We start with universe [n]={1,…,n}, and consider families of sets F⊆2[n]. Such a family is called an antichain when its sets are not related by ⊂; that is, F1⊄F2 for all F1,F2∈F. Sperner's Theorem says that, for any antichain F, |F|≤(n⌊n/2⌋) and it is a consequence of the more general LYM inequality, which also holds for antichains: ∑F∈F(n|F|)−1≤1
It is easy to see that Sperner follows from LYM because (n⌊n/2⌋)≥(nk) for all n and k. It is perhaps not so easy to see why the LYM inequality holds. Here is a proof by induction on n. The base case n=0 is easy. Otherwise, we can assume the induction hypothesis ∑FF∈Fx∉F(n−1|F|)−1≤1 for any x∈[n]. Then 1≥1n∑x∈[n]∑FF∈Fx∉F(n−1|F|)−1average induction hypothesis over all x∈[n]=1n∑x∈[n]∑F∈F[x∉F](n−1|F|)−1use indicator function [⋅]=∑F∈F1n(n−1|F|)−1∑x∈[n][x∉F]swap sums=∑F∈F1n(n−1|F|)−1(n−|F|)compute the inner sum=∑F∈F(n|F|)−1property of binomial coefficients
We just proved the LYM inequality. Or did we? Where did I use that F is an antichain? I didn't, so the proof above must be wrong. Can you find the mistake and fix it? (Hint: The mistake is subtle but silly, the fix is simple.)
The problem is in the step that introduces the indicator function [⋅]. That can be done only if (n−1|F|)≠0. This happens most of the time, except when |F|=n. Such a case must be treated separately. If |F|=n then |F|=1 because F is an antichain. (There — we used the antichain property.) Thus, the inequality holds for this case as well.
5 comments:
(recommend to try to get math to typeset right)
Haven't had a chance to think it through fully, but my immediate reaction was that the inductive hypothesis is wrong. If you are doing induction on n then surely you want to say n∉F rather than x∉F? I have too much work to be nerd sniped right now though :p
Hi Dominic! It's induction on the size of the universe.
Sorry for nerd-snipping you.
Here's an alternate proof I came up with, though I'm guessing it's already know. Pick a random permutation sigma of {1,...,n}. Let A_F be the event that F = {sigma(1),...,sigma(|F|)}. Probability of A_F is 1/(n choose |F|) and A_F and A_G can't both be true for different F and G in {\cal F}.
@Lance: Nice!
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