Geometric Representation Theory (Lecture 6)
Posted by John Baez
Where would a wizard be without his magic wands?
In mathematics, a ‘magic wand’ is any systematic process that you can apply to big chunks of interesting mathematics and get new, more interesting mathematics. Or — more magical still — it’s a mysterious bunch of tricks that feel like they’d be part of a systematic process if only we understood them better.
What are some magic wands? One of the most famous was stolen from physicists: it’s called quantization. Muttering one of several cryptic spells, you can wave this wand over any mathematical concept related to classical mechanics, and hope that — POOF! — it suddenly transforms into an analogous concept related to quantum mechanics. We’ve had huge success with this over the last century, but it’s still poorly understood.
Another magic wand is categorification: replacing any number by a set with that number of elements, replacing any set by a category whose set of isomorphism classes it is, and so on. You could almost say this blog is a shrine to categorification. It too, is still poorly understood. Perhaps when a magic wand’s powers become fully understood, it ceases to count as ‘magic’!
Yet another magic wand is -deformation — closely related to quantization but not the same. It’s a way of modifying mathematical entities that depends on a parameter . Sometimes this parameter has the physical meaning of … but sometimes it’s better to think of it as a power of a prime number! In fact, -deformation was discovered by Gauss long before the quantum was a twinkle in Planck’s eye.
When you have two magic wands at your disposal, you can ask if they commute. First wave one, then the other. First wave the other, then the one. Does the same magic occur? Or at least isomorphic magics?
In lecture 6 of the Geometric Representation Theory seminar, I wave two magic wands — categorification and -deformation — at a humble mathematical entity: the binomial coefficient. It seems they commute. But, puzzles abound!

-
Lecture 6 (Oct. 16) -
John Baez on categorifying and -deforming the theory of multinomial
coefficients.
A surprising fact: -multinomial coefficients are actually polynomials
in with natural number coefficients. It suffices to prove this
for -binomial coefficients, since any -multinomial coefficient is a product of
-binomial coefficients. Since a -binomial coefficient is the number of points in a Grassmanian over (the field with elements), it’s enough to decompose this
Grassmannian into ‘Bruhat classes’, and show that each of these
is isomorphic (as a set) to for some k.
For this, we show that each Bruhat class corresponds to a set of
matrices in
reduced
row echelon form. Example: the Young diagram
for which -flags on correspond to 2d subspaces of
, or equivalently, lines in projective 3-space.
Another surprising fact: any -multinomial is actually a ‘palindromic’
polynomial in . The closure of a Bruhat class is called a Schubert cell, and this palindromic property follows from Poincaré duality, since Schubert cells are a basis for the cohomology of the Grassmannian over .
Posted at October 26, 2007 9:39 PM UTC
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Re: Geometric Representation Theory (Lecture 6)
Have you ever thought of using magic wands from electrical engineering [EE]?
Paul AM Dirac had no degree in physics, but a BS in EE and PhD in mathematics. This allowed him to do things that were considered not to be rigorous.
http://nobelprize.org/nobel_prizes/physics/laureates/1933/dirac-bio.html
Richard Bellman developed dynamic programming or optimization.
http://en.wikipedia.org/wiki/Dynamic_programming
When a colleague remarked that this was not rigorous, Bellman reportedly responded, “Of course not. It’s not even precise. A good principle should guide the intuition.”
Re: Geometric Representation Theory (Lecture 6)
John, your lecture looks interesting and I will try to find some time to watch it, but before I do can you please clarify something:
Is there a good mathematical reason why you consider the classical cohomology of a complex Grassmannian as opposed to its quantum cohomology? See, for example, the paper Quantum Schubert Calculus by Aaron Bertram.
Re: Geometric Representation Theory (Lecture 6)
It was sort of fun watching you guys battle your way through the last bit, using the row echelon business to prove that belongs to . But your row echelon forms looked a little “left-handed” to me, and I’m wondering whether that made it any more confusing to the people out in TV Land, who are used to reduced row echelon forms “done right”.
I’d like to replay what you said in slightly different words (obviously with the same mathematical content). My preference would be to start with what “almost always” happens when you carry out the reduction process (i.e., start with the “generic” case), and then get more and more special.
Almost always, you can finagle your rank two -matrix so that the -entry is nonzero, which we then rescale to make it 1, and then kill off the (21)-entry. Then, almost always the (22)-entry will be nonzero; we rescale to make that 1, and then kill off the (12)-entry. There’s nothing more to be done: we’re in reduced form, and the class of matrices here looks like
(where stands for ‘anything’). Or maybe in the second sentence of the last paragraph, we had 0 for the (22)-entry, but something nonzero for the (23)-entry. Rescale to make that 1, then kill off the (13)-entry, and then you’re done. The class of matrices here looks like
Or maybe both the (22)- and (23)-entries are 0; then the last (24)-entry has to be nonzero, and eventually we reduce to
Or, maybe when you start, you have 0’s in the first column (but from there on it’s generic):
Or, it might be
Or, in the most unlikely case of all, you have nothing but 0’s in the first two columns, and in reduced form you wind up with
This is what reduced row echelon forms usually look like (at least in textbooks I am familiar with). Now, in hindsight, we can figure out the flag we use to get the corresponding Bruhat cells: I think it’s
where is the vector with 1 in the -th slot, and zeroes elsewhere. The cases where the second row is should be where the given projective line passes through the point of the flag. So, in order of the reduced row echelon forms as given above, I think we get
-
generic
-
Line intersects line of flag
-
Line intersects point of flag
-
Line in plane of flag
-
Line in plane and through point of flag
-
So it look like my order is opposite to yours in every respect!
Re: Geometric Representation Theory (Lecture 6)
There should be a nice variant of ‘reduced row echelon form’ that describes the Schubert cells not just for Grassmannians, but also more general flag varieties. The difference should be something like this: you group the rows of your matrix into ‘bands’, and you’re only allowed to add a multiple of one row to another if it lies in the same or higher band.
Does this idea have a name?
As we’ll see next time, the Schubert cells for Grassmannians correspond to Young diagrams in a nice way — and you can instantly read off the Young diagram from the reduced row echelon form!
So: what’s the correspondingly cute notation for the Schubert cells of a general (i.e., partial) flag variety?
And while I’m at it: what’s the most fun and readable treatment of the Schubert calculus? What’s the most thorough one? What’s the most notation-ridden and tedious one?
An elementary proof that q-multinomials are palindromes in N[q]
I was amused to watch your rather painful check that the -multinomials are in fact palindromes in . Here’s an argument that a middle-school student could follow.
I begin with an easy observation: if divides , then divides . Indeed, let . Then
(1)
So, now, let’s consider some binomial coefficient -choose-. From combinatorics, we know that the integer binomial is in fact an integer: this observation gives us a collection of explicit cancellations between numbers in the denominator of .
So, say you want to evaluate a -multinomial. You do the integer calculation, which consists first of a series of divisions. Each division can be promoted into a division of polynomials in as above. So at the end of the day, a -multinomial is a product of palindromes in . Explicitly, it’s a product of “integers” of the form for various and . The product of these is definitely also in .
Moreover, there’s an easy algebraic description of palindromic polynomials. Let be a polynomial of degree . Then it is a palindrome iff
(2)
I.e. is a palindrome iff is symmetric under . But the product of symmetric functions is itself symmetric, and we can keep track of degrees: the product of palindromic polynomials is palindromic.
Not that the Bruhat classes aren’t themselves interesting, but -deformed arithmetic works for more elementary reasons.
(Incidentally, is there a good way to get the Mathbb fonts here? I’d love to write “{\mathbb N}” rather than .)
Re: Geometric Representation Theory (Lecture 6)
The comment by Theo above that there are direct ways of establishing that lies in does remind me of another tack to take, which has a nice combinatorial (or categorified) interpretation.
Namely, there’s a -Pascal’s triangle for :
To prove this combinatorially, say you have a -plane in . Let be a coordinate hyperplane of dimension . Then either your -plane is contained in (and there are ways that can happen), or it intersects in an (affine) -dimensional space. These affine spaces can be described in “slope-intercept” form: there are possible slopes (where “slope” means “parallel translate of the -space through a chosen origin of ”), and degrees of freedom in choosing the “intercept”. This gives the Pascal formula above.
We then immediately infer by induction that belongs to .
Re: Geometric Representation Theory (Lecture 6)
Have you ever thought of using magic wands from electrical engineering [EE]?
Paul AM Dirac had no degree in physics, but a BS in EE and PhD in mathematics. This allowed him to do things that were considered not to be rigorous.
http://nobelprize.org/nobel_prizes/physics/laureates/1933/dirac-bio.html
Richard Bellman developed dynamic programming or optimization.
http://en.wikipedia.org/wiki/Dynamic_programming
When a colleague remarked that this was not rigorous, Bellman reportedly responded, “Of course not. It’s not even precise. A good principle should guide the intuition.”