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20 April 2018

LYM Inequality

A simple proof.

We start with universe [n]={1,,n}, and consider families of sets F2[n]. Such a family is called an antichain when its sets are not related by ; that is, F1F2 for all F1,F2F. Sperner's Theorem says that, for any antichain F, |F|(nn/2) and it is a consequence of the more general LYM inequality, which also holds for antichains: FF(n|F|)11

It is easy to see that Sperner follows from LYM because (nn/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 FFFxF(n1|F|)11 for any x[n]. Then 11nx[n]FFFxF(n1|F|)1average induction hypothesis over all x[n]=1nx[n]FF[xF](n1|F|)1use indicator function []=FF1n(n1|F|)1x[n][xF]swap sums=FF1n(n1|F|)1(n|F|)compute the inner sum=FF(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.)

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The problem is in the step that introduces the indicator function []. That can be done only if (n1|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:

Dominic Orchard said...
This comment has been removed by the author.
Dominic Orchard said...

(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 nF rather than xF? I have too much work to be nerd sniped right now though :p

rgrig said...

Hi Dominic! It's induction on the size of the universe.
Sorry for nerd-snipping you.

Lance Fortnow said...

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}.

rgrig said...

@Lance: Nice!

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