For quite a while I have been planning on writing a blog post on something that I know about more than the average Joe. My crippling case of impostor syndrome in math, however, has kept me from using this space to write anything related to my work in math (you’d be forgiven if you thought from my previous blog posts that I only ever thought about issues of race and inequity). I have been feeling relatively good about myself recently, however, so I figured I would give this a go.
In what follows, I am going to do the best to trace the idea of “support” through different subjects in math and to then talk about the role it plays in modern representation theory. I will do my best to keep things as self-contained as possible, but it is possible it will be necessary for the reader to hold their nose at certain points and be willing to accept results at face value.
We will be talking about some topics in the following that you normally wouldn’t see unless you were at least in the later part of a math degree:
- Basic category theory – If you’re willing to just look up the definitions of a category and a functor between categories (perhaps several times), I will try to keep the abstract nonsense to a minimum.
- Abstract algebra – I bet you didn’t know that there was even MORE algebra out there after the one you took in school! Well good news, there is and it’s my favorite kind of math.
- Some light (algebraic) geometry – The idea of support (and a lot of interesting math, really) includes geometry and I tend to be a visual reasoner so it appeals to me quite a bit to include some discussion towards the end about sheaves. Normally you wouldn’t even hear about these until grad school.
At the end of the day, I am writing about a topic in research-level math so it is not expected that everyone will understand 100% of what I am saying. That being said, don’t let that turn you off if you enjoy math and would like to try to learn more! I will do my best to leave breadcrumbs whenever I can.
We will start by giving some examples of support that many people will have seen before and try to motivate why people care about them.
The first stop of our journey is the one where I (and likely many other math students) first experienced the idea of support. Let’s begin by giving a definition:
Definition (Set support). Let be a set and be any function. Then the support of is defined to be the subset
A very slight generalization of this (one many people see in calculus or analysis or topology) is the following.
Definition (Topological support). Let and be topological spaces (or metric spaces or just if you like). Let be a continuous map. Then the (topological) support of is defined to be the closed subset
Here the line over the top of the set denotes the closure of the set, which is (roughly) the set along with all points that are arbitrarily close to the set. In a sense that can be made rigorous, one tends to like sets that are self-contained, and the closure ensures that one can take limits while staying in the set.
With these first definitions we can begin to build an intuition for what we are looking at. If we are interested in a function where (or in fact any real number) represents “ is not interesting”, a natural question to ask is “where is this function interesting?”
It is important to notice that there isn’t anything implicitly unimportant about the points where a function is zero. In some ways, these are actually quite interesting! There are some places where this comes up naturally, though. Let and let and both be maps . Then one can quite easily consider the function where Now on their face, both and are comprised of an infinite amount of data (they give a real number for all of the infinitely many integers ), but what if I told you that was supported at finitely-many integers? What could you tell me about ?
Well the answer is, you could tell me everything! I mean that in a literal “I give you a piece of paper and you can write down ALL the data of ” kind of way! That is because the function is going to be zero whenever either or is, so it suffices to write out the (now finitely many) values of when and then finish up with saying “and everything else is zero.”
If you have taken a calculus course, you may have seen the following:
Theorem (Extreme value theorem). Let be a continuous function. Then attains a maximum and a minimum value.
As a reminder why this is a meaningful result, notice that if we let be our interval, the function doesn’t attain its maximum value. As you move closer to , gets arbitrarily large. Thus it has no maximum value on this interval. The reason this example doesn’t contradict our theorem is that is not continuous on , since it isn’t even defined for .
It ends up that this theorem can be easily rephrased in terms of supports!
Theorem (Extreme value theorem 2). Let be a continuous function supported on . Then attains its maximum and minimum values (in ).
This indicates how a function supported on can, in some ways, be treated as if it were just defined on (as long as one is careful about what happens outside that interval).
Below we have an example of a (smooth!) function that is supported on the compact set . The blue marks denote where the function is zero. This example was constructed using bump functions. Its minimum value is , attained at and its maximum value is , attained at . Click here or on the image below to play with it yourself.
A natural extension for people who have seen a bit more calculus would be the following:
Theorem. If is a continuous function that is supported on a compact set (we say that is compactly supported), attains its maximum and minimum values.
So the takeaway is this: there are lots of nice results for functions on finite or compact sets that just don’t generalize to more unwieldy ones. If our function is compactly or finitely supported, however, we can this gives us the ability to think of it as a function on a compact or finite set (since everywhere else the function is constantly zero).
The role that support (whether compact or not) plays in all of this is it provides a language to talk about this phenomenon.
Adding depth: Sheaves
Algebraic geometry is pretty wonderful and interesting, but has a connotation for being difficult to understand and full of strange and confusing notation and techniques. It is, unfortunately, a necessary step along the way to understand the support of a module, so we’ll have to think about them for a bit.
In this part I am going to try to keep things as concrete as possible for people who may not be familiar with the intricacies of topology and algebra. For any such neophytes, just keep in mind that you are just getting a (hopefully) illustrative slice of a deep and interesting theory. For any professionals reading this, I apologize in advance for reducing your field to a caricature.
Throughout what follows I will let be a topological space. If you are unfamiliar with topology, the idea is to be a more general version of a geometric space (one in which distances and angles make sense). It does away with these notions, and just keeps the notion about whether two points are “near” one another. A good example to keep in mind is “the skin of a donut”, what mathematicians call a torus.
When I say “open set” or “locally” or “in a neighborhood of a point”, you should think of little oval patches on the surface of this torus.
If you look up the dictionary definition of “sheaf” you will see something like
a quantity of the stalks and ears of a cereal grass or sometimes other plant material bound together
So… something like this:
What does this have to do with math, you ask? Well in my opinion the best math terminology leverages our intuition about “everyday” things1 to give us something tangible to remember when we’re working with our abstract mathematical object. In this case, we think of a (mathematical) sheaf as “bundle” of “stalks”, where there is one stalk for each point in our space .
“What are the stalks made of?”, I hear you asking. Well they can be lots of things! At their simplest they can be sets of things and that is how we will think of them. The way that sheaves were first invented were as sheaves of functions on a space. For instance, given an open set2 on (remember: oval on the torus), the continuous functions on will be These “local” pieces coalesce into the sheaf , which becomes a functor which, for every open set of spits out some data in the form of a set .
What’s the reason to consider sheaves (instead of just single set)? Well let’s stick with the idea of sheaves of functions. If we just look at , the collection of continuous functions on , we completely erase the fact that there may be some functions that are defined on a part of but don’t extend to the whole space. The sheaf approach allows us to keep ALL the local data neatly organized and coherent package that can be analyzed all together.
Let be a sheaf on . As was hinted above, it makes some sense to think of as a collection of things over points. We won’t go into how one can go from the definition above to points, but it involves colimits. Ignoring the details: given a point , we can denote the stalk over as .
This gives us a way to start to think of as a function, so we’re starting to see how we can apply the idea of support to . But we still have a problem: what plays the role of zero? If we want there to be something that acts like zero, we need another object:
Definition (Abelian sheaf). A sheaf on a topological space such that for every open set , is an abelian (commutative) group is called an abelian sheaf.
A large class of useful sheaves arise in this form.
One of the properties of an abelian sheaf is that we have a zero object, which we will denote . This is the trivial group. Using this object, we get the following:
Definition (Sheaf support). Let be a topological space and be an abelian sheaf on . Then the (sheaf) support of is defined to be
Now that we have applied our simple idea to sheaves, let’s see how to make it do work with another object:
If you haven’t seen modules before, it may take some time to digest them fully. They play a very important role in representation theory (and many other fields of algebra), so I will relegate a discussion of modules to a later article.
For now, it suffices to think of modules as vector spaces like . If you have taken a linear algebra or matrix algebra course, you’ve implicitly studied these objects already. The main way representation theorists think of modules is as things that are acted upon. Our mantra is that by studying how an object acts on things like modules, we can ascertain facts about the object itself.
The prime spectrum of a ring
I am going to throw a few more definitions out at this point, but hopefully we can use our intuition to guide us through this part.
Definition (Spectrum of a ring). The spectrum of a ring is the set of prime ideals of . The Zariski topology makes into a topological space.
The spectrum ends up (at least in the simple cases) telling us a lot about the structure of . The key example to keep in mind is , the integers. Here the prime ideals are the ones generated by the prime numbers, so an example is the ideal consisting of the multiples of 3: You may recall that any number can be uniquely written as a product of powers of primes, so that for instance In fact, this can all be phrased in terms of ideals! The above factorizations correspond to ideal factorizations The picture for more complicated rings is less perfect but more interesting. In particular, ideal factorizations no longer have to be unique.
Extracting a topological space from a ring in a canonical way is the first peek at the connection between algebra and geometry (though at this point it is just topology).3 We won’t go into a lot of details here, but the idea is that the open sets are connected to how ideals intersect in the ring, which is related to ideal factorizations.
The spectrum as a scheme
Earlier I described sheaves as naturally arising as the functions on a space.
Sheaves might be “everyday things” if you’re a farmer, I guess. ↩
I am actually interested in doing a series on “Graduate math for non-graduate-students” where I think I could have a lot of fun describing why algebraic geometry is cool and alluring without requiring a lot of prerequisites. ↩