Lattice Points in Polytopes
Richard P. Stanley
M.I.T.
Lattice Points in Polytopes – p. 1
A lattice polygon
Georg Alexander Pick (1859–1942)
P : lattice polygon in R2
(vertices ∈ Z2 , no self-intersections)
Lattice Points in Polytopes – p. 2
Boundary and interior lattice points
Lattice Points in Polytopes – p. 3
Pick’s theorem
A = area of P
I = # interior points of P (= 4)
B = #boundary points of P (= 10)
Then
2I + B − 2
A=
.
2
Lattice Points in Polytopes – p. 4
Pick’s theorem
A = area of P
I = # interior points of P (= 4)
B = #boundary points of P (= 10)
Then
2I + B − 2
A=
.
2
Example on previous slide:
2 · 4 + 10 − 2
= 9.
2
Lattice Points in Polytopes – p. 4
Two tetrahedra
Pick’s theorem (seemingly) fails in higher
dimensions. For example, let T1 and T2 be the
tetrahedra with vertices
v(T1 ) = {(0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1)}
v(T2 ) = {(0, 0, 0), (1, 1, 0), (1, 0, 1), (0, 1, 1)}.
Lattice Points in Polytopes – p. 5
Failure of Pick’s theorem in dim 3
Then
I(T1 ) = I(T2 ) = 0
B(T1 ) = B(T2 ) = 4
A(T1 ) = 1/6,
A(T2 ) = 1/3.
Lattice Points in Polytopes – p. 6
Polytope dilation
Let P be a convex polytope (convex hull of a
finite set of points) in Rd . For n ≥ 1, let
nP = {nα : α ∈ P}.
Lattice Points in Polytopes – p. 7
Polytope dilation
Let P be a convex polytope (convex hull of a
finite set of points) in Rd . For n ≥ 1, let
nP = {nα : α ∈ P}.
P
3P
Lattice Points in Polytopes – p. 7
i(P, n)
Let
i(P, n) = #(nP ∩ Zd )
= #{α ∈ P : nα ∈ Zd },
the number of lattice points in nP.
Lattice Points in Polytopes – p. 8
ī(P, n)
Similarly let
P ◦ = interior of P = P − ∂P
ī(P, n) = #(nP ◦ ∩ Zd )
= #{α ∈ P ◦ : nα ∈ Zd },
the number of lattice points in the interior of nP.
Lattice Points in Polytopes – p. 9
ī(P, n)
Similarly let
P ◦ = interior of P = P − ∂P
ī(P, n) = #(nP ◦ ∩ Zd )
= #{α ∈ P ◦ : nα ∈ Zd },
the number of lattice points in the interior of nP.
Note. Could use any lattice L instead of Zd .
Lattice Points in Polytopes – p. 9
An example
P
3P
i(P, n) = (n + 1)2
ī(P, n) = (n − 1)2 = i(P, −n).
Lattice Points in Polytopes – p. 10
Reeve’s theorem
lattice polytope: polytope with integer vertices
Theorem (Reeve, 1957). Let P be a
three-dimensional lattice polytope. Then the
volume V (P) is a certain (explicit) function of
i(P, 1), ī(P, 1), and i(P, 2).
Lattice Points in Polytopes – p. 11
The main result
Theorem (Ehrhart 1962, Macdonald 1963). Let
P = lattice polytope in RN , dim P = d.
Then i(P, n) is a polynomial (the Ehrhart
polynomial of P) in n of degree d.
Lattice Points in Polytopes – p. 12
Reciprocity and volume
Moreover,
i(P, 0) = 1
ī(P, n) = (−1)d i(P, −n), n > 0
(reciprocity).
Lattice Points in Polytopes – p. 13
Reciprocity and volume
Moreover,
i(P, 0) = 1
ī(P, n) = (−1)d i(P, −n), n > 0
(reciprocity).
If d = N then
i(P, n) = V (P)nd + lower order terms,
where V (P) is the volume of P.
Lattice Points in Polytopes – p. 13
Eugène Ehrhart
April 29, 1906: born in Guebwiller, France
1932: begins teaching career in lycées
1959: Prize of French Sciences Academy
1963: begins work on Ph.D. thesis
1966: obtains Ph.D. thesis from Univ. of
Strasbourg
1971: retires from teaching career
January 17, 2000: dies
Lattice Points in Polytopes – p. 14
Photo of Ehrhart
Lattice Points in Polytopes – p. 15
Self-portrait
Lattice Points in Polytopes – p. 16
Generalized Pick’s theorem
Corollary. Let P ⊂ Rd and dim P = d. Knowing
any d of i(P, n) or ī(P, n) for n > 0 determines
V (P).
Lattice Points in Polytopes – p. 17
Generalized Pick’s theorem
Corollary. Let P ⊂ Rd and dim P = d. Knowing
any d of i(P, n) or ī(P, n) for n > 0 determines
V (P).
Proof. Together with i(P, 0) = 1, this data
determines d + 1 values of the polynomial i(P, n)
of degree d. This uniquely determines i(P, n)
and hence its leading coefficient V (P). Lattice Points in Polytopes – p. 17
An example: Reeve’s theorem
Example. When d = 3, V (P) is determined by
i(P, 1) = #(P ∩ Z3 )
i(P, 2) = #(2P ∩ Z3 )
ī(P, 1) = #(P ◦ ∩ Z3 ),
which gives Reeve’s theorem.
Lattice Points in Polytopes – p. 18
Birkhoff polytope
Example. Let BM ⊂ RM ×M be the Birkhoff
polytope of all M × M doubly-stochastic
matrices A = (aij ), i.e.,
aij ≥ 0
X
aij = 1 (column sums 1)
X
aij = 1 (row sums 1).
i
j
Lattice Points in Polytopes – p. 19
(Weak) magic squares
Note. B = (bij ) ∈ nBM ∩ ZM ×M if and only if
bij ∈ N = {0, 1, 2, . . . }
X
bij = n
X
bij = n.
i
j
Lattice Points in Polytopes – p. 20
Example of a magic square





2
3
1
1
1
1
3
2
0
1
2
4
4
2
1
0





(M = 4, n = 7)
Lattice Points in Polytopes – p. 21
Example of a magic square





2
3
1
1
1
1
3
2
0
1
2
4
4
2
1
0





(M = 4, n = 7)
∈ 7B4
Lattice Points in Polytopes – p. 21
HM (n)
HM (n) := #{M × M N-matrices, line sums n}
= i(BM , n)
Lattice Points in Polytopes – p. 22
HM (n)
HM (n) := #{M × M N-matrices, line sums n}
= i(BM , n)
H1 (n) = 1
H2 (n) = ??
Lattice Points in Polytopes – p. 22
HM (n)
HM (n) := #{M × M N-matrices, line sums n}
= i(BM , n)
H1 (n) = 1
H2 (n) = n + 1
"
a
n−a
n−a
a
#
,
0 ≤ a ≤ n.
Lattice Points in Polytopes – p. 22
The case M = 3
n+2
n+3
n+4
H3 (n) =
+
+
4
4
4
(MacMahon)
Lattice Points in Polytopes – p. 23
Values for small n
HM (0) = ??
Lattice Points in Polytopes – p. 24
Values for small n
HM (0) = 1
Lattice Points in Polytopes – p. 24
Values for small n
HM (0) = 1
HM (1) = ??
Lattice Points in Polytopes – p. 24
Values for small n
HM (0) = 1
HM (1) = M ! (permutation matrices)
Lattice Points in Polytopes – p. 24
Values for small n
HM (0) = 1
HM (1) = M ! (permutation matrices)
Anand-Dumir-Gupta, 1966:
X
M ≥0
xM
HM (2) 2 =??
M!
Lattice Points in Polytopes – p. 24
Values for small n
HM (0) = 1
HM (1) = M ! (permutation matrices)
Anand-Dumir-Gupta, 1966:
ex/2
xM
HM (2) 2 = √
M!
1−x
M ≥0
X
Lattice Points in Polytopes – p. 24
Anand-Dumir-Gupta conjecture
Theorem (Birkhoff-von Neumann). The vertices
of BM consist of the M ! M × M permutation
matrices. Hence BM is a lattice polytope.
Lattice Points in Polytopes – p. 25
Anand-Dumir-Gupta conjecture
Theorem (Birkhoff-von Neumann). The vertices
of BM consist of the M ! M × M permutation
matrices. Hence BM is a lattice polytope.
Corollary (Anand-Dumir-Gupta conjecture).
HM (n) is a polynomial in n (of degree (M − 1)2 ).
Lattice Points in Polytopes – p. 25
H4(n)
1
Example. H4 (n) =
11n9 + 198n8 + 1596n7
11340
+7560n6 + 23289n5 + 48762n5 + 70234n4 + 68220n2
+40950n + 11340) .
Lattice Points in Polytopes – p. 26
Reciprocity for magic squares
Reciprocity ⇒ ±HM (−n) =
#{M ×M matrices B of positive integers, line sum n}
But every such B can be obtained from an
M × M matrix A of nonnegative integers by
adding 1 to each entry.
Lattice Points in Polytopes – p. 27
Reciprocity for magic squares
Reciprocity ⇒ ±HM (−n) =
#{M ×M matrices B of positive integers, line sum n}
But every such B can be obtained from an
M × M matrix A of nonnegative integers by
adding 1 to each entry.
Corollary.
HM (−1) = HM (−2) = · · · = HM (−M + 1) = 0
HM (−M − n) = (−1)M −1 HM (n)
Lattice Points in Polytopes – p. 27
Two remarks
Reciprocity greatly reduces computation.
Applications of magic squares, e.g., to
statistics (contingency tables).
Lattice Points in Polytopes – p. 28
Zeros of H9(n) in complex plane
Zeros of H_9(n)
3
2
1
–8
–6
–4
–2
0
–1
–2
–3
Lattice Points in Polytopes – p. 29
Zeros of H9(n) in complex plane
Zeros of H_9(n)
3
2
1
–8
–6
–4
–2
0
–1
–2
–3
No explanation known.
Lattice Points in Polytopes – p. 29
Zonotopes
Let v1 , . . . , vk ∈ Rd . The zonotope Z(v1 , . . . , vk )
generated by v1 , . . . , vk :
Z(v1 , . . . , vk ) = {λ1 v1 + · · · + λk vk : 0 ≤ λi ≤ 1}
Lattice Points in Polytopes – p. 30
Zonotopes
Let v1 , . . . , vk ∈ Rd . The zonotope Z(v1 , . . . , vk )
generated by v1 , . . . , vk :
Z(v1 , . . . , vk ) = {λ1 v1 + · · · + λk vk : 0 ≤ λi ≤ 1}
Example. v1 = (4, 0), v2 = (3, 1), v3 = (1, 2)
(1,2)
(3,1)
(0,0)
(4,0)
Lattice Points in Polytopes – p. 30
Lattice points in a zonotope
Theorem. Let
Z = Z(v1 , . . . , vk ) ⊂ Rd ,
where vi ∈ Zd . Then
i(Z, 1) =
X
h(X),
X
where X ranges over all linearly independent
subsets of {v1 , . . . , vk }, and h(X) is the gcd of all
j × j minors (j = #X) of the matrix whose rows
are the elements of X.
Lattice Points in Polytopes – p. 31
An example
Example. v1 = (4, 0), v2 = (3, 1), v3 = (1, 2)
(1,2)
(3,1)
(0,0)
(4,0)
Lattice Points in Polytopes – p. 32
Computation of i(Z, 1)
4 0
i(Z, 1) = 3 1
4 0
+
1 2
3 1
+
1 2
+gcd(4, 0) + gcd(3, 1)
+gcd(1, 2) + det(∅)
= 4+8+5+4+1+1+1
= 24.
Lattice Points in Polytopes – p. 33
Computation of i(Z, 1)
4 0
i(Z, 1) = 3 1
4 0
+
1 2
3 1
+
1 2
+gcd(4, 0) + gcd(3, 1)
+gcd(1, 2) + det(∅)
= 4+8+5+4+1+1+1
= 24.
Lattice Points in Polytopes – p. 33
Corollaries
Corollary. If Z is an integer zonotope generated
by integer vectors, then the coefficients of i(Z, n)
are nonnegative integers.
Lattice Points in Polytopes – p. 34
Corollaries
Corollary. If Z is an integer zonotope generated
by integer vectors, then the coefficients of i(Z, n)
are nonnegative integers.
Neither property is true for general integer
polytopes. There are numerous conjectures
concerning special cases.
Lattice Points in Polytopes – p. 34
The permutohedron
Πd = conv{(w(1), . . . , w(d)) : w ∈ Sd } ⊂ Rd
Lattice Points in Polytopes – p. 35
The permutohedron
Πd = conv{(w(1), . . . , w(d)) : w ∈ Sd } ⊂ Rd
dim Πd = d − 1, since
X
w(i) =
d+1
2
Lattice Points in Polytopes – p. 35
The permutohedron
Πd = conv{(w(1), . . . , w(d)) : w ∈ Sd } ⊂ Rd
dim Πd = d − 1, since
X
w(i) =
d+1
2
Πd ≈ Z(ei − ej : 1 ≤ i < j ≤ d)
Lattice Points in Polytopes – p. 35
Π3
123
132
213
Π3
222
231
312
321
Lattice Points in Polytopes – p. 36
Π3
123
132
213
Π3
222
231
312
321
i(Π3 , n) = 3n2 + 3n + 1
Lattice Points in Polytopes – p. 36
Π4
(truncated octahedron)
Lattice Points in Polytopes – p. 37
i(Πd, n)
Theorem. i(Πd , n) =
Pd−1
k
f
(d)x
, where
k=0 k
fk (d) = #{forests with k edges on vertices 1, . . . , d}
Lattice Points in Polytopes – p. 38
i(Πd, n)
Theorem. i(Πd , n) =
Pd−1
k
f
(d)x
, where
k=0 k
fk (d) = #{forests with k edges on vertices 1, . . . , d}
1
2
3
i(Π3 , n) = 3n2 + 3n + 1
Lattice Points in Polytopes – p. 38
Application to graph theory
Let G be a graph (with no loops or multiple
edges) on the vertex set V (G) = {1, 2, . . . , n}.
Let
di = degree (# incident edges) of vertex i.
Define the ordered degree sequence d(G) of G
by
d(G) = (d1 , . . . , dn ).
Lattice Points in Polytopes – p. 39
Example of d(G)
Example. d(G) = (2, 4, 0, 3, 2, 1)
1
2
3
4
5
6
Lattice Points in Polytopes – p. 40
# ordered degree sequences
Let f (n) be the number of distinct d(G), where
V (G) = {1, 2, . . . , n}.
Lattice Points in Polytopes – p. 41
f (n) for n ≤ 4
Example. If n ≤ 3, all d(G) are distinct, so
f (1) = 1, f (2) = 21 = 2, f (3) = 23 = 8. For n ≥ 4
we can have G 6= H but d(G) = d(H), e.g.,
1
2
1
2
1
2
3
4
3
4
3
4
In fact, f (4) = 54 < 26 = 64.
Lattice Points in Polytopes – p. 42
The polytope of degree sequences
Let conv denote convex hull, and
Dn = conv{d(G) : V (G) = {1, . . . , n}} ⊂ Rn ,
the polytope of degree sequences (Perles,
Koren).
Lattice Points in Polytopes – p. 43
The polytope of degree sequences
Let conv denote convex hull, and
Dn = conv{d(G) : V (G) = {1, . . . , n}} ⊂ Rn ,
the polytope of degree sequences (Perles,
Koren).
Easy fact. Let ei be the ith unit coordinate vector
in Rn . E.g., if n = 5 then e2 = (0, 1, 0, 0, 0). Then
Dn = Z(ei + ej : 1 ≤ i < j ≤ n).
Lattice Points in Polytopes – p. 43
The Erdős-Gallai theorem
Theorem. Let
α = (a1 , . . . , an ) ∈ Zn .
Then α = d(G) for some G if and only if
α ∈ Dn
a1 + a2 + · · · + an is even.
Lattice Points in Polytopes – p. 44
A generating function
Enumerative techniques leads to:
Theorem. Let
X
xn
F (x) =
f (n)
n!
n≥0
x2
x3
x4
= 1 + x + 2 + 8 + 54 + · · · .
2!
3!
4!
Then:
Lattice Points in Polytopes – p. 45
A formula for F (x)

X xn
1
1+2
nn
F (x) =
2
n!
n≥1
×
n
X
x
1−
(n − 1)n−1
n!
n≥1
n
x
× exp
nn−2
n!
n≥1
X
!1/2
!
#
+1
(00 = 1)
Lattice Points in Polytopes – p. 46
Coefficients of i(P, n)
Let P denote the tetrahedron with vertices
(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 13). Then
13 3
1
2
i(P, n) = n + n − n + 1.
6
6
Lattice Points in Polytopes – p. 47
The “bad” tetrahedron
y
x
z
Lattice Points in Polytopes – p. 48
The “bad” tetrahedron
y
x
z
Thus in general the coefficients of Ehrhart
polynomials are not “nice.” Is there a “better”
basis?
Lattice Points in Polytopes – p. 48
∗
The h -vector of i(P, n)
Let P be a lattice polytope of dimension d. Since
i(P, n) is a polynomial of degree d, ∃ hi ∈ Z such
that
d
h
+
h
x
+
·
·
·
+
h
x
0
1
d
n
i(P, n)x =
.
d+1
(1
−
x)
n≥0
X
Lattice Points in Polytopes – p. 49
∗
The h -vector of i(P, n)
Let P be a lattice polytope of dimension d. Since
i(P, n) is a polynomial of degree d, ∃ hi ∈ Z such
that
d
h
+
h
x
+
·
·
·
+
h
x
0
1
d
n
i(P, n)x =
.
d+1
(1
−
x)
n≥0
X
Definition. Define
h(P) = (h0 , h1 , . . . , hd ),
the h∗ -vector of P.
Lattice Points in Polytopes – p. 49
∗
Example of an h -vector
Example. Recall
1
i(B4 , n) =
(11n9
11340
+198n8 + 1596n7 + 7560n6 + 23289n5
+48762n5 + 70234n4 + 68220n2
+40950n + 11340).
Lattice Points in Polytopes – p. 50
∗
Example of an h -vector
Example. Recall
1
i(B4 , n) =
(11n9
11340
+198n8 + 1596n7 + 7560n6 + 23289n5
+48762n5 + 70234n4 + 68220n2
+40950n + 11340).
Then
h∗ (B4 ) = (1, 14, 87, 148, 87, 14, 1, 0, 0, 0).
Lattice Points in Polytopes – p. 50
∗
Elementary properties of h (P)
h∗0 = 1
h∗d = (−1)dim P i(P, −1) = I(P)
max{i : h∗i 6= 0} = min{j ≥ 0 :
i(P, −1) = i(P, −2) = · · · = i(P, −(d−j)) = 0}
E.g., h∗ (P) = (h∗0 , . . . , h∗d−2 , 0, 0) ⇔ i(P, −1) =
i(P, −2) = 0.
Lattice Points in Polytopes – p. 51
Another property
i(P, −n − k) = (−1)d i(P, n) ∀n ⇔
h∗i = h∗d+1−k−i ∀i, and
h∗d+2−k−i = h∗d+3−k−i = · · · = h∗d = 0
Lattice Points in Polytopes – p. 52
Back to B4
Recall:
h∗ (B4 ) = (1, 14, 87, 148, 87, 14, 1, 0, 0, 0).
Thus
i(B4 , −1) = i(B4 , −2) = i(B4 , −3) = 0
i(B4 , −n − 4) = −i(B4 , n).
Lattice Points in Polytopes – p. 53
∗
Main properties of h (P)
Theorem A (nonnegativity). (McMullen, RS)
h∗i ≥ 0.
Lattice Points in Polytopes – p. 54
∗
Main properties of h (P)
Theorem A (nonnegativity). (McMullen, RS)
h∗i ≥ 0.
Theorem B (monotonicity). (RS) If P and Q are
lattice polytopes and Q ⊆ P, then
h∗i (Q) ≤ h∗i (P) ∀i.
Lattice Points in Polytopes – p. 54
∗
Main properties of h (P)
Theorem A (nonnegativity). (McMullen, RS)
h∗i ≥ 0.
Theorem B (monotonicity). (RS) If P and Q are
lattice polytopes and Q ⊆ P, then
h∗i (Q) ≤ h∗i (P) ∀i.
B ⇒ A: take Q = ∅.
Lattice Points in Polytopes – p. 54
Proofs
Both theorems can be proved geometrically.
There are also elegant algebraic proofs based on
commutative algebra.
Lattice Points in Polytopes – p. 55
Further directions
I. Zeros of Ehrhart polynomials
Sample theorem (de Loera, Develin, Pfeifle,
RS). Let P be a lattice d-polytope. Then
i(P, α) = 0, α ∈ R ⇒ −d ≤ α ≤ ⌊d/2⌋.
Lattice Points in Polytopes – p. 56
Further directions
I. Zeros of Ehrhart polynomials
Sample theorem (de Loera, Develin, Pfeifle,
RS). Let P be a lattice d-polytope. Then
i(P, α) = 0, α ∈ R ⇒ −d ≤ α ≤ ⌊d/2⌋.
Theorem. Let d be odd. There exists a 0/1
d-polytope Pd and a real zero αd of i(Pd , n) such
that
1
αd
=
= 0.0585 · · · .
lim
d→∞ d
2πe
d odd
Lattice Points in Polytopes – p. 56
An open problem
Open. Is the set of all complex zeros of all
Ehrhart polynomials of lattice polytopes dense in
C? (True for chromatic polynomials of graphs.)
Lattice Points in Polytopes – p. 57
II. Brion’s theorem
Example. Let P be the polytope [2, 5] in R, so P
is defined by
(1) x ≥ 2,
(2) x ≤ 5.
Lattice Points in Polytopes – p. 58
II. Brion’s theorem
Example. Let P be the polytope [2, 5] in R, so P
is defined by
(1) x ≥ 2,
(2) x ≤ 5.
Let
2
t
F1 (t) =
tn =
1−t
n≥2
X
n∈Z
5
t
F2 (t) =
tn =
1.
1
−
t
n≤5
X
n∈Z
Lattice Points in Polytopes – p. 58
F1(t) + F2(t)
t2
t5
+
F1 (t) + F2 (t) =
1−t 1−
1
t
= t2 + t3 + t4 + t5
X
=
tm .
m∈P∩Z
Lattice Points in Polytopes – p. 59
Cone at a vertex
P: Z-polytope in RN with vertices v1 , . . . , vk
Ci : cone at vertex vi supporting P
Lattice Points in Polytopes – p. 60
Cone at a vertex
P: Z-polytope in RN with vertices v1 , . . . , vk
Ci : cone at vertex vi supporting P
v
C (v)
Lattice Points in Polytopes – p. 60
The general result
Let Fi (t1 , . . . , tN ) =
X
(m1 ,...,mN )∈Ci ∩ZN
mN
1
tm
·
·
·
t
1
N .
Lattice Points in Polytopes – p. 61
The general result
Let Fi (t1 , . . . , tN ) =
X
(m1 ,...,mN )∈Ci ∩ZN
mN
1
tm
·
·
·
t
1
N .
Theorem (Brion). Each Fi is a rational function
of t1 , . . . , tN , and
k
X
Fi (t1 , . . . , tN ) =
i=1
X
(m1 ,...,mN )∈P∩ZN
mN
1
tm
·
·
·
t
1
N
(as rational functions).
Lattice Points in Polytopes – p. 61
III. Toric varieties
Given an integer polytope P, can define a
projective algebraic variety XP , a toric variety.
Leads to deep connections with toric geometry,
including new formulas for i(P, n).
Lattice Points in Polytopes – p. 62
IV. Complexity
Computing i(P, n), or even i(P, 1) is
#P -complete. Thus an “efficient” (polynomial
time) algorithm is extremely unlikely. However:
Lattice Points in Polytopes – p. 63
IV. Complexity
Computing i(P, n), or even i(P, 1) is
#P -complete. Thus an “efficient” (polynomial
time) algorithm is extremely unlikely. However:
Theorem (A. Barvinok, 1994). For fixed dim P, ∃
polynomial-time algorithm for computing i(P, n).
Lattice Points in Polytopes – p. 63
V. Fractional lattice polytopes
Example. Let SM (n) denote the number of
symmetric M × M matrices of nonnegative
integers, every row and column sum n. Then
(
1
3
2
(2n
+
9n
+ 14n + 8), n even
8
S3 (n) =
1
3
2
(2n
+
9n
+ 14n + 7), n odd
8
1
=
(4n3 + 18n2 + 28n + 15 + (−1)n ).
16
Lattice Points in Polytopes – p. 64
V. Fractional lattice polytopes
Example. Let SM (n) denote the number of
symmetric M × M matrices of nonnegative
integers, every row and column sum n. Then
(
1
3
2
(2n
+
9n
+ 14n + 8), n even
8
S3 (n) =
1
3
2
(2n
+
9n
+ 14n + 7), n odd
8
1
=
(4n3 + 18n2 + 28n + 15 + (−1)n ).
16
Why a different polynomial depending on n
modulo 2?
Lattice Points in Polytopes – p. 64
The symmetric Birkhoff polytope
TM : the polytope of all M × M symmetric
doubly-stochastic matrices.
Lattice Points in Polytopes – p. 65
The symmetric Birkhoff polytope
TM : the polytope of all M × M symmetric
doubly-stochastic matrices.
M ×M
Easy fact: SM (n) = # nTM ∩ Z
Lattice Points in Polytopes – p. 65
The symmetric Birkhoff polytope
TM : the polytope of all M × M symmetric
doubly-stochastic matrices.
M ×M
Easy fact: SM (n) = # nTM ∩ Z
Fact: vertices of TM have the form 21 (P + P t ),
where P is a permutation matrix.
Lattice Points in Polytopes – p. 65
The symmetric Birkhoff polytope
TM : the polytope of all M × M symmetric
doubly-stochastic matrices.
M ×M
Easy fact: SM (n) = # nTM ∩ Z
Fact: vertices of TM have the form 21 (P + P t ),
where P is a permutation matrix.
Thus if v is a vertex of TM then 2v ∈ ZM ×M .
Lattice Points in Polytopes – p. 65
SM (n) in general
Theorem. There exist polynomials PM (n) and
QM (n) for which
SM (n) = PM (n) + (−1)n QM (n), n ≥ 0.
M
Moreover, deg PM (n) = 2 .
Lattice Points in Polytopes – p. 66
SM (n) in general
Theorem. There exist polynomials PM (n) and
QM (n) for which
SM (n) = PM (n) + (−1)n QM (n), n ≥ 0.
M
Moreover, deg PM (n) = 2 .
Difficult result (W. Dahmen and C. A. Micchelli,
1988):
(
n−1
− 1, n odd
2
deg QM (n) =
n−2
− 1, n even.
2
Lattice Points in Polytopes – p. 66
The last slide
Lattice Points in Polytopes – p. 67
The last slide
Lattice Points in Polytopes – p. 68
The last slide
Lattice Points in Polytopes – p. 68