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Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic combinatorics applied tofinite geometry
John Bamberg
Centre for the Mathematics of Symmetry and Computation,The University of Western Australia
December 1, 2011
,
= 2
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Graphs vs Linear algebra
Euler’s Theorem on latin squares
Finite geometry
The bigger picture
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Graphs
• Vertices
• Edges: pairs of vertices (u, v)
Degree: 3
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Graphs
• Vertices
• Edges: pairs of vertices (u, v)
Degree: 3
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Good labellings
• Assign a real number to each vertex.
• For each vertex, sum the values of adjacent vertices.
• Goal: Sum at each vertex should be a common multiple of thevalue at the vertex.
?1
?
1?
1
?
1
?
1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Good labellings
• Assign a real number to each vertex.
• For each vertex, sum the values of adjacent vertices.
• Goal: Sum at each vertex should be a common multiple of thevalue at the vertex.
?1
?
1?
1
?
1
?
1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Good labellings
• Assign a real number to each vertex.
• For each vertex, sum the values of adjacent vertices.
• Goal: Sum at each vertex should be a common multiple of thevalue at the vertex.
?1
?
1?
1
?
1
?
1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Good labellings
• Assign a real number to each vertex.
• For each vertex, sum the values of adjacent vertices.
• Goal: Sum at each vertex should be a common multiple of thevalue at the vertex.
?1
?
1?
1
?
1
?
1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Good labellings
• Assign a real number to each vertex.
• For each vertex, sum the values of adjacent vertices.
• Goal: Sum at each vertex should be a common multiple of thevalue at the vertex.
−11
−1
1−1
1
−1
1
−1
1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
1
1
?
?
??
?
1
1 ?
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
1
1
-23
-23
-23-23
-23
1
1 -23
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Linear algebra
Adjacency relation
Let V be the vertex-set of a graph.u ∼ v
Adjacency operator A on RV
Given f : V → R, we define
Af : V → R
: v 7→∑u∼v
f (u)
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Eigenvectors of A
11
1
1
11
1
1
1
1
−11
−1
1
−11
−1
1
−1
1
1
1
- 23
- 23
- 23
- 23
- 23
1
1 - 23
3 1 −2
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic graph theory
• Subject grew in the 1950’s and ‘60’s:
• Graph is regular if 1 is an eigenvector.
• #Edges = 12
∑λ2i
• #Triangles = 16
∑λ3i
• 3 distinct eigenvalues −→ strongly regular
• Smallest eigenvalue −→ independence and chromatic numbers
• Second largest eigenvalue −→ expansion and randomness properties
• Interlacing −→ substructures.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic graph theory
• Subject grew in the 1950’s and ‘60’s:
• Graph is regular if 1 is an eigenvector.
• #Edges = 12
∑λ2i
• #Triangles = 16
∑λ3i
• 3 distinct eigenvalues −→ strongly regular
• Smallest eigenvalue −→ independence and chromatic numbers
• Second largest eigenvalue −→ expansion and randomness properties
• Interlacing −→ substructures.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic graph theory
• Subject grew in the 1950’s and ‘60’s:
• Graph is regular if 1 is an eigenvector.
• #Edges = 12
∑λ2i
• #Triangles = 16
∑λ3i
• 3 distinct eigenvalues −→ strongly regular
• Smallest eigenvalue −→ independence and chromatic numbers
• Second largest eigenvalue −→ expansion and randomness properties
• Interlacing −→ substructures.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic graph theory
• Subject grew in the 1950’s and ‘60’s:
• Graph is regular if 1 is an eigenvector.
• #Edges = 12
∑λ2i
• #Triangles = 16
∑λ3i
• 3 distinct eigenvalues −→ strongly regular
• Smallest eigenvalue −→ independence and chromatic numbers
• Second largest eigenvalue −→ expansion and randomness properties
• Interlacing −→ substructures.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Algebraic graph theory
• Subject grew in the 1950’s and ‘60’s:
• Graph is regular if 1 is an eigenvector.
• #Edges = 12
∑λ2i
• #Triangles = 16
∑λ3i
• 3 distinct eigenvalues −→ strongly regular
• Smallest eigenvalue −→ independence and chromatic numbers
• Second largest eigenvalue −→ expansion and randomness properties
• Interlacing −→ substructures.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Using the spectral decomposition
Spectral Theorem
The eigenspaces of A form an orthogonal decomposition of RV .
1
1
- 23
- 23
- 23- 2
3
- 23
1
1 - 23
,
−11
−1
1−1
1
−1
1
−1
1
= 0
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing sets
Corollary
Intriguing sets X and Y associated to different eigenvalues satisfy
|X ∩ Y | =|X ||Y ||V |
.
1
1
0
0
00
0
1
1 0
,
10
1
01
0
1
0
1
0
= 2
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
f : V → R is intriguing ⇐⇒ for some α, β ∈ R
Af = α · f + β · 1.
(In fact, α is an eigenvalue of A.)
1
0
1
0
1
0
1
0
1
0
2
1
2
1
2
1
2
1
2
1
f
Af = f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
f : V → R is intriguing ⇐⇒ for some α, β ∈ R
Af = α · f + β · 1.
(In fact, α is an eigenvalue of A.)
1
0
1
0
1
0
1
0
1
0
2
1
2
1
2
1
2
1
2
1
f
Af = f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
f : V → R is intriguing ⇐⇒ for some α, β ∈ R
Af = α · f + β · 1.
(In fact, α is an eigenvalue of A.)
1
0
1
0
1
0
1
0
1
0
2
1
2
1
2
1
2
1
2
1
f
Af = f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
f : V → R is intriguing ⇐⇒ for some α, β ∈ R
Af = α · f + β · 1.
(In fact, α is an eigenvalue of A.)
1
0
1
0
1
0
1
0
1
0
2
1
2
1
2
1
2
1
2
1
f Af = f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
0
−1
1
0
1
0
1
0
0 0
1
3
−1
1
−1
1
−1
1
1 1
f
Af = −2 · f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
0
−1
1
0
1
0
1
0
0 0
1
3
−1
1
−1
1
−1
1
1 1
f
Af = −2 · f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Intriguing maps
0
−1
1
0
1
0
1
0
0 0
1
3
−1
1
−1
1
−1
1
1 1
f Af = −2 · f + 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Af = αf f + βf 1, Ag = αg f + βg1, A1 = k1.
Corollary
Intriguing maps f and g associated to different eigenvalues satisfy
〈f , g〉 =〈f ,1〉〈g ,1〉|V |
.
Proof.Eigenvectors Eigenvalue
(k − αf )f − βf 1 αf
(k − αg )g − βg1 αg
1 k
(k − αf )(k − αg )〈f , g〉−βg (k − αf )〈f ,1〉−βf (k − αg )〈1, g〉+βf βg |V | = 0
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Af = αf f + βf 1, Ag = αg f + βg1, A1 = k1.
Corollary
Intriguing maps f and g associated to different eigenvalues satisfy
〈f , g〉 =〈f ,1〉〈g ,1〉|V |
.
Proof.Eigenvectors Eigenvalue
(k − αf )f − βf 1 αf
(k − αg )g − βg1 αg
1 k
(k − αf )(k − αg )〈f , g〉−βg (k − αf )〈f ,1〉−βf (k − αg )〈1, g〉+βf βg |V | = 0
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Af = αf f + βf 1, Ag = αg f + βg1, A1 = k1.
Corollary
Intriguing maps f and g associated to different eigenvalues satisfy
〈f , g〉 =〈f ,1〉〈g ,1〉|V |
.
Proof.Eigenvectors Eigenvalue
(k − αf )f − βf 1 αf
(k − αg )g − βg1 αg
1 k
(k − αf )(k − αg )〈f , g〉−βg (k − αf )〈f ,1〉−βf (k − αg )〈1, g〉+βf βg |V | = 0
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Euler’s Theorem on latin squares
Cyclic latin squares1 2 3 4 5
2 4 1 5 3
3 5 4 2 1
4 1 5 3 2
5 3 2 1 4
Transversal1 2 3 4 5
2 4 1 5 3
3 5 4 2 1
4 1 5 3 2
5 3 2 1 4
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Euler’s Theorem on latin squares
Cyclic latin squares1 2 3 4 5
2 4 1 5 3
3 5 4 2 1
4 1 5 3 2
5 3 2 1 4
Transversal1 2 3 4 5
2 4 1 5 3
3 5 4 2 1
4 1 5 3 2
5 3 2 1 4
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Theorem (Euler, 1782)
An n × n cyclic latin square does not have a transversal whenn is even.
The strongly regular graph
Vertices: Cells of the latin squareAdjacency: Same row, same column, or same symbolEigenvalues: 3(n − 1), n − 3, −3.
1 2 3 4 52 4 1 5 33 5 4 2 14 1 5 3 25 3 2 1 4
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Theorem (Euler, 1782)
An n × n cyclic latin square does not have a transversal whenn is even.
The strongly regular graph
Vertices: Cells of the latin squareAdjacency: Same row, same column, or same symbolEigenvalues: 3(n − 1), n − 3, −3.
1 2 3 4 52 4 1 5 33 5 4 2 14 1 5 3 25 3 2 1 4
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Transversals are intriguing
11 20 30 40 50
20 40 10 51 30
30 50 41 20 10
40 10 50 30 21
50 31 20 10 40
10 23 33 43 53
23 43 13 50 33
33 53 40 23 13
43 13 53 33 20
53 30 23 13 43
1T
A1T = −31T + 31
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Transversals are intriguing
11 20 30 40 50
20 40 10 51 30
30 50 41 20 10
40 10 50 30 21
50 31 20 10 40
10 23 33 43 53
23 43 13 50 33
33 53 40 23 13
43 13 53 33 20
53 30 23 13 43
1T
A1T = −31T + 31
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Transversals are intriguing
11 20 30 40 50
20 40 10 51 30
30 50 41 20 10
40 10 50 30 21
50 31 20 10 40
10 23 33 43 53
23 43 13 50 33
33 53 40 23 13
43 13 53 33 20
53 30 23 13 43
1T A1T = −31T + 31
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
A magical intriguing map µ
11 21 31 41 51
21 40 14 51 34
31 50 42 25 17
41 15 52 35 27
51 34 26 18 46
132 232 332 432 532
232 430 138 532 338
332 530 434 240 144
432 140 534 340 244
532 338 242 146 442
µ
Aµ = 2 · µ+ 30 · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
A magical intriguing map µ
11 21 31 41 51
21 40 14 51 34
31 50 42 25 17
41 15 52 35 27
51 34 26 18 46
132 232 332 432 532
232 430 138 532 338
332 530 434 240 144
432 140 534 340 244
532 338 242 146 442
µ
Aµ = 2 · µ+ 30 · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
A magical intriguing map µ
11 21 31 41 51
21 40 14 51 34
31 50 42 25 17
41 15 52 35 27
51 34 26 18 46
132 232 332 432 532
232 430 138 532 338
332 530 434 240 144
432 140 534 340 244
532 338 242 146 442
µ Aµ = 2 · µ+ 30 · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Two opposing intriguing maps
1T : 〈1T ,1〉 = nµ: 〈µ,1〉 = n2(n + 1)/2
So
〈1T , µ〉 =〈1T ,1〉〈µ,1〉
n2=
n · n2(n + 1)/2
n2=
n(n + 1)
2
But if n is even, then
n(n + 1)
2≡ n
2(mod n).
A contradiction for a CYCLIC latin square!
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Two opposing intriguing maps
1T : 〈1T ,1〉 = nµ: 〈µ,1〉 = n2(n + 1)/2
So
〈1T , µ〉 =〈1T ,1〉〈µ,1〉
n2=
n · n2(n + 1)/2
n2=
n(n + 1)
2
But if n is even, then
n(n + 1)
2≡ n
2(mod n).
A contradiction for a CYCLIC latin square!
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Two opposing intriguing maps
1T : 〈1T ,1〉 = nµ: 〈µ,1〉 = n2(n + 1)/2
So
〈1T , µ〉 =〈1T ,1〉〈µ,1〉
n2=
n · n2(n + 1)/2
n2=
n(n + 1)
2
But if n is even, then
n(n + 1)
2≡ n
2(mod n).
A contradiction for a CYCLIC latin square!
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Finitely many points and lines
Generalised quadrangle
Given a point P and ` which are not incident, there is a unique linem on P concurrent with `.
`
P
Order (s, t)
s + 1 points on a line, t + 1 lines through a point
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Finitely many points and lines
Generalised quadrangle
Given a point P and ` which are not incident, there is a unique linem on P concurrent with `.
`
P
Order (s, t)
s + 1 points on a line, t + 1 lines through a point
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
m-coversA set of lines M of a generalised quadrangle is an m-cover ifevery point lies on m elements of M.
Figure: A 2-cover of W(3, 2).
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• Known m-covers:W(3, q) Not too manyQ(4, q), q odd Many, m evenQ−(5, q) Many!H(4, q2) Many found (recent), m > 1H(3, q2) Hemisystems, q odd
• Segre (1965):An m-cover of H(3, q2), q odd, has m = q+1
2 (a hemisystem).
• Bruen & Hirschfeld (1978):No m-cover exists of H(3, q2), q even.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• Known m-covers:W(3, q) Not too manyQ(4, q), q odd Many, m evenQ−(5, q) Many!H(4, q2) Many found (recent), m > 1H(3, q2) Hemisystems, q odd
• Segre (1965):An m-cover of H(3, q2), q odd, has m = q+1
2 (a hemisystem).
• Bruen & Hirschfeld (1978):No m-cover exists of H(3, q2), q even.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• Known m-covers:W(3, q) Not too manyQ(4, q), q odd Many, m evenQ−(5, q) Many!H(4, q2) Many found (recent), m > 1H(3, q2) Hemisystems, q odd
• Segre (1965):An m-cover of H(3, q2), q odd, has m = q+1
2 (a hemisystem).
• Bruen & Hirschfeld (1978):No m-cover exists of H(3, q2), q even.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• J. A. Thas (1981):
Hemisystem of H(3, q2) −→ partial quadrangle andstrongly regular graph
• J. A. Thas (1989):An m-cover of a GQ of order (q2, q), q odd, has m = q+1
2 .
• m-covers are intriguing.
Af = (−s + 1) · f + m(s + 1) · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• J. A. Thas (1981):
Hemisystem of H(3, q2) −→ partial quadrangle andstrongly regular graph
• J. A. Thas (1989):An m-cover of a GQ of order (q2, q), q odd, has m = q+1
2 .
• m-covers are intriguing.
Af = (−s + 1) · f + m(s + 1) · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
• J. A. Thas (1981):
Hemisystem of H(3, q2) −→ partial quadrangle andstrongly regular graph
• J. A. Thas (1989):An m-cover of a GQ of order (q2, q), q odd, has m = q+1
2 .
• m-covers are intriguing.
Af = (−s + 1) · f + m(s + 1) · 1
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
A magical intriguing map (Bamberg, Devillers & Schillewaert)
Suppose we have two disjoint lines ` and m. Then
µ := s · 1{`,m}⊥⊥ + t · 1{`,m}⊥
is intriguing.
`
m
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Two opposing intriguing maps 1
In the case that |{`,m}⊥⊥| = t2/s + 1 ...
1m-cover 〈1m-cover,1〉 = m(st + 1)µ 〈µ,1〉 = (s + t)(t + 1)
So
〈1m-cover, µ〉 =〈1m-cover,1〉〈µ,1〉
(t + 1)(st + 1)= m(s + t)
1µ = s · 1{`,m}⊥⊥ + t · 1{`,m}⊥
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Two opposing intriguing maps 1
In the case that |{`,m}⊥⊥| = t2/s + 1 ...
1m-cover 〈1m-cover,1〉 = m(st + 1)µ 〈µ,1〉 = (s + t)(t + 1)
So
〈1m-cover, µ〉 =〈1m-cover,1〉〈µ,1〉
(t + 1)(st + 1)= m(s + t)
1µ = s · 1{`,m}⊥⊥ + t · 1{`,m}⊥
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Double Counting: Fix ` not in m-cover M. Count concurrent pairs(r , s) ∈M, such that r , s are concurrent with `.
Result: m = t+12 .
Generalisation: by Frederic Vanhove to regular near polygons.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Double Counting: Fix ` not in m-cover M. Count concurrent pairs(r , s) ∈M, such that r , s are concurrent with `.
Result: m = t+12 .
Generalisation: by Frederic Vanhove to regular near polygons.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
Double Counting: Fix ` not in m-cover M. Count concurrent pairs(r , s) ∈M, such that r , s are concurrent with `.
Result: m = t+12 .
Generalisation: by Frederic Vanhove to regular near polygons.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
The bigger picture
Instead of regular graphs:Association schemes and the Bose-Mesner algebra.
More than inner products:Inner/outer distribution, Krein parameters,MacWilliams Transform.
More than latin squares and generalised quadrangles:Projective spaces, polar spaces, partial geometries.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
The bigger picture
Instead of regular graphs:Association schemes and the Bose-Mesner algebra.
More than inner products:Inner/outer distribution, Krein parameters,MacWilliams Transform.
More than latin squares and generalised quadrangles:Projective spaces, polar spaces, partial geometries.
Graphs vs Linear algebra Euler’s Theorem on latin squares Finite geometry The bigger picture
The bigger picture
Instead of regular graphs:Association schemes and the Bose-Mesner algebra.
More than inner products:Inner/outer distribution, Krein parameters,MacWilliams Transform.
More than latin squares and generalised quadrangles:Projective spaces, polar spaces, partial geometries.