SlideShare a Scribd company logo
1 of 186
EKR for Matchings
.
.
.
.
.
.
.
.
.
Mr
n: the family of all matchings of size r in K2n.
(Note that r n.)
Mr
n: the family of all matchings of size r in K2n.
(Note that r n.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n: the family of all matchings of size r in K2n.
(Note that r n.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n: the family of all matchings of size r in K2n.
(Note that r n.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
Mr
n(e): all matchings of K2n, on r edges, containing e.
(This is the star centered at e.)
.
.1
.2
.3
.4
.5
.6
.7
.8
The number of matchings of size r in K2n is:
The number of matchings of size r in K2n is:
(
2n
2
)
The number of matchings of size r in K2n is:
(
2n
2
)(
2n − 2
2
)
The number of matchings of size r in K2n is:
(
2n
2
)(
2n − 2
2
)(
2n − 4
2
)
The number of matchings of size r in K2n is:
(
2n
2
)(
2n − 2
2
)(
2n − 4
2
)
· · ·
The number of matchings of size r in K2n is:
(
2n
2
)(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
r choices
The number of matchings of size r in K2n is:
(
2n
2
)(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
r choices
r!
The number of matchings of size r in K2n is:
|Mr
n| =
(
2n
2
)(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
r choices
r!
The number of matchings of size r in a star centered at e is:
The number of matchings of size r in a star centered at e is:
(
2n − 2
2
)
The number of matchings of size r in a star centered at e is:
(
2n − 2
2
)(
2n − 4
2
)
The number of matchings of size r in a star centered at e is:
(
2n − 2
2
)(
2n − 4
2
)
· · ·
The number of matchings of size r in a star centered at e is:
(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
(r−1) choices
The number of matchings of size r in a star centered at e is:
(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
(r−1) choices
(r − 1)!
The number of matchings of size r in a star centered at e is:
|Mr
n(e)| =
(
2n − 2
2
)(
2n − 4
2
)
· · ·
(
2n − 2(r − 1)
2
)
(r−1) choices
(r − 1)!
The EKR Statement
The EKR Statement
If A is an intersecting family of r-matchings in K2n, then:
|A| |Mr
n(e)|,
The EKR Statement
If A is an intersecting family of r-matchings in K2n, then:
|A| |Mr
n(e)|,
with equality holding if and only if A is a star.
EKR for Set Systems
The EKR Statement for Families of Sets
The EKR Statement for Families of Sets
If A is an intersecting family of r-subsets of [n], then:
|A|
(
n − 1
r − 1
)
,
The EKR Statement for Families of Sets
If A is an intersecting family of r-subsets of [n], then:
|A|
(
n − 1
r − 1
)
,
with equality holding if and only if A is a star.
Proof by Picture
Katona (1972)
Proof by Picture
Katona (1972)
Arrange the elements of the universe on a circle.
Call this arrangement σ.
Proof by Picture
Katona (1972)
Arrange the elements of the universe on a circle.
Call this arrangement σ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
Proof by Picture
Katona (1972)
Arrange the elements of the universe on a circle.
Call this arrangement σ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
How big can Aσ be, given that A is intersecting?
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
Proof by Picture
Katona (1972)
Arrange the elements of the universe on a circle.
Call this arrangement σ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
How big can Aσ be, given that A is intersecting?
Proof by Picture
Katona (1972)
Arrange the elements of the universe on a circle.
Call this arrangement σ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
|Aσ| r
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, and σ is a cyclic permutation of [n]?
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.15
.2
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.3
.r!
.∈ A
.
.3
.14
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.2
.15
.r!
.∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15
.r!
.∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15
.r! · (n − r)!
.∈ A
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, σ is a cyclic permutation of [n], and S occurs as an
interval in σ?
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, σ is a cyclic permutation of [n], and S occurs as an
interval in σ?
|A| · r! · (n − r)!
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, σ is a cyclic permutation of [n], and S occurs as an
interval in σ?
|A| · r! · (n − r)! (n − 1)!r
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, σ is a cyclic permutation of [n], and S occurs as an
interval in σ?
|A| · r! · (n − r)! (n − 1)!r
|A| (n−1)!r
r!(n−r)!
The Count
Katona (1972)
How many pairs (S, σ) are there,
where S ∈ A, σ is a cyclic permutation of [n], and S occurs as an
interval in σ?
|A| · r! · (n − r)! (n − 1)!r
|A| (n−1)!r
r!(n−r)! =
(n−1
r−1
)
.
Setting Up Katona-Like Local Environments
Our universe is now the set of all edges in K2n.
Setting Up Katona-Like Local Environments
Our universe is now the set of all edges in K2n.
We might begin by considering cyclic permutations of these edges.
Setting Up Katona-Like Local Environments
Our universe is now the set of all edges in K2n.
We might begin by considering cyclic permutations of these edges.
A direct approach only leads to a weak bound...
...and informally, the bounds are loose because
an interval of an arbitrary permutation of E(K2n)
is not automatically a matching.
...and informally, the bounds are loose because
an interval of an arbitrary permutation of E(K2n)
is not automatically a matching.
Our first goal, therefore, is to come up with a more suitable
selection of cyclic permutations.
Baranyai Partitions
A Decomposition of the Edges of K2n
into (2n − 1) perfect matchings.
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
Baranyai Partitions
.
.1
.2
.3
.4
.5
.6
.7
.8
.
.1
.2
.3
.4
.5
.6
.7
.8
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(1, 2) .(3, 7) .(6, 4) .(5, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(2, 3) .(4, 1) .(7, 5) .(6, 8)
.
.1
.2
.3
.4
.5
.6
.7
.8
.(3, 4) .(5, 2) .(1, 6) .(7, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.(1, 2) .(3, 7) .(6, 4) .(5, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.(1, 2) .(3, 7) .(6, 4) .(5, 8)
.(2, 3) .(4, 1) .(7, 5) .(6, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.(1, 2) .(3, 7) .(6, 4) .(5, 8)
.(2, 3) .(4, 1) .(7, 5) .(6, 8)
.(3, 4) .(5, 2) .(1, 6) .(7, 8)
.
.(4, 5) .(3, 6) .(2, 7) .(1, 8)
.(6, 5) .(7, 4) .(1, 3) .(2, 8)
.(7, 6) .(1, 5) .(2, 4) .(3, 8)
.(7, 1) .(2, 6) .(3, 5) .(4, 8)
.(1, 2) .(3, 7) .(6, 4) .(5, 8)
.(2, 3) .(4, 1) .(7, 5) .(6, 8)
.(3, 4) .(5, 2) .(1, 6) .(7, 8)
We have just described one cyclic permutation of E(K2n).
We have just described one cyclic permutation of E(K2n).
We can generate other cyclic permutations of E(K2n) using this
method.
We have just described one cyclic permutation of E(K2n).
We can generate other cyclic permutations of E(K2n) using this
method.
Let σ be a permutation of [2n].
Start with the following Baranyai Partition...
(illustration for n = 4):
.
.σ(1)
.σ(2)
.σ(3)
.σ(4)
.σ(5)
.σ(6)
.σ(7)
.σ(8)
...and generate the following cyclic order, just as before:
.
.(σ(4), σ(5)) .(σ(3), σ(6)) .(σ(2), σ(7)) .(σ(1), σ(8))
.(σ(6), σ(5)) .(σ(7), σ(4)) .(σ(1), σ(3)) .(σ(2), σ(8))
.(σ(7), σ(6)) .(σ(1), σ(5)) .(σ(2), σ(4)) .(σ(3), σ(8))
.(σ(7), σ(1)) .(σ(2), σ(6)) .(σ(3), σ(5)) .(σ(4), σ(8))
.(σ(1), σ(2)) .(σ(3), σ(7)) .(σ(6), σ(4)) .(σ(5), σ(8))
.(σ(2), σ(3)) .(σ(4), σ(1)) .(σ(7), σ(5)) .(σ(6), σ(8))
.(σ(3), σ(4)) .(σ(5), σ(2)) .(σ(1), σ(6)) .(σ(7), σ(8))
The cyclic orders that we have just generated will serve as
wireframes on which we can project elements of A as intervals, a la
Katona’s proof for the classic version of the theorem.
The cyclic orders that we have just generated will serve as
wireframes on which we can project elements of A as intervals, a la
Katona’s proof for the classic version of the theorem.
Preliminary Observation.
Every interval of length r is a matching, as long as r < n.
The cyclic orders that we have just generated will serve as
wireframes on which we can project elements of A as intervals, a la
Katona’s proof for the classic version of the theorem.
Preliminary Observation.
Every interval of length r is a matching, as long as r < n.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
Proving the EKR bound
Let A be an intersecting family of r-matchings of K2n, where r < n.
Proving the EKR bound
Let A be an intersecting family of r-matchings of K2n, where r < n.
Let σ be a permutation of [2n] - consider the cyclic permutations
of E(K2n) that we generated based on σ - let’s call this χσ.
Proving the EKR bound
Let A be an intersecting family of r-matchings of K2n, where r < n.
Let σ be a permutation of [2n] - consider the cyclic permutations
of E(K2n) that we generated based on σ - let’s call this χσ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
Proving the EKR bound
Let A be an intersecting family of r-matchings of K2n, where r < n.
Let σ be a permutation of [2n] - consider the cyclic permutations
of E(K2n) that we generated based on σ - let’s call this χσ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
How big can Aσ be, given that A is intersecting?
Proving the EKR bound
Let A be an intersecting family of r-matchings of K2n, where r < n.
Let σ be a permutation of [2n] - consider the cyclic permutations
of E(K2n) that we generated based on σ - let’s call this χσ.
Aσ: those sets of A that happen to be intervals on this circular
arrangement.
|Aσ| r, for the same reasons as before.
Proving the EKR bound (contd.)
As before, consider the set of pairs (M, σ), where:
♣ M is a r-matching of K2n,
Proving the EKR bound (contd.)
As before, consider the set of pairs (M, σ), where:
♣ M is a r-matching of K2n,
♣ M belongs to A,
Proving the EKR bound (contd.)
As before, consider the set of pairs (M, σ), where:
♣ M is a r-matching of K2n,
♣ M belongs to A,
♣ σ is a permutation of [2n],
Proving the EKR bound (contd.)
As before, consider the set of pairs (M, σ), where:
♣ M is a r-matching of K2n,
♣ M belongs to A,
♣ σ is a permutation of [2n],
♣ and M occurs as an interval in χσ.
Proving the EKR bound (contd.)
As before, consider the set of pairs (M, σ), where:
♣ M is a r-matching of K2n,
♣ M belongs to A,
♣ σ is a permutation of [2n],
♣ and M occurs as an interval in χσ.
Clearly,
#(M, σ) r · (2n)!
Now, let us investigate the following question:
In how many cyclic orders χσ can a given matching M occur as an
interval?
To address the question, let us recall the cyclic orders χσ.
To address the question, let us recall the cyclic orders χσ.
It is useful to think of χσ as being composed of (2n − 1) chunks, as
they arise from the (2n − 1) parts of the Baranyai partitions.
To address the question, let us recall the cyclic orders χσ.
It is useful to think of χσ as being composed of (2n − 1) chunks, as
they arise from the (2n − 1) parts of the Baranyai partitions.
In the picture that follows, each row is a “chunk”.
.
.(σ(4), σ(5)) .(σ(3), σ(6)) .(σ(2), σ(7)) .(σ(1), σ(8))
.(σ(6), σ(5)) .(σ(7), σ(4)) .(σ(1), σ(3)) .(σ(2), σ(8))
.(σ(7), σ(6)) .(σ(1), σ(5)) .(σ(2), σ(4)) .(σ(3), σ(8))
.(σ(7), σ(1)) .(σ(2), σ(6)) .(σ(3), σ(5)) .(σ(4), σ(8))
.(σ(1), σ(2)) .(σ(3), σ(7)) .(σ(6), σ(4)) .(σ(5), σ(8))
.(σ(2), σ(3)) .(σ(4), σ(1)) .(σ(7), σ(5)) .(σ(6), σ(8))
.(σ(3), σ(4)) .(σ(5), σ(2)) .(σ(1), σ(6)) .(σ(7), σ(8))
Task 1.
Enumerate all σ where
M belongs to χσ as an interval,
and M lies entirely inside one of the chunks of χσ.
If M has r edges, then we have r! ways to order the edges of M.
For a fixed ordering p of the edges of M, let us devise a σ such that
χσ will contain M as an interval in its ith chunk, with the edges of
M appearing in the order prescribed by p.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
We have (n − r) choices to begin the placement of the edges of M.
We have (n − r) choices to begin the placement of the edges of M.
The ordering of the vertices that are not incident to M are
immaterial, and there are (2n − 2r)! such orderings.
We have (n − r) choices to begin the placement of the edges of M.
The ordering of the vertices that are not incident to M are
immaterial, and there are (2n − 2r)! such orderings.
Finally, for a fixed realization of M respecting the order p, we may
still swap the endpoints of M to get a different permutation with
the same realization.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
Note that there are 2r choices for swapping the endpoints of the
edges of M.
Note that there are 2r choices for swapping the endpoints of the
edges of M.
Putting everything together, we have....
These many ways in which
the chunk of σ in which we realize M
can be chosen:
(2n − 1)
These many ways in which
the ordering of the edges of M
can be chosen:
(2n − 1) · r!
These many ways in which
the starting point of M
can be chosen:
(2n − 1) · r! · (n − r)
These many ways in which
the ordering the vertices not incident to M
can be chosen:
(2n − 1) · r! · (n − r) · (2n − 2r)!
These many ways in which
“swapped” vertices within edges of M
can be chosen:
(2n − 1) · r! · (n − r) · (2n − 2r)! · 2r
These many ways in which
the permutation σ
can be chosen:
(2n − 1) · r! · (n − r) · (2n − 2r)! · 2r
Task 2.
Enumerate all σ where
M belongs to χσ as an interval,
and M “splits across” two chunks of χσ.
If M has r edges, then we have r! ways to order the edges of M.
For a fixed ordering p of the edges of M, let us devise a σ such that
χσ will contain M as an interval starting in its ith chunk, with the
edges of M appearing in the order prescribed by p,
and spilling over to the (i + 1)th chunk.
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
(Crossover edge not depicted for clarity.)
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
(Crossover edge not depicted for clarity.)
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
(Crossover edge not depicted for clarity.)
.
.i
.
.
.
.
.
.
..
.
.
.
.
.
.
(Crossover edge not depicted for clarity.)
This time, we have r choices to begin the placement of the edges of
M.
This time, we have r choices to begin the placement of the edges of
M.
As before, the ordering of the vertices that are not incident to M
are immaterial, and there are (2n − 2r)! such orderings.
This time, we have r choices to begin the placement of the edges of
M.
As before, the ordering of the vertices that are not incident to M
are immaterial, and there are (2n − 2r)! such orderings.
And again, for a fixed realization of M respecting the order p, we
may still swap (in 2r ways) the endpoints of M to get a different
permutation with the same realization.
These many ways in which
the chunk of σ in which we realize M
can be chosen:
(2n − 1)
These many ways in which
the ordering of the edges of M
can be chosen:
(2n − 1) · r!
These many ways in which
the starting point of M
can be chosen:
(2n − 1) · r! · r
These many ways in which
the ordering the vertices not incident to M
can be chosen:
(2n − 1) · r! · r · (2n − 2r)!
These many ways in which
“swapped” vertices within edges of M
can be chosen:
(2n − 1) · r! · r · (2n − 2r)! · 2r
These many ways in which
the permutation σ
can be chosen:
(2n − 1) · r! · r · (2n − 2r)! · 2r
We are now in a position to tackle this:
In how many cyclic orders χσ can a given matching M occur as an
interval?
We are now in a position to tackle this:
In how many cyclic orders χσ can a given matching M occur as an
interval?
(2n − 1) · r! · r · (2n − 2r)! · 2r
+
(2n − 1) · r! · (n − r) · (2n − 2r)! · 2r
We are now in a position to tackle this:
In how many cyclic orders χσ can a given matching M occur as an
interval?
(2n − 1) · r! · r · (2n − 2r)! · 2r
+
(2n − 1) · r! · (n − r) · (2n − 2r)! · 2r
= (2n − 1) · r! · n · (2n − 2r)! · 2r
Finally, we have:
#(M, σ) (2n)!r
Finally, we have:
|A| · (2n − 1)r!n(2n − 2r)!2r
= #(M, σ) (2n)!r
Finally, we have:
|A| · (2n − 1)r!n(2n − 2r)!2r
= #(M, σ) (2n)!r
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
Finally, we have:
|A| · (2n − 1)r!n(2n − 2r)!2r
= #(M, σ) (2n)!r
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
= |Mr
n(e)|
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)!
(2n − 1)n(2n − 2r)!2r
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)!
(2n − 1)n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)(2n − 1) · · · (2n − 2r + 1)
(2n − 1)n2r
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)!
(2n − 1)n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)(2n − 1) · · · (2n − 2r + 1)
(2n − 1)n2r
|A|
1
(r − 1)!
·
(2n − 2)(2n − 3) · · · (2n − 2r + 1)
2r−1
|A|
(2n)!r
(2n − 1)r!n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)!
(2n − 1)n(2n − 2r)!2r
|A|
1
(r − 1)!
·
(2n)(2n − 1) · · · (2n − 2r + 1)
(2n − 1)n2r
|A|
1
(r − 1)!
·
(2n − 2)(2n − 3) · · · (2n − 2r + 1)
2r−1
|A|
1
(r − 1)!
·
(
2n − 2
2
)
· · ·
(
2n − 2(r − 1)
2
)
= |Mr
n(e)|
The structural claim
Let A be an extremal intersecting family of r-matchings of K2n.
The structural claim
Let A be an extremal intersecting family of r-matchings of K2n.
Then there must be an edge that is common to all matchings in A.
High Level Proof Strategy
We know that if A is an extremal intersecting family, then:
|Aσ| = r, for all σ ∈ S2n.
Therefore, the matchings of the subfamily Aσ necessarily have a
common edge...
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
.
.1
.2
.3
.4
.5
.6
.7
.8.9
.10
.11
.12
.13
.14
.15 .∈ A
High Level Proof Strategy (Contd.)
Now that we know that every Aσ has a common edge,
it remains to show that the common edge is the same for every σ.
High Level Proof Strategy (Contd.)
Now that we know that every Aσ has a common edge,
it remains to show that the common edge is the same for every σ.
Begin by assuming that Aσ is centered at the edge e, where σ is the
identity permutation.
High Level Proof Strategy (Contd.)
Now that we know that every Aσ has a common edge,
it remains to show that the common edge is the same for every σ.
Begin by assuming that Aσ is centered at the edge e, where σ is the
identity permutation.
High Level Proof Strategy (Contd.)
Let σi be obtained by σ by a transposition of the element at i.
σi(j) =



i + 1 if j = i,
i if j = i + 1,
j otherwise .
High Level Proof Strategy (Contd.)
Let σi be obtained by σ by a transposition of the element at i.
σi(j) =



i + 1 if j = i,
i if j = i + 1,
j otherwise .
It suffices to show that, for every 1 i 2n,
Aσi
and Aσ have the same common edge.
High Level Proof Strategy (Contd.)
Let σi be obtained by σ by a transposition of the element at i.
σi(j) =



i + 1 if j = i,
i if j = i + 1,
j otherwise .
It suffices to show that, for every 1 i 2n,
Aσi
and Aσ have the same common edge.
High Level Proof Strategy (Contd.)
Let σi be obtained by σ by a transposition of the element at i.
σi(j) =



i + 1 if j = i,
i if j = i + 1,
j otherwise .
It suffices to show that, for every 1 i 2n,
Aσi
and Aσ have the same common edge.
The proof follows by an analysis on the structure of χσi
,
based on position of i.
Thank You!
.
.
.
.
.
.
.
.
.

More Related Content

Similar to EKR for Matchings

11 x1 t10 07 sum & product of roots (2013)
11 x1 t10 07 sum & product of roots (2013)11 x1 t10 07 sum & product of roots (2013)
11 x1 t10 07 sum & product of roots (2013)Nigel Simmons
 
11 x1 t15 06 roots & coefficients (2013)
11 x1 t15 06 roots & coefficients (2013)11 x1 t15 06 roots & coefficients (2013)
11 x1 t15 06 roots & coefficients (2013)Nigel Simmons
 
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 32018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3Yosia Adi Setiawan
 
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 32018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3Yosia Adi Setiawan
 
Ветровое волнение океана и волны-убийцы. Владимир Захаров
Ветровое волнение океана и волны-убийцы. Владимир ЗахаровВетровое волнение океана и волны-убийцы. Владимир Захаров
Ветровое волнение океана и волны-убийцы. Владимир ЗахаровAlexander Dubynin
 
IRJET- On Certain Subclasses of Univalent Functions: An Application
IRJET- On Certain Subclasses of Univalent Functions: An ApplicationIRJET- On Certain Subclasses of Univalent Functions: An Application
IRJET- On Certain Subclasses of Univalent Functions: An ApplicationIRJET Journal
 
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONS
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONSCAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONS
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONSCarlon Baird
 
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS ONE DIMENSIONAL FINITE ELEMENT ANALYSIS
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS velliyangiri1
 
The Study of the Wiener Processes Base on Haar Wavelet
The Study of the Wiener Processes Base on Haar WaveletThe Study of the Wiener Processes Base on Haar Wavelet
The Study of the Wiener Processes Base on Haar WaveletScientific Review SR
 
X2 t02 03 roots & coefficients (2013)
X2 t02 03 roots & coefficients (2013)X2 t02 03 roots & coefficients (2013)
X2 t02 03 roots & coefficients (2013)Nigel Simmons
 
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-ssusere0a682
 
مراجعة مركزة نهائية 2021
مراجعة مركزة  نهائية 2021مراجعة مركزة  نهائية 2021
مراجعة مركزة نهائية 2021Rihan Rihan
 
вестник южно уральского-государственного_университета._серия_математика._меха...
вестник южно уральского-государственного_университета._серия_математика._меха...вестник южно уральского-государственного_университета._серия_математика._меха...
вестник южно уральского-государственного_университета._серия_математика._меха...Иван Иванов
 

Similar to EKR for Matchings (20)

11 x1 t10 07 sum & product of roots (2013)
11 x1 t10 07 sum & product of roots (2013)11 x1 t10 07 sum & product of roots (2013)
11 x1 t10 07 sum & product of roots (2013)
 
11 x1 t15 06 roots & coefficients (2013)
11 x1 t15 06 roots & coefficients (2013)11 x1 t15 06 roots & coefficients (2013)
11 x1 t15 06 roots & coefficients (2013)
 
1110 ch 11 day 10
1110 ch 11 day 101110 ch 11 day 10
1110 ch 11 day 10
 
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 32018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 8 Rombel 3
 
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 32018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3
2018 Geometri Transformasi Perkalian 5 Isometri Kelompok 7 Rombel 3
 
Ветровое волнение океана и волны-убийцы. Владимир Захаров
Ветровое волнение океана и волны-убийцы. Владимир ЗахаровВетровое волнение океана и волны-убийцы. Владимир Захаров
Ветровое волнение океана и волны-убийцы. Владимир Захаров
 
IRJET- On Certain Subclasses of Univalent Functions: An Application
IRJET- On Certain Subclasses of Univalent Functions: An ApplicationIRJET- On Certain Subclasses of Univalent Functions: An Application
IRJET- On Certain Subclasses of Univalent Functions: An Application
 
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONS
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONSCAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONS
CAPE PURE MATHEMATICS UNIT 2 MODULE 2 PRACTICE QUESTIONS
 
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS ONE DIMENSIONAL FINITE ELEMENT ANALYSIS
ONE DIMENSIONAL FINITE ELEMENT ANALYSIS
 
The Study of the Wiener Processes Base on Haar Wavelet
The Study of the Wiener Processes Base on Haar WaveletThe Study of the Wiener Processes Base on Haar Wavelet
The Study of the Wiener Processes Base on Haar Wavelet
 
X2 t02 03 roots & coefficients (2013)
X2 t02 03 roots & coefficients (2013)X2 t02 03 roots & coefficients (2013)
X2 t02 03 roots & coefficients (2013)
 
Solid state chemistry
Solid state chemistrySolid state chemistry
Solid state chemistry
 
Types of RELATIONS
Types of RELATIONSTypes of RELATIONS
Types of RELATIONS
 
AJMS_487_23.pdf
AJMS_487_23.pdfAJMS_487_23.pdf
AJMS_487_23.pdf
 
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-
ゲーム理論 BASIC 演習88 -投票ゲームにおけるシャープレイ•シュービック指数-
 
#26 Key
#26 Key#26 Key
#26 Key
 
Lect17
Lect17Lect17
Lect17
 
مراجعة مركزة نهائية 2021
مراجعة مركزة  نهائية 2021مراجعة مركزة  نهائية 2021
مراجعة مركزة نهائية 2021
 
вестник южно уральского-государственного_университета._серия_математика._меха...
вестник южно уральского-государственного_университета._серия_математика._меха...вестник южно уральского-государственного_университета._серия_математика._меха...
вестник южно уральского-государственного_университета._серия_математика._меха...
 
Physics_notes.pdf
Physics_notes.pdfPhysics_notes.pdf
Physics_notes.pdf
 

More from Neeldhara Misra

Efficient algorithms for hard problems on structured electorates
Efficient algorithms for hard problems on structured electoratesEfficient algorithms for hard problems on structured electorates
Efficient algorithms for hard problems on structured electoratesNeeldhara Misra
 
On the Parameterized Complexity of Party Nominations
On the Parameterized Complexity of Party NominationsOn the Parameterized Complexity of Party Nominations
On the Parameterized Complexity of Party NominationsNeeldhara Misra
 
Graph Modification Problems: A Modern Perspective
Graph Modification Problems: A Modern PerspectiveGraph Modification Problems: A Modern Perspective
Graph Modification Problems: A Modern PerspectiveNeeldhara Misra
 
Deleting to Structured Trees
Deleting to Structured TreesDeleting to Structured Trees
Deleting to Structured TreesNeeldhara Misra
 
Elicitation for Preferences Single Peaked on Trees
Elicitation for Preferences Single Peaked on Trees Elicitation for Preferences Single Peaked on Trees
Elicitation for Preferences Single Peaked on Trees Neeldhara Misra
 
An FPT Algorithm for Maximum Edge Coloring
An FPT Algorithm for Maximum Edge ColoringAn FPT Algorithm for Maximum Edge Coloring
An FPT Algorithm for Maximum Edge ColoringNeeldhara Misra
 
Cheat Sheets for Hard Problems
Cheat Sheets for Hard ProblemsCheat Sheets for Hard Problems
Cheat Sheets for Hard ProblemsNeeldhara Misra
 
A Kernel for Planar F-deletion: The Connected Case
A Kernel for Planar F-deletion: The Connected CaseA Kernel for Planar F-deletion: The Connected Case
A Kernel for Planar F-deletion: The Connected CaseNeeldhara Misra
 
Kernels for Planar F-Deletion (Restricted Variants)
Kernels for Planar F-Deletion (Restricted Variants)Kernels for Planar F-Deletion (Restricted Variants)
Kernels for Planar F-Deletion (Restricted Variants)Neeldhara Misra
 
Kernelization Complexity of Colorful Motifs
Kernelization Complexity of Colorful MotifsKernelization Complexity of Colorful Motifs
Kernelization Complexity of Colorful MotifsNeeldhara Misra
 
Expansions for Reductions
Expansions for ReductionsExpansions for Reductions
Expansions for ReductionsNeeldhara Misra
 
Lower Bounds In Kernelization
Lower Bounds In KernelizationLower Bounds In Kernelization
Lower Bounds In KernelizationNeeldhara Misra
 
Connected Dominating Set and Short Cycles
Connected Dominating Set and Short CyclesConnected Dominating Set and Short Cycles
Connected Dominating Set and Short CyclesNeeldhara Misra
 

More from Neeldhara Misra (16)

Efficient algorithms for hard problems on structured electorates
Efficient algorithms for hard problems on structured electoratesEfficient algorithms for hard problems on structured electorates
Efficient algorithms for hard problems on structured electorates
 
On the Parameterized Complexity of Party Nominations
On the Parameterized Complexity of Party NominationsOn the Parameterized Complexity of Party Nominations
On the Parameterized Complexity of Party Nominations
 
Graph Modification Problems: A Modern Perspective
Graph Modification Problems: A Modern PerspectiveGraph Modification Problems: A Modern Perspective
Graph Modification Problems: A Modern Perspective
 
Deleting to Structured Trees
Deleting to Structured TreesDeleting to Structured Trees
Deleting to Structured Trees
 
Elicitation for Preferences Single Peaked on Trees
Elicitation for Preferences Single Peaked on Trees Elicitation for Preferences Single Peaked on Trees
Elicitation for Preferences Single Peaked on Trees
 
Wg qcolorable
Wg qcolorableWg qcolorable
Wg qcolorable
 
An FPT Algorithm for Maximum Edge Coloring
An FPT Algorithm for Maximum Edge ColoringAn FPT Algorithm for Maximum Edge Coloring
An FPT Algorithm for Maximum Edge Coloring
 
Research in CS
Research in CSResearch in CS
Research in CS
 
Cheat Sheets for Hard Problems
Cheat Sheets for Hard ProblemsCheat Sheets for Hard Problems
Cheat Sheets for Hard Problems
 
From FVS to F-Deletion
From FVS to F-DeletionFrom FVS to F-Deletion
From FVS to F-Deletion
 
A Kernel for Planar F-deletion: The Connected Case
A Kernel for Planar F-deletion: The Connected CaseA Kernel for Planar F-deletion: The Connected Case
A Kernel for Planar F-deletion: The Connected Case
 
Kernels for Planar F-Deletion (Restricted Variants)
Kernels for Planar F-Deletion (Restricted Variants)Kernels for Planar F-Deletion (Restricted Variants)
Kernels for Planar F-Deletion (Restricted Variants)
 
Kernelization Complexity of Colorful Motifs
Kernelization Complexity of Colorful MotifsKernelization Complexity of Colorful Motifs
Kernelization Complexity of Colorful Motifs
 
Expansions for Reductions
Expansions for ReductionsExpansions for Reductions
Expansions for Reductions
 
Lower Bounds In Kernelization
Lower Bounds In KernelizationLower Bounds In Kernelization
Lower Bounds In Kernelization
 
Connected Dominating Set and Short Cycles
Connected Dominating Set and Short CyclesConnected Dominating Set and Short Cycles
Connected Dominating Set and Short Cycles
 

Recently uploaded

08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 

Recently uploaded (20)

08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 

EKR for Matchings