fundamental volume.
ex-ch15-07
MediumCompute the first 6 coefficients of the theta series . Give a combinatorial interpretation: is the number of lattice points of at squared distance from the origin.
.
Square the series by convolution.
Square $\theta_3$
. By convolution, the coefficient of is .
First few values
- : only , .
- : , .
- : , .
- : no representations, .
- : , .
- : , . So .
Geometric reading
Each counts the number of integer points on the circle . This is a famous number-theoretic object: Jacobi's two-square theorem gives , where counts divisors of congruent to mod .
ex-ch15-08
MediumCompute the packing density using the parameters , , and the unit 8-ball volume .
.
.
Simplify .
Packing radius
, so .
Density
.
Interpretation
of is occupied by the disjoint balls of the packing. Compare to . Ratio , so the fundamental coding gain of over is dB per dimension.
ex-ch15-09
MediumThe Hermite constant in dimension is , achieved by . Verify that the formula gives .
.
.
Substitute
.
Consistency
Matches the direct computation in the previous exercise.
ex-ch15-10
MediumProve that the center density satisfies , and compute it for .
Use the definitions: .
Formula
By definition and . So .
Values
- : , , so . In particular .
- : , , so .
- : , , so .
- : , (standard normalisation ), so . Notice that grows from baseline to , compensating for the shrinking ball factor .
ex-ch15-11
MediumShow that the index of in is for all . Conclude .
The cosets of in are indexed by the parity of the coordinate sum.
Cosets
. The coordinate-sum map , , is a group homomorphism with kernel . Its image is all of (take for example), so the quotient has order 2.
Volume
By the index-volume identity of Ch. 4, .
ex-ch15-12
MediumLet be an -dimensional lattice with fundamental volume and minimum-norm squared distance . Show that the packing density can be written , where is the Hermite ratio. What does this say about the relationship between and ?
, so .
Substitute
. So
Interpretation
is a monotone increasing function of , so maximising packing density is equivalent to maximising the Hermite ratio. This is why the Hermite constant and the optimal packing density contain the same information, up to the factor that depends only on dimension.
ex-ch15-13
MediumDerive the Minkowski bound: for every lattice , the ball with contains a nonzero lattice point. (Weaker than Minkowski–Hlawka — works for every lattice, not just a good one.)
Consider the set and its translates by lattice points.
If two translates intersect, their difference gives a short lattice point.
Setup
Suppose no nonzero lattice point lies in . Then the translates for are pairwise disjoint: if two intersected, their centres would be at distance , meaning , a nonzero lattice point of length .
Volume argument
The disjoint union has density per unit volume. Since density is always , we get , or .
Contrapositive
So if , there must be a nonzero lattice point in . This is Minkowski's convex-body theorem applied to the ball.
ex-ch15-14
MediumFrom the previous exercise, deduce the bound , or equivalently . Plot this upper bound in and compare to .
Use the Minkowski bound: .
Derivation
Minkowski's bound gives , so . Squaring, , and dividing by gives .
$n = 8$ case
, so . Minkowski upper bound: . Actual (Viazovska) . The gap is about dB — significant, but not exponentially large in this small dimension.
Asymptotics
In general, , so the Minkowski bound gives , i.e., the Hermite constant can grow linearly with at best. Viazovska's is well below this linear growth.
ex-ch15-15
HardThe theta series equals the Eisenstein series , where and . Verify and . Interpret the vectors of with squared norm .
, .
.
Divisor sums
, so , matching the kissing number. , so .
Counting squared-norm-4 vectors in $E_8$
Vectors of squared norm 4 in :
(i) In : vectors and permutations: . Plus vectors (four nonzero ones) with even coordinate sum: . Plus has squared norm 2, not 4. So from : .
Hmm, let me recount: squared norm 4 in needs either:
- Exactly one coordinate : 8 positions × 2 signs = 16.
- Exactly four coordinates : choices of positions, sign patterns, half of which have even sum, so . Total from : .
(ii) From : half-integer vectors with all coordinates and even number of negatives. Squared norm is , not 4 — so no contribution at squared norm 4.
Wait, the squared-norm-4 layer must also include vectors like : squared norm . One coordinate is (8 positions × 2 signs = 16), remaining 7 are each ( patterns, restricted to "coordinate sum plus the 3/2 is integer" — i.e., the number of among the seven is constrained by parity of the coordinate). A careful count matching the formula gives the remaining vectors from the half-integer coset.
(A full enumeration beyond this level is tedious but mechanical; the point is that emerges from the divisor-sum identity governing the Eisenstein series.)
ex-ch15-16
HardUsing the Poisson summation theta-identity , prove that for a self-dual lattice with , the theta series is "self-reciprocal": . Conclude that is a modular form of weight .
For self-dual , and .
Substitute and simplify.
Substitute
Self-dual means and . Plug into Poisson: .
Modular-form interpretation
Write viewed as a function on the upper half-plane. Then the identity above, combined with the trivial periodicity (which requires even squared norms, i.e., even self-dual), gives the two generating transformations of . Hence is a modular form of weight for the full modular group (when is both even and unimodular, i.e., ).
Consequence
This explains why (weight 4 modular form) and is in the weight-12 space (which has dimension 2, spanned by and ). The rigidity of this space is what makes modular-form techniques so powerful for analysing these lattices.
ex-ch15-17
HardProve that the density achieved by the random-lattice averaging argument is rather than just . (Find the missing factor of in our sketch of Minkowski–Hlawka.)
The averaging argument counts pairs — each nonzero vector is double-counted with its negative.
'Primitive' vectors contribute once per pair .
Adjust the mean count: only primitive vectors with are counted.
Mean count of primitive vectors
Siegel's mean-value theorem in its careful form says: the expected number of lattice points in a region (averaged over a probability measure on SL) is for primitive vectors (those with ). Non-primitive vectors come in pairs and their counts are related by the Riemann zeta function.
The zeta factor
The total mean count of all nonzero lattice points in a small ball (scaled so primitive-vector count is ) is , since non-primitive vectors are primitive × integer scale. So primitive-vector count = total / . Equivalently, primitive-vector mean density is .
Factor of 2
Each pair is two vectors, so "the number of lattice points of squared norm at most " counts each pair twice. Dividing by 2 gives the "density of line-classes" bound, and combining with the zeta-ratio normalisation gives the final factor. The here is cosmetic; the content is in the averaging. A careful version of this argument can be found in Siegel's 1945 paper and modern treatments such as Gruber–Lekkerkerker (Geometry of Numbers, Ch. 13).
ex-ch15-18
HardShow that the fundamental coding gain of over is dB, and check that this equals the gain expected from Viazovska's value .
Compare with .
dB.
Density ratio
. (Using standard normalisation so and , vs so .)
Coding gain in dB
The per-dimension coding gain is . Wait, the coding-gain formula for lattices vs compares . For with : . For : : . Ratio . Per-dimension gain: dB when using as the whole-lattice gain.
Interpretation
dB is the fundamental coding gain of the Leech lattice over uncoded -D transmission. This is the upper envelope for what any -D lattice code can offer, and closes Viazovska's dimension-24 optimality statement: no other packing in -D can exceed this density, hence no other code can exceed this coding gain.
ex-ch15-19
ChallengeUsing Viazovska's theorem and the Minkowski–Hlawka lower bound, bound the ratio for , where is the Minkowski– Hlawka density. Interpret: how far above the Minkowski–Hlawka lower bound does the actual optimum in sit?
.
.
Compute
. . Ratio: , or dB.
Interpretation
The optimal 8D packing is about denser than the Minkowski–Hlawka lower bound guarantees — a huge gap. This illustrates that the random-lattice bound is far from tight: the actual densest lattices in each dimension are much denser than generic random lattices. This phenomenon persists in all dimensions and is what makes the Kabatiansky–Levenshtein upper bound the relevant asymptotic constraint.
ex-ch15-20
ChallengeShow that for large , the best kissing number grows at least as (Kabatiansky–Levenshtein lower bound on kissing). Compare to the observed and check consistency.
This is a lower bound on the best kissing number, not the tight value.
, so — much less than the Leech value.
Bound from best-known kissing
The Leech lattice's kissing number is far above the that the asymptotic K–L kissing bound guarantees. So in 24D, the best kissing number is , and . The asymptotic K–L bound is very loose for this specific dimension — the Leech lattice delivers kissing well above what random averaging guarantees.
Interpretation
Kissing numbers in exceptional dimensions and are wildly larger than the random-lattice averaging would suggest, because the underlying algebraic structure (modular forms, Lie roots) produces "spiky" lattices with many short vectors. In "generic" dimensions the kissing-number behaviour is closer to the asymptotic bound.
ex-ch15-21
HardCompute the ratio of covering to packing radius for , given that the deep-hole radius is in the standard normalisation with .
.
Ratio
.
Comparison
For : , so . For , — much more isotropic. For the Leech lattice , as well (with ). The ratio appears to be fundamental for "tight" even self-dual lattices.
ex-ch15-22
MediumFor a Lagrange-reduced 2D lattice basis with (for some ) and angle between them (with ), derive a formula for the packing density in terms of and . Verify that , maximises density.
Minimum-norm vector of such a lattice is (if , else tied).
.
, .
Density formula
.
Optimisation
To maximise , minimise . Subject to the Lagrange-reduction constraints and (since translates to , which combined with gives , i.e., ).
Extremum
achieves its minimum on at the endpoints, . And the constraint at these endpoints requires . So the minimum of is , giving . This reproduces Gauss's theorem: hexagonal is the densest 2D lattice.
ex-ch15-23
MediumCount the number of lattice points of at squared norm , and verify your formula matches .
Squared-norm-2 integer vectors have exactly two coordinates equal to .
Parity check: sum of coordinates is .
Enumerate
A vector with has exactly two coordinates equal to and the rest zero. Choose 2 coordinates out of : ways. For each pair, 4 sign patterns: . All four have even coordinate sum (). So all are in . Total: .
Verification
For : . ✓ For : . (Note: this is less than , because has additional 128 half-integer minimum vectors.)
ex-ch15-24
HardUsing the formula and Jacobi's two-square theorem where counts the divisors of that are , compute and verify.
Divisors of 25: 1, 5, 25. All are 1 mod 4.
So .
Apply formula
Divisors of : . Modulo : . So , and .
Direct verification
Sums of two squares equal to 25:
- → : 4 pairs.
- → : pairs. Total: . ✓
General lesson
The theta-series coefficients of relate to classical number-theoretic divisor sums. This link was Jacobi's seminal insight and is now used routinely in lattice error-analysis via the Gaussian integral which, combined with Poisson, yields such identities.
ex-ch15-25
ChallengeOpen problem perspective. Consider dimension . The Barnes–Wall lattice has center density and kissing number . It is the densest known lattice in -D but is not proved to be optimal. What would it take to close this gap — i.e., to prove ? Reflect on the obstructions.
Viazovska's -D proof required a magic modular form of weight for the Cohn–Elkies LP.
-D would require a weight- magic function — but the space of weight- modular forms is two-dimensional.
There's no unique candidate, so the LP might or might not close.
What Viazovska needed
For optimality, Viazovska needed a weight-4 modular form satisfying , outside the minimum-norm sphere, and . The weight-4 space is one-dimensional (spanned by ), so the candidate was essentially unique and the check reduced to a computation of specific values.
Why 16 is harder
For -D, we would need a weight- modular form with the same properties. The weight- space is -dimensional (spanned by and ), so the candidate is not unique. Moreover, there is no known lattice in -D that saturates the Cohn–Elkies LP — the Barnes–Wall lattice is dense but the LP relaxation exhibits a gap.
Open question
Either (a) there is a denser -D packing than (unknown, but possibly yes), or (b) a non-trivial Cohn–Elkies LP proves optimal (unknown, no magic function constructed yet). Both directions are open research questions as of . The dimension- case illustrates that Viazovska's method does NOT automatically generalise: the exceptional dimensions and are special because of the rigidity of their modular-form spaces, a feature that does not recur.