Combinatorics (Fall 2010)/Extremal set theory II

Sauer's lemma and VC-dimension

Shattering

Definition

Let [math]\displaystyle{ \mathcal{F}\subseteq 2^X }[/math] be set family and let [math]\displaystyle{ R\subseteq X }[/math] be a subset. The trace of [math]\displaystyle{ \mathcal{F} }[/math] on [math]\displaystyle{ R }[/math], denoted [math]\displaystyle{ \mathcal{F}|_R }[/math] is defined as [math]\displaystyle{ \mathcal{F}|_R=\{S\cap R\mid S\in\mathcal{F}\} }[/math]. We say that [math]\displaystyle{ \mathcal{F} }[/math] shatters [math]\displaystyle{ R }[/math] if [math]\displaystyle{ \mathcal{F}|_R=2^R }[/math], i.e. for all [math]\displaystyle{ T\subseteq R }[/math], there exists an [math]\displaystyle{ S\in\mathcal{F} }[/math] such that [math]\displaystyle{ T=S\cap R }[/math].

Sauer's Lemma (Sauer; Shelah-Perles; Vapnik-Chervonenkis)

Let [math]\displaystyle{ \mathcal{F}\subseteq 2^X }[/math] where [math]\displaystyle{ |X|=n }[/math]. If [math]\displaystyle{ |\mathcal{F}|\gt \sum_{1\le i\lt k}{n\choose i} }[/math], then there exists an [math]\displaystyle{ R\in{X\choose k} }[/math] such that [math]\displaystyle{ \mathcal{F} }[/math] shatters [math]\displaystyle{ R }[/math].

Down-shifts

Definition (down-shifts)

Assume [math]\displaystyle{ \mathcal{F}\subseteq 2^{[n]} }[/math], and [math]\displaystyle{ i\in[n] }[/math]. Define the down-shift operator [math]\displaystyle{ S_{i} }[/math] as follows: for each [math]\displaystyle{ T\in\mathcal{F} }[/math], let [math]\displaystyle{ S_{i}(T)= \begin{cases} T\setminus\{i\} & \mbox{if }i\in T \mbox{ and }T\setminus\{i\} \not\in\mathcal{F},\\ T & \mbox{otherwise;} \end{cases} }[/math] let [math]\displaystyle{ S_{i}(\mathcal{F})=\{S_{i}(T)\mid T\in \mathcal{F}\} }[/math].

Proposition

[math]\displaystyle{ |S_{i}(\mathcal{F})|=\mathcal{F} }[/math]; [math]\displaystyle{ S_i(\mathcal{F})|_R\subseteq S_i(\mathcal{F}|_R) }[/math], thus [math]\displaystyle{ |S_i(\mathcal{F})|_R|\le |\mathcal{F}|_R| }[/math].

Proof. 1 is immediate. We prove 2, that [math]\displaystyle{ S_i(\mathcal{F})|_R\subseteq S_i(\mathcal{F}|_R) }[/math]. File:Down-shift-property.png [math]\displaystyle{ \square }[/math]

The Vapnik-Chervonenkis dimenison

Definition (VC-dimension)

The Vapnik–Chervonenkis dimension (VC-dimension) of a set family [math]\displaystyle{ \mathcal{F}\subseteq 2^X }[/math], denoted [math]\displaystyle{ \text{VC-dim}(\mathcal{F}) }[/math], is the size of the largest [math]\displaystyle{ R\subseteq X }[/math] shattered by [math]\displaystyle{ \mathcal{F} }[/math].

The Kruskal–Katona Theorem

The shadow of a set system [math]\displaystyle{ \mathcal{F} }[/math], denoted [math]\displaystyle{ \Delta\mathcal{F} }[/math], consists of all sets which can be obtained by removing an element from a set in [math]\displaystyle{ \mathcal{F} }[/math].

Definition

Let [math]\displaystyle{ \mathcal{F}\subseteq {X\choose k} }[/math]. The shadow of [math]\displaystyle{ \mathcal{F} }[/math] is defined to be [math]\displaystyle{ \Delta\mathcal{F}=\left\{T\in {X\choose k-1}\,\,\bigg|\,\, \exists S\in\mathcal{F}\mbox{ such that } T\subset S\right\} }[/math].

The shadow contains rich information about the set system. An extremal question is: for a system of fixed number of [math]\displaystyle{ k }[/math]-sets, how small can its shadow be? The Kruskal–Katona theorem gives an answer to this question.

To state the result of the Kruskal–Katona theorem, we need to introduce the concepts of the [math]\displaystyle{ k }[/math]-cascade representation of numbers and the colex order of sets.

[math]\displaystyle{ k }[/math]-cascade representation of a number

Theorem

Given positive integers [math]\displaystyle{ m }[/math] and [math]\displaystyle{ k }[/math], there exists a unique representation of [math]\displaystyle{ m }[/math] in the form [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t}=\sum_{\ell=t}^k{m_\ell\choose \ell} }[/math], where [math]\displaystyle{ m_k\gt m_{k-1}\gt \cdots\gt m_t\ge t\ge 1 }[/math]. This representation of [math]\displaystyle{ m }[/math] is called a [math]\displaystyle{ k }[/math]-cascade (or a [math]\displaystyle{ k }[/math]-binomial) representation of [math]\displaystyle{ m }[/math].

Proof. In fact, the [math]\displaystyle{ k }[/math]-cascade representation [math]\displaystyle{ (m_k,m_{k-1},\ldots,m_t) }[/math] of an [math]\displaystyle{ m }[/math] can be found by the following simple greedy algorithm: [math]\displaystyle{ \ell=k; }[/math] while ([math]\displaystyle{ m\gt 0 }[/math]) do let [math]\displaystyle{ m_\ell }[/math] be the largest integer for which [math]\displaystyle{ {m_\ell\choose \ell}\le m; }[/math] [math]\displaystyle{ m=m-m_\ell; }[/math] [math]\displaystyle{ \ell=\ell-1; }[/math] end We then show that the above algorithm constructs a sequence [math]\displaystyle{ (m_k,m_{k-1},\ldots,m_t) }[/math] with [math]\displaystyle{ m_k\gt m_{k-1}\gt \cdots\gt m_t\ge t\ge 1 }[/math]. Suppose the current [math]\displaystyle{ \ell }[/math] and the current [math]\displaystyle{ m }[/math]. To see that [math]\displaystyle{ m_{\ell-1}\lt m_\ell }[/math], we suppose otherwise [math]\displaystyle{ m_{\ell-1}\ge m_\ell }[/math]. Then [math]\displaystyle{ m\ge {m_\ell\choose \ell}+{m_{\ell-1}\choose \ell-1}\ge{m_\ell\choose \ell}+{m_{\ell}\choose \ell-1}={1+m_{\ell}\choose \ell} }[/math] contradicting the maximality of [math]\displaystyle{ m_\ell }[/math]. Therefore, [math]\displaystyle{ m_\ell\gt m_{\ell-1} }[/math]. The algorithm continues reducing [math]\displaystyle{ m }[/math] to smaller nonnegative values, and eventually reaches a stage where the choice of [math]\displaystyle{ m_t }[/math] for some [math]\displaystyle{ t\ge 2 }[/math] where [math]\displaystyle{ {m_t\choose t} }[/math] equals the current [math]\displaystyle{ m }[/math]; or gets right down to choosing [math]\displaystyle{ m_1 }[/math] as the integer such that [math]\displaystyle{ m_1={m_1\choose 1} }[/math] equals the current [math]\displaystyle{ m }[/math]. Therefore, [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t} }[/math] and [math]\displaystyle{ m_k\gt m_{k-1}\gt \cdots\gt m_t\ge t\ge 1 }[/math]. The uniqueness of the [math]\displaystyle{ k }[/math]-cascade representation follows by the induction on [math]\displaystyle{ k }[/math]. When [math]\displaystyle{ k=1 }[/math], [math]\displaystyle{ m }[/math] has a unique [math]\displaystyle{ k }[/math]-cascade representation [math]\displaystyle{ m={m\choose 1} }[/math]. For general [math]\displaystyle{ k\gt 1 }[/math], suppose that every nonnegative integer [math]\displaystyle{ m }[/math] has a unique [math]\displaystyle{ (k-1) }[/math]-cascade representation. Suppose then that [math]\displaystyle{ m }[/math] has two [math]\displaystyle{ k }[/math]-cascade representations: [math]\displaystyle{ m={a_k\choose k}+{a_{k-1}\choose k-1}+\cdots+{a_t\choose t}={b_k\choose k}+{b_{k-1}\choose k-1}+\cdots+{b_r\choose r} }[/math]. We then show that it must hold that [math]\displaystyle{ a_k=b_k }[/math]. If [math]\displaystyle{ a_k\neq b_k }[/math], WLOG, suppose that [math]\displaystyle{ a_k\lt b_k }[/math]. We obtain [math]\displaystyle{ \begin{align} m& ={a_k\choose k}+{a_{k-1}\choose k-1}+\cdots+{a_t\choose t}\\ &\le {a_k\choose k}+{a_{k}-1\choose k-1}+\cdots+{a_k-(k-t)\choose t}+\cdots+{a_k-k+1\choose 1}\\ &={a_k+1\choose k}-1, \end{align} }[/math] where the last equation is got by repeatedly applying the identity [math]\displaystyle{ {n\choose k}+{n\choose k-1}={n+1\choose k} }[/math]. We then obtain [math]\displaystyle{ m\lt {a_k+1\choose k}\le{b_k\choose k}\le m }[/math], which is a contradiction. Therefore, [math]\displaystyle{ a_k=b_k }[/math], and by the induction hypothesis, the remaining value [math]\displaystyle{ m-a_k=m-b_k }[/math] has a unique [math]\displaystyle{ (k-1) }[/math]-cascade representation, so [math]\displaystyle{ a_i=b_i }[/math] for all [math]\displaystyle{ i }[/math]. [math]\displaystyle{ \square }[/math]

Co-lexicographic order of subsets

The co-lexicographic order of sets plays a particularly important role in the investigation of the size of the shadow of a system of [math]\displaystyle{ k }[/math]-sets.

Definition

The co-lexicographic (colex) order (also called the reverse lexicographic order) of sets is defined as follows: for any [math]\displaystyle{ A,B\subseteq [n] }[/math], [math]\displaystyle{ A\neq B }[/math], [math]\displaystyle{ A\lt B }[/math] if [math]\displaystyle{ \max A\setminus B \lt \max B\setminus A }[/math].

We can sort sets in colex order by first writing each set as a tuple, whose elements are in decreasing order, and then sorting the tuples in the lexicographic order of tuples.

For example, [math]\displaystyle{ {[5]\choose 3} }[/math] in colex order is

{3,2,1}
{4,2,1}
{4,3,1}
{4,3,2}
{5,2,1}
{5,3,1}
{5,3,2}
{5,4,1}
{5,4,2}
{5,4,3}

We find that the first [math]\displaystyle{ {n\choose 3} }[/math] sets in this order for [math]\displaystyle{ n=3,4,5 }[/math], form precisely [math]\displaystyle{ {[n]\choose 3} }[/math]. And if we write the colex order of [math]\displaystyle{ {[6]\choose 3} }[/math], the above colex order of [math]\displaystyle{ {[5]\choose 3} }[/math] appears as a prefix of that order. Elaborating on this, we have:

Proposition

Let [math]\displaystyle{ \mathcal{R}(m,k) }[/math] be the first [math]\displaystyle{ m }[/math] sets in the colex order of [math]\displaystyle{ {\mathbb{N}\choose k} }[/math]. Then [math]\displaystyle{ \mathcal{R}\left({n\choose k},k\right)={[n]\choose k} }[/math], that is, the first [math]\displaystyle{ {n\choose k} }[/math] sets in the colex order of all [math]\displaystyle{ k }[/math]-sets of natural numbers is precisely [math]\displaystyle{ {[n]\choose k} }[/math].

This proposition says that the sets in [math]\displaystyle{ \mathcal{R}(m,k) }[/math] is highly overlapped, which suggests that [math]\displaystyle{ \mathcal{R}(m,k) }[/math] may have small shadow. The size of the shadow of [math]\displaystyle{ \mathcal{R}(m,k) }[/math] is closely related to the [math]\displaystyle{ k }[/math]-cascade representation of [math]\displaystyle{ m }[/math].

Theorem

Suppose the [math]\displaystyle{ k }[/math]-cascade representation of [math]\displaystyle{ m }[/math] is [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t} }[/math]. Then [math]\displaystyle{ |\Delta\mathcal{R}(m,k)|={m_k\choose k-1}+{m_{k-1}\choose k-2}+\cdots+{m_t\choose t-1} }[/math].

Proof. Given [math]\displaystyle{ m }[/math] and its [math]\displaystyle{ k }[/math]-cascade representation [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t} }[/math], the [math]\displaystyle{ \mathcal{R}(m,k) }[/math] is constructed as: all sets in [math]\displaystyle{ {[m_k]\choose k} }[/math]; all sets in [math]\displaystyle{ {[m_{k-1}]\choose k-1} }[/math], unioned with [math]\displaystyle{ \{1+m_k\}\, }[/math]; [math]\displaystyle{ \vdots }[/math] all sets in [math]\displaystyle{ {[m_{t}]\choose t} }[/math], unioned with [math]\displaystyle{ \{1+m_k,1+m_{k-1},\ldots,1+m_{t+1}\} }[/math]. The shadow [math]\displaystyle{ \Delta\mathcal{R}(m,k) }[/math] is the collection of all [math]\displaystyle{ (k-1) }[/math]-sets contained by the above sets, which are all sets in [math]\displaystyle{ {[m_k]\choose k-1} }[/math]; all sets in [math]\displaystyle{ {[m_{k-1}]\choose k-2} }[/math], unioned with [math]\displaystyle{ \{1+m_k\}\, }[/math]; [math]\displaystyle{ \vdots }[/math] all sets in [math]\displaystyle{ {[m_{t}]\choose t-1} }[/math], unioned with [math]\displaystyle{ \{1+m_k,1+m_{k-1},\ldots,1+m_{t+1}\} }[/math]. Thus, [math]\displaystyle{ |\Delta\mathcal{R}(m,k)|={m_k\choose k-1}+{m_{k-1}\choose k-2}+\cdots+{m_t\choose t-1} }[/math]. [math]\displaystyle{ \square }[/math]

The Kruskal–Katona theorem

The Kruskal–Katona theorem states that among all systems of [math]\displaystyle{ m }[/math] [math]\displaystyle{ k }[/math]-sets, [math]\displaystyle{ \mathcal{R}(m,k) }[/math], i.e., the first [math]\displaystyle{ m }[/math] [math]\displaystyle{ k }[/math]-sets in the colex order, has the smallest shadow.

The theorem is proved independently by Joseph Kruskal in 1963 and G.O.H. Katona in 1966, and is a fundamental result in finite set theory and combinatorial topology.

Theorem (Kruskal 1963, Katona 1966)

Let [math]\displaystyle{ \mathcal{F}\subseteq {X\choose k} }[/math] with [math]\displaystyle{ |\mathcal{F}|=m }[/math], and suppose that [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t} }[/math] where [math]\displaystyle{ m_k\gt m_{k-1}\gt \cdots\gt m_t\ge t\ge 1 }[/math]. Then [math]\displaystyle{ |\Delta\mathcal{F}|\ge {m_k\choose k-1}+{m_{k-1}\choose k-2}+\cdots+{m_t\choose t-1} }[/math].

The original proof of the theorem is rather complicated. In the following years, several different proofs were discovered. Here we present a proof dueto Frankl by the shifting technique.

Frankl's shifting proof of Kruskal-Katonal theorem (Frankl 1984)

We take the classic [math]\displaystyle{ (i,j) }[/math]-shift operator [math]\displaystyle{ S_{ij} }[/math] defined in the original proof of the Erdős-Ko-Rado theorem.

Definition ([math]\displaystyle{ (i,j) }[/math]-shift)

Assume [math]\displaystyle{ \mathcal{F}\subseteq 2^{[n]} }[/math], and [math]\displaystyle{ 0\le i\lt j\le n-1 }[/math]. Define the [math]\displaystyle{ (i,j) }[/math]-shift [math]\displaystyle{ S_{ij} }[/math] as an operator on [math]\displaystyle{ \mathcal{F} }[/math] as follows: for each [math]\displaystyle{ T\in\mathcal{F} }[/math], write [math]\displaystyle{ T_{ij}=(T\setminus\{j\})\cup\{i\} }[/math], and let [math]\displaystyle{ S_{ij}(T)= \begin{cases} T_{ij} & \mbox{if }j\in T, i\not\in T, \mbox{ and }T_{ij} \not\in\mathcal{F},\\ T & \mbox{otherwise;} \end{cases} }[/math] let [math]\displaystyle{ S_{ij}(\mathcal{F})=\{S_{ij}(T)\mid T\in \mathcal{F}\} }[/math].

It is immediate that shifting does not change the size of the set or the size of the system, i.e., [math]\displaystyle{ |S_{ij}(T)|=|T|\, }[/math] and [math]\displaystyle{ |S_{ij}(\mathcal{F})|=\mathcal{F} }[/math]. And due to the finiteness, repeatedly applying [math]\displaystyle{ (i,j) }[/math]-shifts for any [math]\displaystyle{ 1\le i\lt j\le n }[/math], eventually [math]\displaystyle{ \mathcal{F} }[/math] does not changing any more. We called such an [math]\displaystyle{ \mathcal{F} }[/math] with [math]\displaystyle{ \mathcal{F}=S_{ij}(\mathcal{F}) }[/math] for any [math]\displaystyle{ 1\le i\lt j\le n }[/math] shifted.

In order to make the shifting technique work for shadows, we have to prove that shifting does not increase the size of the shadow.

Proposition

[math]\displaystyle{ \Delta S_{ij}(\mathcal{F})\subseteq S_{ij}(\Delta\mathcal{F}) }[/math].

Proof. We abuse the notation [math]\displaystyle{ \Delta }[/math] and let [math]\displaystyle{ \Delta A=\Delta\{A\} }[/math] if [math]\displaystyle{ A }[/math] is a set instead of a set system. It is sufficient to show that for any [math]\displaystyle{ A\in\mathcal{F} }[/math], [math]\displaystyle{ \Delta S_{ij}(A)\subseteq S_{ij}(\Delta\mathcal{F}) }[/math]. File:Property-shift-shadow.png [math]\displaystyle{ \square }[/math]

An immediate corollary of the above proposition is that the [math]\displaystyle{ (i,j) }[/math]-shifts [math]\displaystyle{ S_{ij} }[/math] for any [math]\displaystyle{ 1\le i\lt j\le n }[/math] do not increase the size of the shadow.

Corollary

[math]\displaystyle{ |\Delta S_{ij}(\mathcal{F})|\le |\Delta\mathcal{F}| }[/math].

Proof. By the above proposition, [math]\displaystyle{ |\Delta S_{ij}(\mathcal{F})|\le|S_{ij}(\Delta\mathcal{F})| }[/math], and we know that [math]\displaystyle{ S_{ij} }[/math] does not change the cardinality of a set family, that is, [math]\displaystyle{ |S_{ij}(\Delta\mathcal{F})|=|\Delta\mathcal{F}| }[/math], therefore [math]\displaystyle{ \Delta S_{ij}(\mathcal{F})|\le|\Delta\mathcal{F}| }[/math]. [math]\displaystyle{ \square }[/math]

Proof of Kruskal-Katona theorem

We know that shifts never enlarge the shadow, thus it is sufficient to prove the theorem for shifted [math]\displaystyle{ \mathcal{F} }[/math]. We then assume [math]\displaystyle{ \mathcal{F} }[/math] is shifted.

Apply induction on [math]\displaystyle{ m }[/math] and for given [math]\displaystyle{ m }[/math] on [math]\displaystyle{ k }[/math]. The theorem holds trivially for the case that [math]\displaystyle{ k=1 }[/math] and [math]\displaystyle{ m }[/math] is arbitrary.

Define

[math]\displaystyle{ \mathcal{F}_0=\{A\in\mathcal{F}\mid 1\not\in A\} }[/math],

[math]\displaystyle{ \mathcal{F}_1=\{A\in\mathcal{F}\mid 1\in A\} }[/math].

And let [math]\displaystyle{ \mathcal{F}_1'=\{A\setminus\{1\}\mid A\in\mathcal{F}_1\} }[/math].

Clearly [math]\displaystyle{ \mathcal{F}_0,\mathcal{F}_1\subseteq{X\choose k} }[/math], [math]\displaystyle{ \mathcal{F}_1'\subseteq{X\choose k-1} }[/math], and

[math]\displaystyle{ |\mathcal{F}|=|\mathcal{F}_0|+|\mathcal{F}_1|=|\mathcal{F}_0|+|\mathcal{F}_1'| }[/math].

Our induction is based on the following observation regarding the size of the shadow.

Lemma 1

[math]\displaystyle{ |\Delta\mathcal{F}|\ge|\Delta\mathcal{F}_1'|+|\mathcal{F}_1'| }[/math].

Proof. Obviously [math]\displaystyle{ \mathcal{F}\supseteq\mathcal{F}_1 }[/math] and [math]\displaystyle{ \Delta\mathcal{F}\supseteq\Delta\mathcal{F}_1 }[/math]. [math]\displaystyle{ \begin{align} \Delta\mathcal{F}_1 &=\left\{A\in{X\choose k-1}\,\,\bigg|\,\, \exists B\in\mathcal{F}_1, A\subset B\right\}\\ &=\left\{A\in{X\choose k-1}\,\,\bigg|\,\, 1\in A, \exists B\in\mathcal{F}_1, A\subset B\right\}\\ &\quad\, \cup \left\{A\in{X\choose k-1}\,\,\bigg|\,\, 1\not\in A, \exists B\in\mathcal{F}_1, A\subset B\right\}\\ &=\{S\cup\{1\}\mid S\in\Delta\mathcal{F}_1'\}\cup\mathcal{F}_1'. \end{align} }[/math] The union is taken over two disjoint families. Therefore, [math]\displaystyle{ |\Delta\mathcal{F}|\ge|\Delta\mathcal{F}_1|=|\Delta\mathcal{F}_1'|+|\mathcal{F}_1'| }[/math]. [math]\displaystyle{ \square }[/math]

The following property of shifted [math]\displaystyle{ \mathcal{F} }[/math] is essential for our proof.

Lemma 2

For shifted [math]\displaystyle{ \mathcal{F} }[/math], it holds that [math]\displaystyle{ \Delta\mathcal{F}_0\subseteq \mathcal{F}_1' }[/math].

Proof. If [math]\displaystyle{ A\in\Delta\mathcal{F}_0 }[/math] then [math]\displaystyle{ A\cup\{j\}\in\mathcal{F}_0\subseteq\mathcal{F} }[/math] for some [math]\displaystyle{ j\gt 1 }[/math] so that, since [math]\displaystyle{ \mathcal{F} }[/math] is shifted, applying the [math]\displaystyle{ (1,j) }[/math]-shift [math]\displaystyle{ S_{1j} }[/math], [math]\displaystyle{ A\cup\{1\}\in\mathcal{F} }[/math], thus, [math]\displaystyle{ A\in\mathcal{F}_1' }[/math]. [math]\displaystyle{ \square }[/math]

We then bound the size of [math]\displaystyle{ \mathcal{F}_1' }[/math] as:

Lemma 3

If [math]\displaystyle{ \mathcal{F} }[/math] is shifted, then [math]\displaystyle{ |\mathcal{F}_1'|\ge{m_k-1\choose k-1}+{m_{k-1}-1\choose k-2}+\cdots+{m_t-1\choose t-1} }[/math].

Proof. By contradiction, assume that [math]\displaystyle{ |\mathcal{F}_1'|\lt {m_k-1\choose k-1}+{m_{k-1}-1\choose k-2}+\cdots+{m_t-1\choose t-1} }[/math]. Then by [math]\displaystyle{ |\mathcal{F}|=|\mathcal{F}_0|+|\mathcal{F}_1'| }[/math], it holds that [math]\displaystyle{ \begin{align} |\mathcal{F}_0| &=m-|\mathcal{F}_1'|\\ &\gt \left\{{m_k\choose k}- {m_k-1\choose k-1}\right\}+\cdots+\left\{{m_t\choose t}- {m_t-1\choose t-1}\right\}\\ &={m_k-1\choose k}+\cdots+{m_t-1\choose t}, \end{align} }[/math] so that, by the induction hypothesis, [math]\displaystyle{ |\Delta\mathcal{F}_0|\ge{m_k-1\choose k-1}+{m_{k-1}-1\choose k-1}+\cdots+{m_t-1\choose t-1}\gt |\mathcal{F}_1'| }[/math]. On the other hand, by Lemma 2, [math]\displaystyle{ |\mathcal{F}_1'|\ge|\Delta\mathcal{F}_0| }[/math]. Thus [math]\displaystyle{ |\mathcal{F}_1'|\ge|\Delta\mathcal{F}_0|\gt |\mathcal{F}_1'| }[/math], a contradiction. [math]\displaystyle{ \square }[/math]

Now we officially apply the induction. By Lemma 1,

[math]\displaystyle{ |\Delta\mathcal{F}|\ge|\Delta\mathcal{F}_1'|+|\mathcal{F}_1'| }[/math].

Note that [math]\displaystyle{ \mathcal{F}_1'\subseteq{X\choose k-1} }[/math], and due to Lemma 3,

[math]\displaystyle{ |\mathcal{F}_1'|\ge{m_k-1\choose k-1}+{m_{k-1}-1\choose k-2}+\cdots+{m_t-1\choose t-1} }[/math],

thus by the induction hypothesis,

[math]\displaystyle{ |\Delta\mathcal{F}_1'|\ge{m_k-1\choose k-2}+{m_{k-1}-1\choose k-3}+\cdots+{m_t-1\choose t-2} }[/math].

Combining them together, we have

[math]\displaystyle{ \begin{align} |\Delta\mathcal{F}| &\ge |\Delta\mathcal{F}_1'|+|\mathcal{F}_1'|\\ &\ge {m_k-1\choose k-2}+\cdots+{m_t-1\choose t-2}+{m_k-1\choose k-1}+\cdots+{m_t-1\choose t-1}\\ &= {m_k\choose k-1}+{m_{k-1}\choose k-2}+\cdots+{m_t\choose t-1}. \end{align} }[/math]

[math]\displaystyle{ \square }[/math]

Shadows of specific sizes

The definition of shadow can be generalized to the subsets of any size.

Definition

The [math]\displaystyle{ r }[/math]-shadow of [math]\displaystyle{ \mathcal{F} }[/math] is defined as [math]\displaystyle{ \Delta_r\mathcal{F}=\left\{S\mid |S|=r\text{ and }\exists T\in\mathcal{F}, S\subseteq T\right\} }[/math].

Kruskal-Katona Theorem (general version)

Let [math]\displaystyle{ \mathcal{F}\subseteq {X\choose k} }[/math] with [math]\displaystyle{ |\mathcal{F}|=m }[/math], and suppose that [math]\displaystyle{ m={m_k\choose k}+{m_{k-1}\choose k-1}+\cdots+{m_t\choose t} }[/math] where [math]\displaystyle{ m_k\gt m_{k-1}\gt \cdots\gt m_t\ge t\ge 1 }[/math]. Then for all [math]\displaystyle{ r }[/math], [math]\displaystyle{ 1\le r\le k }[/math], [math]\displaystyle{ \left|\Delta_r\mathcal{F}\right|\ge {m_k\choose r}+{m_{k-1}\choose r-1}+\cdots+{m_t\choose t-k+r} }[/math].

Proof. Note that for [math]\displaystyle{ \mathcal{F}\subseteq {X\choose k} }[/math], [math]\displaystyle{ \Delta_r\mathcal{F}=\underbrace{\Delta\cdots\Delta}_{k-r}\mathcal{F} }[/math]. The theorem follows by repeatedly applying the Kruskal-Katona theorem for [math]\displaystyle{ \Delta\mathcal{F} }[/math]. [math]\displaystyle{ \square }[/math]

Erdős–Ko–Rado theorem

Erdős–Ko–Rado Theorem

Let [math]\displaystyle{ \mathcal{F}\subseteq {X\choose k} }[/math] where [math]\displaystyle{ |X|=n }[/math] and [math]\displaystyle{ n\ge 2k }[/math]. If [math]\displaystyle{ \mathcal{F} }[/math] is intersecting, then [math]\displaystyle{ |\mathcal{F}|\le{n-1\choose k-1} }[/math].

Proof by the Kruskal-Katona theorem (Daykin 1974, Clements 1976) [math]\displaystyle{ \square }[/math]