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    Mathematics

    My Favorite Theorem

    Join us as we spend each episode talking with a mathematical professional about their favorite result. And since the best things in life come in pairs, find out what our guest thinks pairs best with their theorem.

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    Copyright: © Kevin Knudson & Evelyn Lamb

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    Latest Episodes:
    Episode 45 - Your Flash Favorite Theorems Aug 08, 2019
    Show notes

    Episode 45 - Your Flash Favorite Theorems Kevin Knudson & Evelyn Lamb Download Kevin Knudson: 1-2-3 Kevin Knudson and Evelyn Lamb: Welcome to My Favorite Theorem! KK: Okay, good. EL: Yeah. [Theme music] KK: So we’re at the JMM. EL: Yeah, we’re here at the Joint Math Meetings. They’re in Baltimore this year. The last time I was at the Joint Meetings in Baltimore I got really sick, but so far I seem to not be sick. KK: That’s good. You’ve only been here a couple of days, though. EL: Yeah. There’s still time. KK: Yeah, so I’ve only been to the Joint Meetings one other time in my life, 20 years ago as a postdoc in Baltimore. I’ve just got a thing for Baltimore, I guess. EL: Yeah, I guess so. KK: So people may have seen this on Twitter. Fun fact: this is our first time meeting in person. EL: Yeah. KK: And you’re every bit as charming in real life as you are over video. EL: And you’re taller than I expected because my first approximation of all humans is that they are my height, and you are not my height. KK: But you’re not exceptionally short. EL: No. KK: You’re actually above average height, right? EL: I’m about average for a woman, which makes me below average for humans. KK: Well, if we’re going to the Netherlands, for example, I’m below average for the Netherlands. EL: Yes. KK: So I’m actually leaving today. I was only here for a couple of days. I was here for the department chairs workshop. You’re here through when? EL: I’m leaving on Friday, tomorrow. Yeah, while we’ve been here we’ve been collecting flash favorite theorems where people have been telling us about their favorite theorem in a small amount of time. So yeah, we’re excited to share those with you. KK: Yeah, this is going to be a good compilation. I’m going to try to get a couple more before I leave town. We’ll see what happens. EL: Yeah. All right. KK: Enjoy. EL: I am here with Eric Sullivan. Can you tell us a little bit about yourself? Eric Sullivan: Yeah, I'm an associate professor at Carroll College in Helena, Montana, lover of all things mathematics. EL: And here with me in the Salt Lake City Airport, I assume catching a connecting flight to the Joint Math Meetings. ES: You got it. EL: All right, and what is your favorite theorem, or the favorite theorem you'd like to tell me about right now? ES: Oh, I have many favorite theorems, but the one that's really coming to mind right now, especially since I'm teaching complex analysis this semester, are the Cauchy-Riemann equations. EL: Very nice. ES: Giving us a beautiful connection between analytic functions, and ultimately, harmonic functions. Really lovely. And it seems like a mystery to my students when they first see it, but it's beautiful math. EL: Yeah, it is. They are kind of mysterious, even after you've seen them for a while. It's like, why does this balance so beautifully? ES: Right? And the way you get there with the limit, so I'm just going to take the limit going one way, then I’ll take the limit going the other way and voila, out comes these beautiful partial differential equations. EL: Yeah, very lovely. And I know I'm putting you on the spot. But do you have a pairing for this theorem? ES: Ooh, a pairing? Oh boy, something was a very complex taste. Maybe chili. EL: Okay. ES: I’ll say chili because there's all sorts of flavors mixed in with chili, and complex analysis seems to mix all sorts of flavors together. EL: All right, I like it. Well, thank you. This is the first lightning My Favorite Theorem I'm recording so far at the joing meetings, or even before, on the way, so yeah, thanks for joining me. Courtney Gibbons: I'm Courtney Gibbons. I'm a professor at Hamilton College in upstate New York. And my favorite theorem is Hilbert’s Nullstellensatz, which translates to zero point theorem, but if you run it through Google Translate, it's actually quite beautiful. It's like the “empty star theorem” or something like that. It's very astronomical. And I love this theorem because it's one of those magical theorems that connect one area that I love, algebra, to another area that I don't really understand, but would like to love, geometry. And I find that in my classes, when I ask someone, “What's a parabola?” I have a handful of students who do some sort of interpretive dance. And I have a handful of students were like, “Oh, it's like y equals some x squared stuff.” And I'm like, “I'm with you.” I think of the equation. And some people think of the curve, the plot, and that's the geometric object, and the Nullstellensatz tells you how to take ideals and relate them to varieties. So it connects algebra and geometry. And it's just gorgeous, and the proof is gorgeous, and everything about it is wonderful, and David Hilbert was wonderful. And if I were going to pair it with something, I’d probably pair it with a trip to an observatory, so that you could go appreciate the beauty of the stars, and think about the wonderful connectedness of all of mathematics and the universe. And maybe you should have, like, a beer or something too. EL: Why not? CG: Yeah. Why not? Exactly. EL: Good. Well, thank you. Absolutely. KK: All right, JMM flash theorem time. Introduce yourself, please. Shelley Kandola: Hi. My name is Shelly Kandola. I'm a grad student at the University of Minnesota. KK: And it’s warmer here than where you are usually. SK: Yeah, it's 15 degrees in Minnesota right now. KK: That’s awful. SK: Yeah. KK: Well anyway, we’ve got to be quick here. What's your favorite theorem? SK: The Banach-Tarski paradox. KK: This is an amazing result that I still don't really understand and I can't wrap my head around. SK: Yeah, you've got a solid sphere, a filled-in S2, and you can cut it into four pieces using rigid motions, and then put them back together and get two solid spheres that are the same size as the original. KK: Well, theoretically, you can do this, right? This isn't something you can actually do, is it? SK: Physically no, but with the power of group theory, yes. KK: With the power of group theory. SK: The free group on two generators. KK: Why do you like this theorem so much? SK: So I like it because it was the basis of my senior research project in college. KK: It just seems so weird it was something you should think about? SK: Yeah, it intrigued me. It's a paradox. And it's the first theorem I dove really deep into, and we found a way to generalize it to arbitrarily many dimensions with one tweak added. KK: Cool. So what does one pair with the Banach-Tarski paradox? SK: One of my favorite Futurama episodes. There's this one episode where there's a Banach-Tarski duplicator, and Bender jumps into the duplicator, and he makes two more, and he wants to build an army of himself. KK: Sure. SK: But every time he jumps in, the two copies that come out, are half the size of the original. He ends up with an army of nanobots. It contradicts the whole statement of the paradox that you're getting two things back that are the same size as the original. KK: Although an army of Benders might be fun. SK: Yeah, they certainly wreak havoc. KK: Don’t we all have a little inner Bender? SK: Oh yeah. He's powered by beer. KK: Well, thanks for joining us. You gave a really good talk this morning. SK: Thanks. KK: Good luck. SK: Thank you for having me. KK: Sure. David Plaxco: My name is David Plaxco. I'm a math education researcher at Clayton State University. And my favorite theorem is really more of an exercise, I think most people would think. It's proving that the set of all elements in a group that conjugate with a fixed element is a subgroup of the group. I'll tell you why. Because in my dissertation, that exercise was the linchpin in understanding how students can learn by proving. EL: Okay. DP: So I was working with a student. He had read ahead in the textbook and knew that not all groups are commutative, so you can't always commute any two elements you feel like. And he generalized this to thinking about inverses. He didn’t think that every inverse was necessarily two-sided, which in a group you are. Anyway, so he was trying to prove that that set was a subgroup and came to this impasse because he wanted to left cancel and right cancel with inverses and could only do them on one side. And then he started to question, like, maybe I'm just crazy, like maybe you can use the same inverse on both sides. And then he proved it himself using associativity. So he made, like, I call it John’s lemma, he came up with this kind of side proof to show that, well, if you're associative and you have a right inverse and a left inverse, then those have to be the same. And then he came back and was able to left and right cancel at free will any inverse, and then proved that it was a subgroup, so through his own proof activity, he was able to change his own conceptual understanding about what it means to be an inverse, like how groups work, all these things, and it gave him so much more power moving forward. So that's how that theorem became my favorite theorem because it gave me insight into how individuals can learn. EL: Nice. And do you have a pairing for this theorem? DP: My diploma. Because it helped me get it. EL: That seems appropriate. Thanks. DP: Thanks. Terence Tsui: So I'm Terrance, and I'm currently a final year undergraduate studying in Oxford. My favorite theorem is actually a really elegant proof of Euler’s identity on the Riemann zeta function. We all know that the Riemann zeta function is defined in a way of the sum of 1/ks where k runs across all the natural numbers. But at the same time, Euler has given a really good other formulation: we say status is the same as the infinite product of 1-1/ps, where p runs across the primes. And then it's really interesting, because if you look at you see, on one hand, an infinite some, and on the other hand, you have an infinite product. And it’s very rare that we see that infinite sums and infinite products actually coincide. And they’re only there because it is a function that actually works on nearly every s larger than 1. And that means that this beautiful, elegant identity actually runs correct for infinitely many values. And the most interesting thing about this theorem is that the proof to it could be done probabilistically, where we consider some certain particular events, and we realize that the Riemann zeta series sum is actually equivalent to finding a certain intersection of infinitely many independent events. And first it is just an infinite product of certain events. And first we have the Riemann zeta function equalling a particular infinite product. And I think that is something that is really out of out of our imagination, because not only does it link two things—a sum to an infinite product, but at the same time, the way that it proves it comes from somewhere we could not even imagine, which is from probability. So if I need to pair this theorem with something, I would say it’s like a spider web, because you can see that there's very intricate connections and that things connect to each other, but in the most mysterious ways. ELL: Cool. Well, thanks. TT: Thank you. Courtney Davis: So Hi, I'm Courtney Davis. I am an associate professor at Pepperdine University out in LA. EL: Okay. And I hear that we have a favorite model, not a favorite theorem from you. CD: Yes. So I'm a math biologist. So I'm going to say the obvious one, which is SIR modeling, because it is the entry way into getting to do this cool stuff. It’s the way that I get to show students how to write models. It's the first model I ever saw that had biology in it. And it's something that is ubiquitous and used widely. And so despite being the first thing everyone learns, it's still the first thing everyone learns. And that's what makes it interesting to me. EL: Yeah. And and can you kind of just sum up in a couple sentences what this model is, what SIR means? CD: Yeah. So SIR is you are modeling the spread of disease through a susceptible (S) population through infected and into recovered or immune, and you can change that up quite a lot. There are a lot of different ways to do it. It's not one fixed model. And it's all founded on the very simple premise that when two individuals run into each other in a population, that looks like multiplication. And so you can take multiplication, and with that build all the interactions that you really need, in order to capture what's actually happening in a population that at least is well mixed, so that you have a big room of people moving around about it, for instance. EL: Okay. And I'm going to spring something on you, which is that usually we pair something with our theorem, or in this case model, so we have our guests, you know, choose a food, beverage, piece of art, or anything. Is there anything that you would suggest that pairs well with SIR? CD: With an SIR model, I would say, a paint gun. EL: Okay. CD: I don't know that that's what you're looking for. EL: That’s great. CD: Simply because running around and doing pandemic games or other such things is also a common way to get data on college campuses so that you can introduce students, and they can parameterize their models by paint guns or water guns or something like that. EL: Oh, cool. I like it. Thank you. CD: Absolutely. Thank you. Jenny Kenkel: I’m Jenny Kenkel. I'm a graduate student at the University of Utah. I study commutative algebra. My favorite theorem is this isomorphism between a particular local cohomology module and an injective module: The top local cohomology of a Gorenstein ring is isomorphic to the injective hull of its residue field. But I was thinking that maybe it would pair really well with like, a dark chocolate and a sharp cheddar, because these two things are isomorphic, and you would never expect that. But then they go really well together, just in the same way that I think a dark chocolate and a sharp cheddar seem kind of like a weird pairing, but then it's amazing. Also, they're both beautiful. EL: Nice, thank you. JK: Thank you. Dan Daly: My name is Dan Daly. And I am the interim chair of the Department of Mathematics at Southeast Missouri State University. KK: Southeast—is that in the boot? DD: That is close to the boot heel. It's about two hours south of St. Louis. KK: Okay. I'm a Cardinals fan. So I'm ready, we’ve got something here. So what's your favorite theorem? DD: So my favorite theorem is actually the classification of finite simple groups. KK: That’s a big theorem. DD: That is a very big, big, KK: Like 10,000 pages of theorem. DD: At least KK: Yeah. So what draws you to this? Is it your area? DD: So I am interested in algebraic and combinatorics, and I am generally interested in all things related to permutations. KK: Okay. DD: And one of the things that drew me to this theorem is that it's such an amazing, collaborative effort and one of the landmarks of 20th century mathematics. KK: Big deal. Yeah. DD: And, you know, it just to me, it seems such a such an amazing result that we can classify these building blocks of finite groups. KK: Right. So what does one pair this with? DD: So I think since it's such a collaborative effort, I'm going to pair it with Louvre museum. KK: The Louvre, okay. DD: Because it's a collection of all of these different results that are paired together to create something that is really, truly one of a kind. KK: I’ve never been. Have you? DD: I have. It’s a wonderful place. Yeah. It’s a fabulous place. One of my favorite places. KK: I’m going to wait until I can afford to rent it out like Beyonce and Jay Z. DD: Yeah, right. KK: All right, well thanks,…

    Full show notes at the publisher

    Episode 44 - James Propp Jul 11, 2019
    Show notes

    Episode 44 - James Propp Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello, and welcome to My Favorite Theorem, a math podcast. I'm Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City, Utah. And this is your other host. Kevin Knudson: I’m Kevin Knudson, professor of mathematics at the University of Florida. How's it going? EL: All right, yeah. Up early today for me. You know, you’re on the East Coast, and I'm in the Mountain Time Zone. And actually, when my husband is on math trips — sorry, if I'm on math trips on the East Coast, and he's in the mountain time zone, then we have, like, the same schedule, and we can talk to each other before we go to bed. I'm sort of a night owl. So yeah, it's early today. And I always complain about that the whole time. KK: Sure. Is he a morning person? EL: Yes, very much. KK: So Ellen, and I are decidedly not. I mean, I'd still be in bed, really, if I had my way. But you know, now that I'm a responsible adult chair of the department, I have to—even in the summer—get in here to make sure that things are running smoothly. EL: But yeah, other than the ungodly hour (it’s 8am, so everyone can laugh at me), everything is great. KK: Right. Cool. All right, I’m excited for this episode. EL: Yes. And today, we're very happy to have Jim Propp join us. Hi, Jim, can you tell us a little bit about yourself? Jim Propp: Yeah, I'm a math professor at UMass Lowell. My research is in combinatorics, probability, and dynamical systems. And I also blog and tweet about mathematics. KK: You do. Your blog’s great, actually. EL: Yeah. KK: I really enjoy it, and you know, you're smart. Once a month. EL: Yes. That that was a wise choice. Most months, I think on the 17th, Jim has an excellent post at Math Enchantments. KK: Right. EL: So that’s a big treat. I think somehow I didn't realize that you did some things with dynamical systems too. I feel like I'm familiar with you in, like, the combinatorics kind of world. So I learned something new already. KK: Yup. JP: Yeah, I actually did my PhD work in ergodic theory. And after a few years of doing a postdoc in that field, I thought, “No, I'm going to go back to combinatorics," which was sort of my first love. And then some probability mixed into that. KK: Right. And actually, we had some job candidates this year in combinatorics, and one of them was talking about—you have a list of problems, apparently, that's famous. I don't know. JP: Oh, yes. Tilings. Enumeration of tilings. KK: That’s right. It was a talk about tilings. Really interesting stuff. JP: Yeah, actually, I should say, I have gone back to dynamical systems a little bit, combining it with combinatorics. And that's a big part of what I do these days, but I won't be talking about that at all. EL: Okay. And what is your favorite theorem? JP: Ah, well, I've actually been sort of leading you on a bit because I'm not going to tell you my favorite theorem, partly because I don't have a favorite theorem. KK: Sure. JP: And if I did, I wouldn't tell you about it on this podcast, because it would probably have a heavy visual component, like most of my favorite things in math, and it probably wouldn't be suited to the purely auditory podcast medium. KK: Okay, so what are you gonna tell us? JP: Well, I could tell you about one theorem that I like that doesn't have much geometric content. But I'm not going to do that either. EL: Okay, so what bottom of the barrel… JP: I’m going to tell you about two theorems that I like, okay, they’re sort of like twins. One is in continuous mathematics, and one is in discrete mathematics. KK: Great. JP: The first one, the one in continuous mathematics, is pretty obscure. And the second one, in discrete mathematics, is incredibly obscure. Like nobody’s named it. And I've only found it actually referred to, stated as a result in the literature once. But I feel it's kind of underneath the surface, making a lot of things work, and also showing resemblances between discrete and continuous mathematics. So these are, like, my two favorite underappreciated theorems. EL: Okay. KK: Oh, excellent. Okay, great. So what have we got? JP: Okay, so for both of these theorems, the underlying principle, and this is going to sound kind of stupid, is if something doesn't change, it’s constant. EL: Okay. Yes, that is a good principle. JP: Yeah. Well, it sounds like a tautology, because, you know, doesn't “not changing” and “being constant” mean the same thing? Or it sounds like a garbled version of “change is the only constant.” But no, this is actually a mathematical idea. So in the continuous realm, when I say “something,” what I mean is some differentiable function. And when I say “doesn't change,” I mean, has derivative zero. KK: Sure. JP: Derivatives are the way you measure change for differentiable functions. So if you’ve got a differentiable function whose derivative is zero—let’s assume it's a function on the real line, so its derivative is zero everywhere—then it's just a constant function. KK: Yes. And this is a corollary of the mean value theorem, correct? JP: Yes! I should mention that the converse is very different. The converse is almost a triviality. The converse says if you've got a constant function, then its derivative is zero. KK: Sure. JP: And that just follows immediately from the definition of the derivative. But the constant value theorem, as you say, is a consequence of the mean value theorem, which is not a triviality to prove. KK: No. JP: In fact, we'll come back later to the chain of implications that lead you to the constant value theorem, because it's surprisingly long in most developments. KK: Yes. JP: But anyway, I want to point out that it's kind of everywhere, this result, at least in log tables— I mean, not log tables, but anti-differentiation tables. If you look up anti-derivatives, you'll always see this “+C” in the anti-derivative in any responsible, mathematically rigorous table of integrals. EL: Right. JP: Because for anti-derivatives, there's always this ambiguity of a constant. And those are the only anti-derivatives of a function that's defined on the whole real line. You know, you just add a constant to it, no other way of modifying the function will leave its derivative alone. And more generally, when you've got a theorem that says what all the solutions to some differential equation are, the theorem that guarantees there aren't any other solutions you aren't expecting is usually proved by appealing to the constant value theorem at some level. You show that something has derivative zero, you say, “Oh, it must be constant. “ KK: Right. JP: Okay. So before I talk about how the constant value theorem gets proved, I want to talk about how it gets used, especially in Newtonian physics, because that's sort of where calculus comes from. So Newtonian physics says that if you know the initial state of a system, you know, of a bunch of objects—you know their positions, you know their velocities—and you know the forces that act on those objects as the system evolves, then you can predict where the objects will be later on, by solving a differential equation. And if you know the initial state and the differential equation, then you can predict exactly what's going to happen, the future of the system is uniquely determined. KK: Right. JP: Okay. So for instance, take a simple case: you’ve got an object moving at a constant velocity. And let's say there are no forces acting on it all. Okay? Since there are no forces, the acceleration is zero. The acceleration is the rate of change of the velocity, so the velocity has derivative zero everywhere. So that means the velocity will be constant. And the object will just keep on moving at the same speed. If the constant value theorem were false, you wouldn't really be able to make that assertion that, you know, the object continues traveling at constant velocity just because there are no forces acting on it. KK: Sure. JP: So, kind of, pillars of Newtonian physics are that when you know the derivative, then you really know the function up to an ambiguity that can be resolved by appealing to initial conditions. EL: Yeah. KK: Sure. JP: Okay. So this is actually telling us something deep about the real numbers, which Newton didn't realize, but which came out, like, in the 19th century, when people began to try to make rigorous sense of Newton's ideas. And there's actually a kind of deformed version of Newton's physics that's crazy, in the sense that you can't really predict things from their derivatives and from their initial conditions, which no responsible physicist has ever proposed, because it's so unrealistic. But there are some kind of crazy mathematicians who don't like irrational numbers. I won't name names. But they think we should purge mathematics of the real number system and all of these horrible numbers that are in it. And we should just do things with rational numbers. And if these people tried to do physics just using rational numbers, they would run into trouble. EL: Right. JP: Because you can have a function from the rational numbers to itself, whose derivative is zero everywhere—with derivative being defined, you know, in the natural way for functions from the rationals to itself—that isn't a constant function. KK: Okay. JP: So I don't know if you guys have heard this story before. KK: This is making my head hurt a little, but okay. Yeah. EL: Yeah, I feel like I have heard this, but I cannot recall any details. So please tell me. JP: Okay, so we know that the square root of two is irrational, so every rational number, if you square it, is either going to be less than two, or greater than two. KK: Yes. JP: So we could define a function from the rational numbers to itself that takes the value zero if the input value x satisfies the inequality x squared is less than 2 and takes the value 1 if x squared is bigger than two. EL: Yes. JP: Okay. So this is not a constant function. KK: No. JP: Right. Okay. But it actually is not only continuous, but differentiable as a function of the EL: Of the rationals… JP: From the rationals to itself. KK: Right. The derivative zero but it's not constant. Okay. JP: Yeah. Because take any rational number, okay, it's going to have a little neighborhood around it avoiding the square—avoiding the hole in the rational number line where the square root of 2 would be. And it's going to be constant on that little interval. So the derivative of that function is going to be zero. KK: Sure. JP: At every rational number. So there you have a non-constant function whose derivative is zero everywhere. Okay. And that's not good. KK: No. JP: It’s not good. for math. It's terrible for physics. So you really need the completeness property of the reals in a key way to know that the constant value theorem is true. Because it just fails for things like the set of rational numbers. EL: Right. KK: Okay. JP: This is part of the story that Newton didn't know, but people like Cauchy figured it out, you know, long after. KK: Right. JP: Okay. So let's go back to the question of how you prove the constant value theorem. EL: Yeah. JP: Actually, I wanted to jump back, though, because I feel like I wanted to sell a bit more strongly this idea that the constant value theorem is important. Because if you couldn't predict the motions of particles from forces acting on those particles, no one would be interested in Newton's ideas, because the whole story there is that it is predictive of what things will do. It gives us a sort of clockwork universe. KK: Sure.JP: So Newton's laws of motion are kind of like the rails that the Newtonian universe runs on, and the constant value theorem is what keeps the universe from jumping off those rails. KK: Okay. I like that analogy. That’s good. JP: That’s the note I want to end on for that part of the discussion. But now getting back to the math of it. So how do you prove the constant value theorem? Well, you told me you prove it from the mean value theorem. Do you remember how you prove the mean value theorem? KK: You use Rolle’s theorem? EL: Just the mean value theorem turned sideways! KK: Sort of, yeah. And then I always joke that it’s the Forrest Gump proof. Right? You draw the mean value theorem, you draw the picture on the board, and then you tilt your head, then you see that it's Rolle’s theorem. Okay, but Rolle’s theorem requires, I guess what we sometimes call in calculus books Fermat’s theorem, that if you have a differentiable function, and you're at a local max or min, the derivative is equal to zero. Right? JP: Yup. Okay, actually, the fact that there exists even such a point at all is something. KK: Right. JP: So I think that's called the Extreme Value Theorem. KK: Maybe? Well, the Extreme Value Theorem I always think of as as—well, I'm a topologist—that’s the image of a compact set is is compact. JP: Okay. KK: Right. Okay. So they need to know what the compact sets of the real line are. JP: You need to know about boundedness, stuff like that, closedness. KK: Closed and bounded, right. Okay. You're right. This is an increasingly long chain of things that we never teach in Calculus I, really. JP: Yeah. I've tried to do this in some honors classes with, you know, varying levels of success. KK: Sure. JP: There’s the boundedness theorem, which says that, you know, a continuous function is bounded on a closed interval. But then how do you prove that? Well, you know, Bolzano-Weierstrass would be a natural choice if you're teaching a graduate class, maybe you prove that from the monotone convergence theorem. But ultimately, everything goes back to the least upper bound property, or something like it. KK: Which is an axiom. JP: Which is an axiom, that’s right. But it sort of makes sense that you'd have to ultimately appeal to some heavy-duty axiom, because like I said, for the rational numbers, the constant value theorem fails. So at some point, you really need to appeal to the the completeness of the reals. EL: Yeah, the structure of the real numbers. KK: This is fascinating. I've never really thought about it in this much detail. This is great. JP: Okay. Well, I'm going to blow your mind… KK: Good! JP: …because this is the really cool part. Okay. The constant value theorem isn't just a consequence of the least upper bound property. It actually implies the least upper bound property. KK: Wow. Okay. JP: So all these facts that this, this chain of implications, actually closes up to become a loop. KK: Okay. JP: Each of them implies all the others. KK: Wow. Okay. JP: So the precise statement is that if you have an ordered field, so that’s a number system that satisfies the field axioms: you've got the four basic operations of pre-college math, as well as inequality, satisfying the usual axioms there. And it has the Archimedean property, which we don't teach at the pre-college level. But informally, it just says that nothing is infinitely bigger or infinitely smaller than anything else in our number system. Take any positive thing, add it to itself enough times, it becomes as big as you like. KK: Okay. JP: You know, enough mice added together can outweigh an elephant. KK: Sure. EL: Yeah. JP: That kind of thing. So if you've got an ordered field that satisfies the Archimedean property, then each of those eight propositions is equivalent to all the others. KK: Okay. JP: So I really like that because, you know, we tend to think of math as being kind of linear in the sense that you have axioms, and from those you prove the…

    Full show notes at the publisher

    Episode 43 - Matilde Lalin Jun 13, 2019
    Show notes

    Episode 43 - Matilde Lalin Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a podcast about theorems and math and all kinds of things. I'm one of your host,s Kevin Knudson, professor of mathematics at the University of Florida. Here is your other host. Evelyn Lamb: Hi, I'm Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City. How are you today? KK: I have a sunburn. EL: Yeah. Can’t sympathize. KK: No, no, I was, you know, Ellen and I went out birdwatching on Saturday, and it didn't seem like it was sunny at all, and I didn't wear a hat. So I got my head a little sunburned. And then yesterday, she was doing a print festival down in St. Pete. And even though I thought we were in the shade—look my, arms. They're like totally red. I don’t know. This is what happens. EL: You know, March in Florida, you really can’t get away without SPF. KK: No, you really can't. You would think I would have learned this lesson after 10 years of living here, but it just doesn't work. So anyway. Yeah. How are you? EL: Oh, I'm all right. Yeah. Not sunburned. KK: Okay. Good for you. Yeah. I'm on spring break. So, you know, I'm feeling pretty good. I got some time to breathe at least. So anyway, enough about us. This is actually a podcast where we invite guests on instead of boring the world with our chit chat. Today, we're pleased to welcome Matilde Lalín, you want to introduce yourself? Matilde Lalín: Hi. Okay. Thank you for having me here. So I'm originally from Argentina. I grew up in Buenos Aires, and I did my undergraduate there. And then I moved to the US to do my Ph.D., mostly at the University of Texas at Austin. And then I moved to Canada for postdocs, and I stayed in Canada. So right now, I'm a professor at the University of Montreal, and I work in number theory. EL: And I'm guessing you do not have a sunburn, being in Montreal in March. ML: So maybe I should say we are celebrating that we are very close to zero Celsius. KK: Oh, okay. EL: Yeah, so exciting times. ML: Yeah. So some of the snow actually is melting. KK: Oh, okay. I haven’t seen snow in quite a while. I kind of miss it sometimes. But anyway. EL: Oh, It is very pretty. KK: Yeah, it is. It’s lovely. Until you have to shovel it every week for six months. But yeah, so Matilde, what is your favorite theorem? ML: Okay, so I wanted to talk about a problem more than theorem. Well, it will lead to some theorems eventually, and a conjecture. So my favorite problem, let's say, is the congruent number problem. KK: Okay. ML: So okay, so basically, a positive integer number is called congruent if it is the area of a right triangle with rational sides. EL: All three sides, right? ML: Exactly, exactly. So the question will be, you know, how can you tell that a particular number is congruent? But more generally, can you give a list of all congruent numbers? So for example, six is congruent, because it is the area of the right triangle with sides three, four, and five. So that's easy, but then seven is congruent because it’s the area of the triangle with sides 24/5, 35/12, and 337/60. KK: Ah, okay. EL: So that’s not quite as obvious. ML: Not quite as obvious, exactly. And in fact, there is an example, due to Zagier: 157 is congruent, and so the size of the triangle, they are fractions that have—okay, so the hypotenuse has 46 and 47 digits, the numerator and denominator. And so it can be very big. Okay, let me clarify for a congruent number, there are actually infinitely many triangles that satisfy this. But the example I'm giving you is the smallest, in a sense. EL: Okay. ML: So actually it can be very complicated, a priori, to decide whether a number is congruent or not. KK: Sure. ML: So this problem appears for the first time in an Arab manuscript in the 10th century, and then it was— EL: Oh, wow, that's shocking! ML: Yes. Well, because triangles—I mean, it's a very natural question. But then it was picked up by Fibonacci, who was actually looking at this question from a different point of view. So he was studying arithmetic sequences. So he posed the question of whether you can have a three-term arithmetic sequence whose terms are all squares. So basically, let me give you an example. So 1-25-49. Okay? So those are three squares, and 25−1 is 24. And 49−25 is 24. So that makes it an arithmetic sequence. And each of the three members are squares. EL: Yeah. ML: And he said that the difference—so in this case it would be 24, okay? 25−1 is 24, 49−25 is 24—so the difference is called a congruum, if you can build a sequence with this difference, basically. So it turns out that this this problem is essentially equivalent to the congruent number problem, so that's where the name, the word congruent, comes from. Fibonacci was calling this congruum. So congruent has to do with things that sort of congregate. EL: Okay. ML: And so kind of this difference of the arithmetic sequence. And you can prove that from such a sequence you can build your triangle. So in the example I gave you, this is a sequence that shows that six is congruent. Well, technically it shows that 24 is congruent, but 24 is a square times six. And so if you have a triangle, you can always multiply the size by the constant, and that would be equivalent to multiplying the area by some square. KK: Sure, yeah, EL: Right. Right, and so if it has a square in it, then there's a rational relationship that will still be preserved. ML: Exactly. So Fibonacci actually managed to prove that seven is congruent. And then he posed as a question, as a conjecture, that one wasn't congruent. So when you say that one is not congruent, you are also saying that the squares are not congruent. The square of any rational number. EL: Oh. KK: Okay. ML: It’s actually kind of a nicer statement, in a sense. It's like a very special case. And then, like 400 years after, Fermat came, and so he actually managed to solve Fibonacci’s question. So he actually proved, using his famous descent, he proved that one is not congruent. And also that two and three are not congruent. So basically, he settled the question for those. And five is known to be congruent, also six and seven. So well, that takes care of the first few numbers. Because four is one in this case. KK: Four is one, that’s right. ML: Yeah, exactly. And well, one thing that happens with this problem is that actually, if you go in the direction that Fibonacci was looking, okay, so this sequence of three squares, actually, if you can think of them as—say you call the middle square x, and then one is x−n and the other is x+n. So when you multiply these three together, it gives you a square. And what this is telling you, is that actually giving you a solution to an equation that you could write as, say, y2=x(x−n)(x+n). And that's what is called an elliptic curve. EL: Oh, okay. ML: Yes. So basically, an elliptic curve in this context is more general. You could think of it as y2 equals a cubic polynomial in x. And so basically, the congruent numbers problem is asking whether, for such an equation, you have a solution such that y is different from 0. So so then you can study the problem from that point of view. There is a lot, there is a big theory about elliptic curves. EL: Right. And so I've been wondering, like, is this where people got the idea to bring elliptic curves into number theory? That's always seemed mysterious to me—like, when you first learn about Fermat’s Last Theorem, and you learn there's all this elliptic curve stuff involved in proving that, like, how do people think to bring elliptic curves in this way? ML: As a matter of fact, okay, so elliptic curves in general, it’s actually a very natural object to study. So I don't know if it came exactly via the congruent number problem, because essentially—okay, so essentially, a natural problem more generally is Diophantine equations. So basically, I give you a polynomial with integer coefficients, and I am asking you about solutions that are either integers or rational. And we understand very well what happens when the degree is one, say an equation of a form ax+by=c. Okay? So those we understand completely. We actually understand very well what happens when the degree is two and actually, degree three is elliptic curves. So it's a very natural progression. EL: Okay. ML: So it doesn't necessarily have to come with congruent numbers. However, it is true that many people chose choose to introduce elliptic curves via numbers, because it's such a natural question, such a natural problem. But of course, it leads you to a very specific family of elliptic curves. I mean, not just the whole story. So what is known about elliptic curves that can help understand this question of the congruent number problem: So in 1922 Mordell actually proved that the solutions of an elliptic curve—Actually, I should have said this before. So the solutions of an elliptic curve, say, over the rationals, so if you look at all the rational numbers that are solutions to an equation like that, y2 equals some cubic polynomial in x, they form a group. And actually an abelian group. And as I was saying, Mordell proved that this group actually is finally-generated. So you can actually give a finite list of elements in the group, and then every element in the group is a combination of those. Okay? So basically, it's very tempting to say, “Well, I mean, if you give me an elliptic curve, I want to find what the group is. So I just give the generators. So this should be very easy, okay? Yeah. [laughter] But actually, it's not easy. So there's no systematic way to find all the generators to determine what the group is. And even—so, you will always have, you may have, points of finite order. So, elements that if you, take some multiple, you get back to 0. So those are easy to find. But the question of whether they are elements of infinite order, and if there are, how many there are, or how many generators you need, all these questions are difficult in generals for an elliptic curve. And so, my favorite theorem actually—so the way I ended up coming up with the idea of talking about the congruent number problem, is actually Mordell’s theorem. So I really like Mordell’s theorem. KK: And that theorem’s not at all obvious. I mean, so you sort of, I'm not sure if I’ve ever even seen a proof. I mean, I remember, this is one of the first things I learned in algebraic geometry, you draw the picture, you know, of the elliptic curve. And the group law, of course, is given by: take two points, draw the line, and where it intersects the curve in the third point is the sum of those things, right—actually, then you reflect, it’s minus that, right? Yeah. Those three points add to zero. That's right. EL: We’ll put a picture of this up. Because Kevin's helpful air drawing is not obvious to our listeners. ML: That’s right. KK: Yeah. And from that, somehow, the idea that this is a finitely-generated group is really pretty remarkable. But the picture gives you no clue of where the where to find these generators, right? ML: Well, their first issue, actually, is to prove that this is an associative law. So that statement is annoyingly complicated to prove in elementary ways. KK: Yeah, commutativity is kind of obvious, right? ML: But, yes, already to prove that it's a group in the sense that associativity, yeah. And then Mordell’s theorem, actually, it follows, it does some descent. So it follows in the spirit of Fermat’s descent, actually. But I mean, in a more complicated context. But it's very beautiful, yeah. So as I was saying, the number of generators that have infinite order, that's called the rank, and already knowing whether the rank is zero, or what the value is, that's a very difficult question. And so in 1965, Birch and Swinnerton-Dyer came up with a conjecture that relates the rank to the order of vanishing of a certain function that you build from the elliptic curve. It’s called the L-function. So, in principle, with this conjecture, one can predict the value of the rank. That doesn't mean that we can find easily the generators, but at least we can answer, for example, whether there are infinitely many solutions or not and say that. EL: Yeah. ML: So basically, that's kind of the most exciting conjecture associated to this question. And I mean, it goes well beyond this question, and it's one of the Millennium Problems from the Clay. EL: Right. Yeah. So it’s a high dollar-value question. ML Yes. And it's interesting, because for this question, it is known that—if the L-function doesn’t vanish, then the rank is zero. So it's known for R[ank] zero, one direction, and the same for R[ank] 1. But not much more is known on average. So this very recent result, relatively recent result by Bhargava and Shankar, where they prove that the rank for, if you take all the elliptic, curves and order them in a certain way, the rank on average is bounded by 7/6. And so that means that there is a positive proportion of elliptic curves that actually satisfy BSD. Okay. But I mean, the question would be what BSD tells us about the original question that I posed. EL: Right, yeah, so when we were chatting earlier, you said that a lot of questions or theorems about congruent numbers were basically—the theorems were proved as partial solutions to BSD. Am I getting that right? ML: Yeah, okay. Some progress that is being done nowadays has to do with proving BSD for some particular families, I mean for these elliptic curves are attached to congruent numbers. But if I go back to the first connection, so there is this famous theorem by Tunnell that was published in 1983 where he basically ties the property of being a congruent number to two quadratic equations in three variables having one having double the solutions as the other somehow. So Tunnell’s result came, obviously, in ’83, so much earlier than most advances in BSD. EL: Okay. ML: And basically what Tunnell gives is like an algorithm to decide whether a number is congruent or not. And for the case where it’s non-congruent, actually it is conclusive, because this is a case, okay, so it depends on BSD, but this is a case where we know. And then the problem is the case where it will tell you that the number is congruent. So that is assuming BSD. So for now, like I said, many cases will just be the cases that—for example, there is some very recent result by Tian, where basically he proved that BSD applies to certain curves. And so for example, it is known that for primes congruent to five, six, or seven modulo eight, they are congruent. So this is a result that goes back to Heegner and Monsky in ’52, for Heegner. So that's for primes. So that's an infinite family of numbers that satisfy that they are congruent. But every question attached to this problem has to do with, okay, can you generalize this for all natural numbers that are congruent to six, five, or seven modulo eight. For example, that’s some direction of research going on now. EL: So you could disprove the BSD conjecture, if you could find some number that Turner’s [ed. note: Evelyn misremembered the name of the theorem; this should be “Tunnell’s”] theorem said was congruent, but was actually not congruent? ML: Yeah, yeah. So you could disprove—say you find a number that is congruent to six mod eight that is not a congruent number, you disprove BSD, yes. EL: All right. Yeah, so our listeners, I'm sure they'll go—I’m sure no one has ever searched a lot of numbers to check on this. So yeah, that's our assignment for you. So something we like to do on this show then is to ask our guest to pair their theorem with something. So what do you think enhances enjoyment of congruent numbers,…

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    Episode 42 - Moon Duchin May 09, 2019
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    Episode 42 - Moon Duchin Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a podcast about math and…I don't even know what it's going to be today. We'll find out. I'm one of your hosts, Kevin Knudson, professor of mathematics at the University of Florida. Here is your other host. Evelyn Lamb: Hi, I'm Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City, Utah. So yeah, how are you today? KK: I’ve had a busy week. Both of my PhD students defended on Monday. EL: Wow, congratulations. KK: Yeah, and so, through some weird quirk of my career, these are my first two PhD students. And it was a nice time, slightly nerve-wracking here and there. But everybody went through, everything's good. EL: Great. KK: So we have two new professors out there. Well, one guy's going to go into industry. But yeah, how about you? EL: I’m actually—once we're done with this, I need to go pack for a trip I'm leaving on today. I'm teaching a math writing workshop at Ohio State. KK: Right. I saw that, yeah. EL: I mean, if it goes well, then we'll leave this part in the thing. And if it doesn't, no one else will know. But yeah, I'm looking forward to it. KK: Good. Well, Ellen and I are going to Seattle this weekend. EL: Fun. KK: She got invited to be on a panel at the Bainbridge Island Art Museum. And I thought. “I'm going along” because I like Seattle. EL: Yeah, it's beautiful there. KK: Love it there. Anyway, enough about us. Today, we are pleased to welcome Moon Duchin to the show. Moon, why don’t you introduce yourself? Moon Duchin: Hi, I'm Moon. I am a mathematician at Tufts University, where I'm also affiliated with the College of Civic Life. It’s a cool thing Tufts has that not everybody has. And in math, my specialties are geometric group theory, topology (especially low-dimensional topology), and dynamics. KK Very cool. EL: Yeah, and so how does this civil life thing work? MD: Civic life, yeah. EL: Sorry. MD: So that's sort of because in the last couple years, I've become really interested in politics and in applications—I think of it as applications of math to civil rights. So that's that's sort of mathematics engaging with civics, it’s kind of how we do government. So that's become a pretty strong locus of my energy in the last couple years. EL: Yeah. KK: And I'll vouch for Moon's work here. I mean, I've gone to a couple of the workshops that she's put together. Big one at Tufts in 2017, I guess it was, and then last December, this meeting at Radcliffe. Really cool stuff. Really important work. And I've gotten interested in it too. And let's hope we can begin to turn some tides here. But anyway, enough about that. So, Moon, what's your favorite theorem? MD: Alright, so I want to tell you about what I think is a really beautiful theorem that is known to some as Gromov’s gap. KK: Okay. EL: Which also sounds like it could be the name of a mountain pass in the Urals or something. MD: I was thinking it sounds like it could be, you know, in there with the Mines of Moria in Middle Earth. EL: Oh, yeah, that too. MD: Just make sure you toss the dwarf across the gap. Right, it does sound like that. But of course, it's Mischa Gromov, who is the very prolific Russian-French mathematician who works in all kinds of geometry, differential geometry, groups, and so on. So what the theorem is about is, what kinds of shapes can you see in groups? So let me set that up a little with—you know, let me set the stage, and then I'll tell you the result. EL: Okay. MD: So here's the setting. Suppose you want to understand—the central objects in geometric group theory are, wel,l groups. So what are groups? Of course, those are sets where you can do an operation. So you can think of that as addition, or multiplication, it's just some sort of composition that tells you how to put elements together to get another element. And geometric group theory is the idea that you can get a handle on the way groups work—they’re algebraic objects, but you can study them in terms of shape, geometrically. So there are two basic ways to do that. Either you can look at those spaces that they act on, in other words, spaces where that group tells you how to move around. Or you can look at the group itself as a network, and then try to understand the shape of that network. So let's stick with that second point of view for a moment. So that says, you know, the group has lots of elements and instructions for how to put things together to move around. So I like to think of the network—a really good way to wrap your mind around that is to think about chess pieces. So if I have a chessboard, and I pick a piece—maybe I pick the queen, maybe I pick the knight—there are instructions for how it can move. And then imagine the network where you connect two squares if your piece can get between them in one step. Right? KK: Okay. MD: So, of course that's going to make a different network for a knight than it would for a queen and so on, right? EL: Yeah. MD: Okay. So that's how to visualize a group, especially an infinite—that works particularly well for infinite groups. That's how to visualize a group as a bunch of points and a bunch of edges. So it's some big graph or network. And then GGT, geometric group theory, says, “What's the shape of that network?” Especially when you view it from a distance, does it look flat? Does it look negatively curved, like a saddle surface? Or does it kind of curve around on itself like a sphere? You know, what's the shape of the group? And actually, just a cool observation from, you know, a hundred plus years ago, an observation of Felix Klein is that actually the two points of view—the spaces that the group acts on or the group itself—those really are telling the same story. So the shape of the space is about the same as the shape of the group. That's become codified as kind of a fundamental observation in GGT. Okay, great. So that's the space I want to think about. What is the shape—what are the possible shapes of groups? Okay, and that's where Gromov kicks in. So the theorem is about the relationship of area to perimeter. And here's what I mean by that. Form a loop in your space, in your network. And here, a loop just means you start at a point, you wander around with a path, and you end up back where you started. Okay? And then look at the efficient ways to fill that in with area. So visualize that, like, first you have an outline, and then you try to fill it in with maybe some sort of potato chip-y surface that kind of interpolates around that boundary. Okay, so the question is, if you look at shapes that have the extremal relationship of area to perimeter, then what is the relationship of area to parameter? So let's do that in Euclidean space first, because it's really familiar. So we know that the extremal shapes there are circles, and you fill those in with discs. And the relationship is that area looks about like perimeter squared, right? KK: Right. MD: Okay, great. So now here's the theorem, then. Get ready for it. I love this theorem. In groups, you can find groups where area looks like perimeter to the K power. It can look like perimeter to the 1, it can look like perimeter to the 1, or 3, or 4, and so on. You can build designer groups with any of those exponents. But furthermore, you can also get rational exponents. You can get pretty much any rational exponent you want. You can get 113/5, you can get, you know, 33/10. Pick your favorite exponent, and you can do it. EL: Can you get less than one? MD: Well, let's come back to that. EL: Okay. Sorry. MD: So let me state Gromov’s theorem in this level of generality. So here's the theorem. You can get pretty much any exponent that you want, as long as it's not between 1 and 2. KK: Wow. EL: Oh. MD: Isn’t that cool? That's Gromov’s gap. KK: Okay. EL: Okay. MD: So there's this wasteland between 1 and 2 that's unachievable. KK: Wow. MD: Yeah. And then you can, see—past 2, you can see anything. Um, it actually turns out, it's not just rationals. You can see lots of other kinds of algebraic numbers too. KK: Sure. MD: And the closure up there is everything from two to infinity! But nothing between one and two. It's a gap. EL: Oh, wow. That's so cool! MD: That’s neat, right? Evelyn, to answer your question under one turns out not to really be well defined for reasons we could talk about. But yeah. KK: This is remarkable. This sounds like something Gromov would prove, right? I mean, just these weird theorems out of nowhere. I mean—how could that be true? And then there it is. Yeah. MD: Or that Gromov would state and leave other people to prove. KK: That—yeah, that's really more accurate. Yeah. So. Okay, so you can't get area to the—I mean, perimeter to the 3/2. I mean, that's, that's really…Okay. Is there any intuition for why you can't get things between one and two? MD: Yeah, there kind of is, and it's beautiful. It is that the stuff that sits at the exponent 1, in other words, where area is proportional, the perimeter is just really qualitatively different from everything else. Hence the gulf. And what is that stuff? That is hyperbolic groups. So this comes back to Evelyn's wheelhouse, I believe. EL: It’s been a while since I thought in a research way about this, but yes, vaguely at the distance of my memory. MD: Let me refresh your memory. Yes, so negatively curved things, things that are saddled shaped, those are the ones where area is proportional to parameter. And everything else is just in a different regime. And that's really what this theorem is telling you. So that's one beautiful point of view, and kind of intuition, that there's this qualitative difference happening there. But there's something—there’s so many things I love about this theorem. It's just the gateway to lots of beautiful math. But one of the things I love about this theorem is that it fails in higher dimensions, which is really neat. So if you, instead of filling a loop with area, if you were to fill a shell with volume, there would be no gap. EL: Oh. MD: Cool, right? EL: So this is, like, the right way to measure it if you want to find this difference in how these groups behave. MDL Absolutely. And, you know, another way to say it, is this is an alternative definition of hyperbolic group from the usual one. It's like, the right way to pick out these special groups from everything else is specifically to look at filling loops. KK: Right. And I might be wrong here, but aren't most groups hyperbolic? Is that? MD: Yeah, so that's definitely the kind of religious philosophy that’s espoused. But you know, to talk about most groups usually the way people do that is they talk about random constructions of groups. And a lot of that is pretty sensitive to the way you set up what random means. But yeah, that's definitely the, kind of, slogan that you hear a lot in geometric group theory, is that hyperbolic groups are special, but they're also generic. EL: Yeah. KK: So are there explicit constructions of groups with say, exponent 33/10, to pick an example? MD: Yes, there are. Yeah. And actually, if you're going to end up writing this up, I can send you some links to beautiful papers. EL: Yeah, yeah. But there’s, like, a recipe, kind of, where you're like, “Oh, I like this exponent. I can cook up this group.” MD: Yeah. And that's why I kind of call them designer groups. EL: Right, right. Yeah. Your bespoke groups here. MD: Yeah, there are constructions that do these things. KK: That’s remarkable. So I was going to guess that your favorite term was the isoperimetric inequality. But I guess this kind of is, right? MD: I mean, exactly. Right? So the isometric inequality is all about asking, what is the extremal relationship of area to parimeter? And so this is exactly that, but it's in the setting of groups. KK: Yeah, yeah. EL: So how did you first come across this theorem? MD: Well, I guess, in—when you're in the areas of geometric topology, geometric group theory, there's this one book that we sometimes call the Bible—here I'm leaning on this religious metaphor again—which is this this great book by Bridson and Haefliger called Metric Spaces of Non-Positive Curvature. And it really does feel like a Bible. It's this fat volume, you always want it around, you flip to the stuff you need, you don't really read it cover to cover. KK: Just like the Bible. Yeah, MD: Exactly. Great. And that's certainly where I first saw it proved. But, yeah, I mean, the ideas that circulate around this theorem are really the fundamental ideas in GGT. KK: Okay, great. Does this come up in your own work a lot? Do you use this for things you do? Or is this just like, something that you love, you know, for its own sake? MD: Yeah, no, it does come up in my own work in a couple of ways. But one is I got interested in the relationship between curvature—curvature in the various senses that come from classical geometry—I got interested in the relationship between that and other notions of shape in networks. So this theorem takes you right there. And so for instance, I have a paper of a theorem with Lelièvre, and Mooney, where we look at something really similar, which is, we call it sprawl. It's how spread out do you get when you start at a point and you look at all the different positions you can get to within a certain distance. So you look at a kind of ball around the point. And then you ask how far apart are the points in that ball from each other? So that's actually a pretty fun question. And it turns out, here's another one of these theorems where hyperbolic stuff, there's just a gap between that and everything else. KK: Right. MD: So let’s follow that through for a minute. So suppose you start at a base point, and you take the ball of radius R around that base point. And then you ask, “How far apart are the points in that ball from each other?” Well, of course, by the triangle inequality, the farthest apart that could possibly be is 2R because can connect them through the center to each other, right, 2R. Okay, so then you could ask, “Hm, I wonder if there's a space that’s so sprawling, so spread out, so much like, you know, Houston, right, so sprawling that the average distance is actually the maximum?” EL: Yeah. MD: Right. What if the average distance between two points is actually equal to 2R? And that’s, so that's something that we proved. We proved that when you're negatively curved, and you have, you know, a few other mild conditions, basically—but certainly true for negatively curved groups, just like the setting of Gromov’s theorem—so for negatively curved groups, the average is the maximum. You’re as sprawling as you can be. Yeah, isn't that neat? So that's very much in the vein of this kind of result. KK: Oh, that's very cool. All right. EL: Yeah. Kind of like the SNCF metric, also, where you have to go to Paris to go anywhere else. Slightly different, but still, that you basically you have to go in to the center to get to the other side. MD: It’s exactly the same collection of ideas. And I'm just back from Europe, where I can attest that it's really true. You want to get from point to point on the periphery of France, you’d better be going through Paris if you want to do it fast. But yeah, it’s precisely the same idea, right? So the average distance between points on the periphery of France will be: get to Paris and get to the other point. So there's a max there that's also realized. KK: All right, so France…

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    Episode 41 - Suresh Venkatasubramanian Apr 25, 2019
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    Episode 41 - Suresh Venkatasubramanian Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Welcome to My Favorite Theorem, a podcast about theorems where we ask mathematicians to tell us about theorems. I'm one of your hosts, Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City, Utah. This is your other host. Kevin Knudson: Hi, I’m Kevin Knudson. I’m a professor of mathematics at the University of Florida. It seems like I just saw you yesterday. EL: Yeah, but I looked a little different yesterday. KK: You did! EL: In between when I talked with you and this morning, I dyed my hair a new color, so I'm trying out bright Crayola crayon yellow right now. KK: It looks good. Looks good. EL: Yeah, it's been fun in this, I don't know, like 18 hours I've had it so far. KK: Well, you know what, it's what it's sort of dreary winter, right, you feel the need to do something to snap you out of it, although it's sunny in Salt Lake, right, it’s just cold? No big deal. EL: Yeah, we’ve had some sun. We’ve had a lot of snow recently, as our guest knows, because our guest today also lives in Salt Lake City. So I'm very happy to introduce Suresh Venkatasubramanian. So Hi. Can you tell us a little bit about yourself? Suresh Venkatasubramanian: Hey, thanks for having me. First of all, I should say if it weren't for your podcast, my dishes would never get done. I put the podcast on, I start doing the dishes and life is good. EL: Glad to be of service. SV: So I'm in the computer science department here. I have many names. It depends on who's asking and when. Sometimes I'm a computational geometer, I'm a data miner. Sometimes occasionally a machine learner, though people raise their eyebrows at that. But the name I like the most right now is bias detective, or computational philosopher. These are the two names I like the most right now. EL: Yeah, yeah. Because you've been doing a lot of work on algorithmic bias. Do you want to talk about that a little bit? SV: Sure. So one of the things that we're dealing with now as machine learning and associated tools go out into the world is that they're being used for not just, you know, predicting what podcast you should listen to, or what music you should listen to, but they're also being used to decide whether you get a job somewhere, whether you get admission to college, whether you get surveilled by the police, what kind of sentences you might get, whether you get a loan. All these things are where machine learning is now being used just because we have lots of data to collect and seemingly make better decisions. But along with that comes a lot of challenges, because what we're finding is that a lot of the human bias in the way make decisions is being transferred to machine bias. And that causes all kinds of problems, both because of the speed at which these decisions get made and the relative obscurity of automated decision making. So trying to piece together, piece out what's going on, how this changes the way we think about the world, how we—the way we think about knowledge about society, has been taking up most of my time of late. EL: Yeah, and you've got some interesting papers that you've worked on, right, on how people who design algorithms can help combat some of these biases that can creep in. SV: Yeah, so there are many, many levels of questions, right? One basic question is how do you even detect whether there’s—so first of all, I mean, I think as a mathematical question, how do you even define what it means for something to be biased? What does that word even mean? These are all loaded terms. And, you know, once you come up with a bunch of different definitions for maybe what is a relatively similar concept, how do they interact? How do you build systems that can sort of avoid these kinds of bias the way you've defined it? And what are the consequences of building systems? What kind of feedback loops do you have in systems that you use? There’s a whole host of questions from the mathematical to the social to the philosophical. So it's very exciting. But it also means everyday, I feel even more dumb than I started the day with so. EL: Yeah. KK: So I think the real challenge here is that, you know, people who aren't particularly mathematically inclined just assume that because a computer spit out an answer, it must be valid, it must be correct. And that, in some sense, you know, it's cold, that the machine made this decision, and therefore, it must be right. How do you think we can overcome that idea that, you know, actually, bias can be built into algorithms? SV: So this is the “math is not racist” argument, basically. KK: Right. SV: That comes up time and time again. And yeah, one thing that I think is an encouraging is that we've moved relatively quickly, in a span of, say, three to four years, from “math is not racist” to “Well, duh, of course, algorithms are biased.” EL: Yeah. SV: So I guess that's a good thing. But I think the problem is that there’s a lot of commercial and other incentives bound up with the idea that automated systems are more objective. And like most things, there's a kernel of truth to it in the sense that you can avoid certain kinds of obvious biases by automating decision making. But then the problem is you can introduce others, and you can also amplify them. So it's tricky, I think. You're right, it's getting away from the notion where commercially, there's more incentive to argue that way. But also saying, “Look, it's not all bad, you just need more nuance.” You know, arguments for more nuance tend not to go as well as, you know, “Here's exactly how things work,” so it's hard. KK: Everything’s black or white, we know this, right? SV: With a 50% probability everything is either true or not, right. EL: So we invited you on to hear about your favorite theorem. And what is that? SV: So it's technically an inequality. But I know that in the past, you've allowed this sort of deviation from the rules, so I'm going to propose it anyway. EL: Yes, we are very flexible. SV: Okay, good good good. So the inequality is called Fano’s inequality after Robert Fano, and it comes from information theory. And it’s one of those things where, you know, the more I talk about it, the more excited I get about it. I'm not even close to being an expert on the ins and outs of this inequality. But I just love it so much. So I need to tell you about that. So like all good stories, right, this starts with pigeons. EL: Of course. SV: Everything starts with pigeons. Yes. So, you may have heard of the pigeonhole principle. KK: Sure. SV: Ok. So the pigeonhole principle, for those in the audience who may not, basically, if you have ten pigeons and you have nine pigeonholes, someone's going to get a roommate, right? It’s one of the most obvious statements one can make, but also one of the more powerful ones, because if you unpack a little bit, it's not telling you where to find that pigeonhole with two pigeons, it's merely saying that you are guaranteed that this thing must exist, right, it's a sort of an existence proof that can be stated very simply. But the pigeonhole principle, which is used in many, many parts of computer science to prove that, you know, some things are impossible or not can be restated, can be used to prove another thing. So we all know that if, okay, I need to store a set of numbers, and if the numbers range from 1 to n, that I need something like log(n) bits to store it. Well, why do I need log(n) bits? One way to think about this is this is the pigeonhole principle in action because you're saying, I have n things. If I have log(n) bits to describe a hole, like an address, then there are 2log(n) different holes, which is n. And if I don't have log(n) bits, then I don't have n holes, and therefore by the pigeonhole principle, two of my things will get the same name. And that's not a good thing. I want to be able to name everything differently. So immediately, you get the simple statement that if you want to store n things you need log(n) bits, and of course, you know, n could be whatever you want it to be, which means that now—in theoretical computer science, you want to prove lower bounds, you want to say that something is not possible, or some algorithm must take a certain amount of time, or you must need to store so many bits of information to do something. These are typically very hard things to prove because you have to reason about any possible imaginary way of storing something or doing something, and that's very hard. But with things like the pigeonhole principle and the log(n) bit idea, you can do surprisingly many things by saying, “Look, I have to store this many things, I'm going to need at least log of that man bits no matter what you do.” And that's great. KK: So that's the inequality. EL: No, not yet. KK: Okay, all right. SV: I was stopping because I thought Evelyn was going to say something. I'm building up. It's like, you know, a suspense story here. KK: Okay, good. EL: Yes. KK: Chapter two. SV: So if you now unpack this log(n) bit thing, what it's really saying is that I can encode elements and numbers in such a way, using a certain number of bits, so that I can decode them perfectly because there aren't two pigeons living in the same pigeonhole. There's no ambiguity. Once I have a pigeonhole, who lives there is very clear, right? It's a perfect decoding. So now you've gone from just talking about storage to talking about an encoding process and a decoding process. And this is where Fano’s inequality comes into play. So information theory—so going back to Shannon, right—is this whole idea of how you transmit information, how you transmit information efficiently, right, so the typical object of study in information theory is a channel. So you have some input, some sort of bits going into a channel, something happens to it in the channel, maybe some noise, maybe something else, and then something comes out. And one of the things you'd like to do is looking at, so x comes in, y comes out, and given y you'd like to decode and get back to x, right? And it turns out that, you know, you can talk about the ideas of mutual information entropy, they start coming up very naturally, where you start saying if x is stochastic, it's a random variable, and y is random, then the channel capacity, in some sense the amount of information the channel can send through, relates to what is called the mutual information between x and y, which is this quantity that captures, roughly speaking, if I know something about x, what can I say about y and vice versa. This is not quite accurate, but this is more or less what it says. So information theory at a broader level—and this is where really Fano’s inequality connects to so many things—it’s really about how to quantify the information content that one thing has about another thing, right, through a channel that might do something mysterious to your variable as it goes through. So now what does Fano’s inequality say? So Fano’s inequality you can think of now in the context of bits and decoding and encoding, says something like this. If you have a channel, you take x, you push it into the channel and out comes y, and now you try to reconstruct what x was, right? And let's say there's some error in the process. The error in the process of reconstructing x from y relates to the term that is a function of the mutual information between x and y. More precisely, if you look at how much, essentially, entropy you have left in x once I tell you y— So for example, let me see if this is a good example for this. So I give you someone's name, let's say it's an American Caucasian name. With a certain degree of probability, you'll be able to predict what their gender is. You won't always get it right, but there will be some probability of this. So you can think of this as saying, okay, there's a certain error in predicting that person's name from the—for predicting the person's gender from the name as you went through the channel. The reason why you have an error is because there's certain one of noise. Some names are sort of gender-ambiguous, and it's not obvious how to tell. And so there's a certain amount of entropy left in the system, even after I've told you the name of the person. There's still an amount of uncertainty. And so your error in predicting that person's gender from the name is related to the amount of entropy left in the system. And this is sort of intuitively reasonable. But what it's doing is connecting two things that you wouldn’t normally expect to be connected. It's connecting a computational metaphor, this process of decoding, right, and the error in decoding, along with a basic information theoretic statement about the relationship between two variables. And because of that—Fano’s inequality, or even the basic log(n) bits needed to sort n things idea—for me, it's pretty much all of computer science. Because if we want to prove a lower bound on how we compete something, at the very least we can say, look, I need at least this much information to do this computation. I might need other stuff, but I need at least as much information. That clearly will be a lower bound. Can I prove a lower bound? And that has been a surprisingly successful endeavor, in reasoning about the lower bounds for computations, where it would be otherwise very hard to think about, okay, what does this computation do? or what does that computation do? Have I imagined every possible algorithm I can design? I don't care about any of that because Fano’s inequality says it doesn't matter. Just analyze the amount of information content you have in your system. That's going to give you a lower bond on the amount of error you’re going to see. EL: Okay, so I was reading—you wrote a lovely posts about this, and I was reading that this morning, before we started talking. And I think this is what you just said, but it's one of these things that is very new to me, I'm not used to thinking like a computer scientists or information theorist or anything. So something that was a little bit, I was having trouble understanding, is whether this inequality, how much it depends on the particular relationship you have between the two, x and y that you're looking at. SV: So one way to answer this question is to say that all you need to know is the conditional entropy of the x given y. That's it. You don't need to know anything else about how y was produced from x. All that you need to know, to put a bound on the decoding error, is the amount of entropy that's left in the system. KK: Is that effectively computable? I mean, it is that easy to compute? SV: For the cases where you apply Fano’s inequality, yes. Typically it is. In fact, you will often construct examples where you can show what the conditional entropy is, and therefore be able to reason, directly use Fano’s inequality, to argue for the probability of error. So let me give an example in computer science of how this works. KK: Okay, great. SV: Suppose I want to build a—so one of the things we have to do sometimes is build a data structure for a problem, which means you're trying to solve some problem, you want to store information in a convenient way so you can access the information quickly. So you want to build some lookup table or a dictionary so that when someone comes up with the question—“Hey, is this person's name in the dictionary?”—you can quickly give an answer to the question. Ok. So now I want to say, lo…

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    Episode 40 - Ursula Whitcher Apr 11, 2019
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    Episode 40 - Ursula Whitcher Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello, and welcome to my favorite theorem, a math podcasts where we ask mathematicians to tell us about their favorite theorems. I'm Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City, Utah. And I am joined as usual by my co host Kevin. Can you introduce yourself? Kevin Knudson: Sure. I'm still Kevin Knudson, professor of mathematics at the University of Florida. How are things going? EL: All right. Yeah. KK: Well, we just talked yesterday, so I doubt much has changed, right? Except I seem to have injured myself between yesterday today. I think it's a function of being—not 50, but not able to say that for much longer. EL: Yeah, it happens. KK: It does. EL: Yeah. Well, hopefully, your podcasting muscles have not been injured. KK: I just need a few fingers for that. EL: Alright, so we are very happy today to welcome Ursula Whitcher to the show. Hi, can you tell us a little bit about yourself? Ursula Whitcher: Hi, my name is Ursula. I am an associate editor at Mathematical Reviews, which, if you've ever used MathSciNet to look up a paper or check your Erdős number or any of those exciting things, there are actually 16 associate editors like me checking all the math that gets posted on MathSciNet and trying to make sure that it makes sense. I got my PhD at the University of Washington in algebraic geometry. I did a postdoc in California and spent a while as a professor at the University of Wisconsin Eau Claire, and then moved here to Ann Arbor where it's a little bit warmer, to start a job here. EL: Ann Arbor being warmer is just kind of a scary proposition. UW: It’s barely even snowing. It's kind of weird. EL: Yeah. Well, and yeah, you mentioned Mathematical Reviews. I—before you got this job, I was not aware that, you know, there were, like, full time employees just of Mathematical Reviews, so that's kind of an interesting thing. UW: Yeah, it's a really cool operation. We actually go back to sometime in probably the ‘40s. KK: I think that’s right, yeah. EL: Oh wow. UW: So it used to be a paper operation where you could sign up and subscribe to the journal. And at some point, we moved entirely online. KK: I’m old enough to remember in grad school, when you could get the year’s Math Reviews on CD ROM before MathSciNet was a thing. And you know, I remember pulling the old Math Reviews, physical copies, off the shelf to look up reviews. UW: We actually have in the basement this set of file cards that our founder, who came from Germany around the Second World War, he had a collection of handwritten cards of all the potential reviewers and their possible interests. And we've still got that floating around. So there's a cool archival project. KK: I’m ashamed to admit that I'm a lapsed reviewer. I used to review, and then I kind of got busy doing other things and the editors finally wised up and stopped sending me papers. UW: I try to tell people to just be really picky and only accept the papers that you're really excited to read. KK: I feel really terrible about this. So maybe I should come back. I owe an an apology to you and the other editors. UW: Yeah, come back. And then just be really super, super picky and only take things that you are truly overjoyed to read. We don't mind. I—you know, I read apologies for my job for part of my day every day for not reviewing. So I’ve become sort of a connoisseur of the apology letter. KK: Sure. So is part of your position also that you have some sort of visiting scholar deal at the University of Michigan? Does that come with this? UW: Yeah, that is. So I get to hang out at the University of Michigan and go to math seminars and learn about all kinds of cool math and use the library card. I'm a really heavy user of my University of Michigan library card. So yeah. EL: Those are excellent. KK: It’s a great campus. That’s a great department, a lot of excellent people there. EL: Yeah. So, what is your favorite theorem, or the favorite theorem you would like to talk about today? UW: So I decided that I would talk about mirror theorems as a genre. EL: Okay. UW: I don't know that I have a single favorite mirror theorem, although I might have a favorite mirror theorem of the past year or two. But as this kind of class of theorems, these are a weird thing, because they run kind of backwards. Like, typically there's this thing that happens where mathematicians are just hanging out and doing math because math is cool. And then at some point, somebody comes along and is like, “Oh, I see it practical use for this. And maybe I can spin it off into biology or physics or engineering or what have you.” Mirror theorems came the other way. They started with physical observation that there were two ways of phrasing of a theoretical physics idea about possible extra dimensions and string theory and gravity and all kinds of cool things. And then that physical duality, people chewed on and figured out how to turn it into precise mathematical statements. So there are lots of different precise mathematical statements encapsulating maybe something different about about the way these physical theories were phrased, or maybe building then, sort of chaining off of the mathematics and saying something that no longer directly relates to something you could state about a possible physical world. But there is still in like a neat mathematical relationship you wouldn't have figured out without having the underlying physical intuition. EL: Yeah. And so this is, the general area is called mirror symmetry. And when I first heard that phrase, I assumed it was something about like group theory that was looking at, like, you know, more tangible, things that I would consider symmetric, like what it looks like when you look in a mirror. But that's not what it is, I learned. UW: So I can tell you why it's called mirror symmetry, although it's kind of a silly reason. The first formulations of mirror symmetry, people were looking at these spaces called Calabi-Yau three-folds, which are—so there are three complex dimensions, six real dimensions, they could maybe be the extra dimensions of the universe, if you're doing string theory. And associated with a Calabi-Yau three-fold, you have a bunch of numbers that tell you about it’s topological information, sort of general stuff about what is this six dimensional shape looking like. And you can arrange those numbers in a diamond that's called the Hodge diamond. And then you can draw a little diagonal line through the Hodge diamond. And some of the mirror theorems predict that if I hand you one Calabi-Yau three-fold with a certain Hodge diamond, there should be somewhere out there in the universe another Calabi-Yau three-fold with another Hodge diamond. And if you flip across this diagonal axis, one is the Hodge diamonds should turn into the other Hodge diamond. EL: Okay. UW: So there is a mirror relationship there. And there is a really simple reflection there. But it's like you have to do a whole bunch of topology, and you have to do a whole bunch of geometry and you, like, convince yourself that Hodge diamonds are a thing. And then you have to somehow—like, once you've convinced yourself Hodge diamonds are a thing, you also have to convince yourself that you can go out there and find another space that has the right numbers in the diamond. EL: So the mirror is, like, the very simplest thing about this. It’s this whole elaborate journey to get to the mirror. UW: Yeah. EL: Okay, interesting. I didn't actually know that that was where the mirror came from. So yeah. So can you tell us what these mirror theorems are here? UW: Sure. So one version of it might be what I said, that given a Calabi-Yau manifold, with this information, that it has a mirror. Or so then this diamond of information is telling you something about the way that the space changes. And there are different types of information that you could look at. You could look at how it changes algebraically, like if you wrote down an equation with some polynomials, and you changed those coefficients on the polynomials just a little bit, sort of how many different sorts of things, how many possible deformations of that sort could you have? That's one thing that you can measure using, like, one number in this diamond. EL: Okay. UW: And then you can also try to measure symplectic structure, which is a related more sort of physics-y information that happens over in a different part of the diamond. And so another type of mirror theorem, maybe a more precise type of mirror theorem, would say, okay, so these deformations measured by this Hodge number on this manifold are isomorphic in some sense to these other sorts of deformations measured by this other Hodge number on this other mirror manifold. KK: Is there some trick for constructing these mirror manifolds if they exist? UW: Yeah, there are. There are sort of recipes. And one of the games that people play with mirror symmetry is trying to figure out where the different recipes overlap, when you’ve, like, really found a new mirror construction, and when you’ve found just another way of looking at an old mirror construction. If I hand you one manifold, does it only have a unique mirror or does it have multiple mirrors? KK: So my advisor tried to teach me Hodge theory once. And I can't even remember exactly what goes on, except there's some sort of bi-grading in the cohomology right? UW: Right. KK: And is that where this diamond shows up? UW: Yeah, exactly. So you think back to when you first learned complex analysis, and there was, like, d/dz direction and there was the d/dz̅ direction. KK: Right. UW: And we're working in a setting where we can break up the cohomology really nicely and say, okay, these are the parts of my cohomology that come from a certain number of homomorphic d/dz kind of things. And these are the other pieces of cohomology that can be decomposed and look like, dz̅. And since it’s a Kähler manifold, everything fits together in a nice way. KK: Right. Okay, there. That's all I needed to know, I think. That's it, you summarized it, you're done. EL: So, I have a question. When you talk about like mirror theorems, I feel like some amount of mirror symmetry stuff is still conjectural—or “I feel like”—my brief perusal of Wikipedia on this indicates that there are some conjectures involved. And so how much of these theorems are that in different settings, these mirror relationships hold, and how much of them are small steps in this one big conjectural picture. Does that question make sense? UW: Yeah. So I feel like we know a ton of stuff about Calabi-Yau three-folds that are realized in sort of the nice, natural ways that physicists first started writing down things about Calabi-Yau three-folds. When you start getting more general on the mathematical side—for instance, there's a whole flavor of mirror symmetry that's called homological mirror symmetry that talks about derived categories and the Fuakaya category—a lot of that stuff has been very conjectural. And it's at the point where people are starting to write down specific theorems about specific classes of examples. And so that's maybe one of the most exciting parts of mirror symmetry right now. And then there are also generalizations to broader classes of spaces, where it's not just Calabi-Yau three-folds where maybe you're allowing a more general kind of variety or relaxing things, or you're starting to look at, what if we went back to the physics language about potentials, instead of talking about actual geometric spaces? Those start having more conjectural flavor. EL: Okay, so a lot of this is in the original thing, but then there are different settings where mirror symmetry might be taking place? UW: Yeah. EL: Okay. And I assume if you're such a connoisseur of mirror theorems, that this is related to your research also. What kinds of questions are you looking at in mirror symmetry? UW: Yeah, so I spend some time just playing around with different mirror constructions and seeing if I can match them up, which is always a fun game, trying to see what you know. Lately, what I've been really excited about is taking the sort of classical old-fashioned hands-on mirror constructions where I can hand you a space, and I can take another space, and I can say these two things are mirror manifolds. And then seeing what knowing that tells me, maybe about number theory, about maybe doing something over a finite field in a setting that is less obviously geometry, but where maybe you can still exploit this idea that you have all of this extra structure that you know about because of the mirror and start trying to prove theorems that way. EL: Oh, wow. I did not know there is this connection in number theory. This is like a whole new tunnel coming out here. UW: Yeah, no, it's super awesome. We were able to make predictions about zeta functions of K3 surfaces. And in fact we have a theorem about a factor of as zeta function for Calabi-Yau manifolds of any dimensions. And it's a very specific kind of Calabi-Yau manifold, but it's so hard to prove anything about zeta functions! In part because if you're a connoisseur of zeta functions, you know they are controlled by the size of the cohomology, so once your cohomology starts getting really big, it’s really difficult to compute anything directly. EL: So, like, how tangible are these? Like, here is a manifold and here is its mirror? Are there some manifolds you can really write down and, like, have a visual picture in your mind of what these things actually look like? UW: Yeah, definitely. So I'm going to tell you about two mirror constructions. I think one of these is maybe more friendly to someone who likes geometry. And one of these is more friendly to someone who likes linear algebra. EL: Okay. UW: So the oldest, oldest mirror symmetry construction was, it's due to Greene and Plesser who were physicists. And they knew that they were looking for things with certain symmetries. So they took the diagonal quintic in projective four-space. I have to get to my dimensions right, because I actually often think about four dimensions instead of six. So you're taking x5+y5+z5 plus, then, v5 and w5, because we ran out of letters, we had to loop around. EL: Go back. UW: Yeah. And you say, Okay, well, these are complex numbers, I could multiply any of them by a fifth route of unity, and I would have preserved my total space, right? Except we're working in projective space, so I have to throw away one of my overall fifth roots of unity because if I multiply by the same fifth root of unity on every coordinate, that doesn't do anything. And then they wanted to maybe fit this into a family where they deformed by the product of all the variables. And if you want to have symmetries of that entire family, you should also make sure that the product of all of your roots of unity, I think multiplies to 1? So anyway, you throw out a couple of fifth roots of unity, because you have these other symmetries from your ambient space and things that you're interested in, and you end up with basically three fifths roots of unity that you can multiply by. So I've got x5+y5+z5+v5+w5, and I'm modding out by z/5z3. Right? So I’m identifying all of these points in this space, right? I've just like got, like, 125 different things, and I’m shoving all these 125 different things together. So when I do that, this space—which was all nice and smooth and friendly, and it's named after Fermat, because Fermat was interested in equations like that—all of a sudden, I'd made it like, really stuck together and messy, and sing…

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    Episode 39 - Fawn Nguyen Mar 28, 2019
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    Episode 39 - Fawn Nguyen Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello and welcome to My Favorite Theorem, a math podcast where we get mathematicians to tell us about theorems. I'm one of your hosts, Evelyn Lamb. I'm a freelance math and science writer in Salt Lake City, Utah. And this is your other host. Kevin Knudson: Hi, I’m Kevin Knudson, professor of mathematics at the University of Florida. It's Friday night. EL: Yeah, yeah, I kind of did things in a weird order today. So there's this concert at the Utah Symphony that I wanted to go to, but I can't go tonight or tomorrow night, which are the only two nights they're doing it. But they had an open rehearsal today. So I went to a symphony concert this morning. And now I'm doing work tonight, so it's kind of a backwards day. KK: Yeah. Well, I got up super early to meet with my financial advisor. EL: Oh, aren’t you an adult? KK: I do want to retire someday. I’ve got 20 years yet, but you know, now it's nighttime and my wife is watching Drag Race and I'm talking about math. So. EL: Cool. Yeah. KK: Life is good. EL: Yes. Well, we're very happy today to have Fawn Nguyen on the show. Hi, Fawn. Could you tell us a little bit about yourself? Fawn Nguyen: Hi, Evelyn. Hi, Kevin. I was was thinking, “How nerdy can we be? It’s Friday.” So my name is Fawn Nguyen. I teach at Mesa Union School in Somis, California. And it's about 60 miles north of LA. EL: Okay. FN: 30 miles south of Santa Barbara. I teach—the school I'm at is a K-8, one-school district, of about 600 students. Of those 600, about 190 of them are in the junior high, 6-8, but it's a unique one-school district. So we're a family. It's nice. It's my 16th year there but 27th overall. EL: Okay, and where did you teach before then? FN: I was in Oregon. And I was actually a science teacher. KK: Is your current place on the coast or a little more inland? FN: Coast, yeah, about 10 miles from the coast. I think we have perfect weather, the best weather in the world. So. KK: It’s beautiful there, it’s really—it’s hard to complain. Yeah. FN: Yeah. It’s reflected in the mortgage, or rent. EL: Yeah KK: Right. FN: Big time. EL: So yeah, what theorem have you decided to share with us today? FN: You mean, not everyone else chose the Pythagorean Theorem? EL: It is a very good theorem. FN: Yeah. I chose the Pythagorean Theorem. I have some reasons, actually five reasons. KK: Five, good. FN: I was thinking, yeah, that's a lot of reasons considering I don't have, you know, five reasons to do anything else! So I don't know, should I just talk about my reasons? EL: Yeah, what’s your first reason? FN: Jump right in? EL: Well, actually, we should probably actually at least say what the Pythagorean theorem is. It's a very familiar theorem, and everyone should have heard about it in a middle school math class from a teacher as great as Fawn. But yeah, so could you summarize the theorem for us? FN: Well, that's just it. Yeah, I chose it for one, it’s the first and most obvious reason, because I am at the middle school. And so this is a big one for us, if not the only one. And it's within my pay grade. I can wrap my head around this one. Yeah, it's one of our eighth grade Common Core standards. And the theorem states that when you have a right triangle, the sum of the squares of the two legs is equal to the square of the hypotenuse. KK: Right.FN: How did I do, Kevin? KK: Well, yeah, that's perfect. In fact, today I was—it just so happens that today, I was looking through the 1847 Oliver Byrne edition of Euclid’s Elements, this sort of very famous one with the pictures where the colors, shapes, and all of that, and I just I happened to look at that theorem, and the proof of it, which is really very nice. FN: Yeah. So it being you know, the middle school one for us, and also, when I talk about my students doing this theorem—I just want to make sure that you understand that I no longer teach eighth grade, though. This is the first year actually at Mesa, and I've been there 16 years, that I do not get eighth graders. I'm teaching sixth graders. So when I refer to the lessons, I just want to make sure that you understand these are my former students. EL: Okay. FN: Yeah. And once upon a time, we tracked our eighth graders at Mesa. So we had a geometry class for the eighth graders. And so of course, we studied the Pythaogrean theorem then. KK: So you have reasons. FN: I have reasons. So that was the first reason, it’s a big one because, yeah. The second reason is there are so many proofs for this theorem, right? It's mainly algebraic or geometric proofs, but it's more than any other theorem. So it's very well known. And, you know, if you ever Google, you get plenty of different proofs. And I had to look this up, but there was a book published in 1940 that already had a 370 proofs in it. KK: Yeah. FN: Yeah. Even one of our presidents, I don't know if you know this, but yeah, this is some little nice trivia for the students. EL: Yeah. FN: One of our presidents, Garfield, submitted a proof back in 18-something. EL: Yeah. FN: He used trapezoids to do that. KK: He was still in Congress at the time, I think the story is that, you know, that he was in the House of Representatives. And like, it was sort of slow on the floor that day, and he figured out this proof, right. FN: Yeah. And then people continue to submit, and the latest one that I know was just over a year ago, back in November 2017, was submitted. That's the latest one I know. Maybe there was one just submitted two hours ago, who knows? And his was rearranging the a-squared and b-squared, the smaller squares, into a parallelogram. So I thought that was interesting. Yeah. And what's interesting is Pythagoras, even though it's the Pythagorean Theorem, he was given credit for it, it was a known long before him. And I guess there's evidence to suggest that it was by developed by a Hindu mathematician around 800 BC. EL: Okay. FN: And Pythagoras was what? 500-something. KK: Something like that. EL: Yeah. FN: Yeah, something like that. But he was the first, I guess he got credit, because he was the first to submit a proof. He wasn't just talking about it, I guess it was official, it was a formal proof. And his was rearrangement. And I think that's a diagram that a lot of us see. And the kids see it. It's the one where you got the big, the big c square in the middle with the four right triangles around it, four congruent right triangles. Yeah. And then just by rearranging that big c squared became two smaller squares, your a squared and b squared. Yeah. EL: Yeah. And I think it was known by, or—you know, I'm not a math historian. And I don't want to make up too much history today. But I think it has been known by a lot of different people, even as far back as Egyptians and Babylonians and things, but maybe not presented as a mathematical theorem, in the same kind of way that we might think about theorems now. But yeah, I think this is one of these things that like pretty much every human culture kind of comes up with, figuring out that this is true, this relationship. KK: Yeah, I think recently wasn’t it? Or last year? There's this Babylonian tablet. And I remember seeing on Twitter or something, there was some controversy about someone claimed that this proved that the the Babylonians knew, all kinds of stuff, but really— EL: Well, they definitely knew Pythagorean triples. KK: Yeah, they knew lots of triples. Maybe you wrote about this, Evelyn. EL: I did write about it, but we won’t derail it this way. We can put a link to that. I’ll get too bothered. FN: Now that you brought up Pythagorean triples, how many do you know? How many of those can you get the kids to figure out? Of course, including Einstein submitted a proof. And I thought it was funny that people consider Einstein’s proof to be the most elegant. And I'm thinking, “Well, duh, it’s Einstein.” Yeah. And I guess I would have to agree, because there were a lot of rearrangements in the proofs, but Einstein, you know, is like, “Yeah, I don't need no stinking rearrangement.” So he stayed with the right triangle. And what he did was draw in the altitude from the 90 degree angle to the hypotenuse, and used similar triangles. And so there was no rearrangement. He simply made the one triangle into—by drawing in that altitude, he got himself three similar triangles. And yeah, and then he drew squares off of the hypotenuse of each one of those triangles, and then wrote, you know, just wrote up an equation. Okay, now we're just going to divide everything by a factor. The one that was drawn, in you just divide out the triangle, then you just you end up with a squared plus b squared equals c squared. It's hard to do without the the image of it, but yeah. EL: But yeah, it is really a lovely one. FN: Yeah. And this is something I didn't know. And it was interesting. I didn't know this until I was teaching it to my eighth graders. And I learned that, I mean, normally, we just see those squares coming off of the right triangle. And then I guess one of the high school students—we were using Geometer’s Sketchpad at the time, and one of the high school students made an animated sketch of the Pythagorean theorem. And, you know, he was literally drawing Harry Potter coming off of the three sides. And you know, and I just, Oh, I said, yeah, yeah. You don't have to have squares as long as they’re similar figures, right, coming off the edges. That would be fine. So that was fun to do. Yeah. So I have my kids just draw circles so that they—just anything but a square coming off of the sides, you know, do other stuff. EL: Well, now I’m trying to—my brain just went to Harry Potter-nuse. KK: Boo. EL: I’m sorry, I’m sorry. FN: That’s a good one. KK: So I have forgotten when I actually learned this in life. You know, it's one of those things that you internalize so much that you can't remember what stage of your education you actually learned it in. So this is this is an actual Common Core eighth grade standard? FN: Yes, yes. In the eighth grade, yeah. KK: I grew up before the Common Core, so I don't really remember when we learned this. FN: I don't know. Yeah, prior to Common Core, I was teaching it in geometry. And I don't think it was—it wasn't in algebra, you know, prior to these things we had algebra and then geometry. So yeah. My third reason—I’m actually keeping track, so that was the second, lots of proofs. So the third reason I love the Pythagorean theorem is one fine day it led me to ask one of the best questions I'd asked of my geometry students. I said to them, “I wonder if you know how to graph and irrational number on the number line.” I mean, the current eighth grade math standard is for the kids to approximate where an irrational numbers is on the number line. That's the extent of the standard. So I went further and just asked my kids to locate it exactly. You know, what the heck? EL: Nice. Yeah. FN: And I actually wrote a blog post about it, because it was one of those magical lessons where you didn't want the class to end. And so I titled the post with “The question was mine, but the answer was all his.” And so I just threw it out to the class, I began with just, “Hey, where can we find—how do you construct the square root of seven on the number line?” And so, you know, they did the usual struggle and just playing around with it, but one of the kids towards the end of class, he got it, he came up with a solution. And I think when I saw it, and heard him explain it, it made me tear up, because it's like, so beautiful. And I'm so glad I did, because it was not, you know, a standard at all. And it was just something at the spur of the moment. I wanted to know, because we'd been working a lot with the Pythagorean theorem. And, yeah, so what he did was he drew two concentric circles, one with radius three and one with radius four on the coordinate plane, and the center is at (0,0). If you can imagine two concentric circles. And then he drew in y=-3, a line y=-3. And then you drew a line perpendicular to that line, that horizontal line, so that it intersects the—right, perpendicular to the horizontal line at negative three. And it intersects the larger circle, the one with radius four. KK: Yeah, okay. FN: So eventually what he did was he created, yeah, so you would have a right triangle created with one of the corners at (0,0). And the triangle would have legs of—the hypotenuse would be, what, four, the hypotenuse is four. One of the legs is three, and the other leg must be √7. EL: Oh. KK: Oh, yeah, okay. FN: Yeah, yeah. So it's just so beautiful. EL: That is very clever! FN: Yeah, it really was. So every time I think about the Pythagorean theorem, I think back on that lesson. The kids really tried. And then from √7, we tried other routes. And we had a great time and continued to the next day. EL: Oh, nice. I really liked that. That brought a big smile to my face. KK: Yeah. FN: The fourth reason I love the Pythagorean theorem is it always makes me think of Fermat’s Last Theorem. You know, it looks familiar, similar enough, where it states that no three positive integers a, b, and c can satisfy the equation of an+bn=cn. So for any integer value of n greater than 2. So it works for the Pythagorean Theorem, but any integer, any exponent greater than two would work. So I love—whenever I can, I love the history of mathematics, and whatever, I try to bring that in with the kids. So I read the book on the Fermat’s Last Theorem, and I kind of bring it up into the students for them to realize, Oh, my gosh, this man, Andrew Wiles, who solved it—and it's, you know, it's an over 300 year old theorem. And yeah, for him to first learn about the theorem when he was 10, and then to spend his life devoted to it. I mean, I can't think of a more beautiful love story than that. And yeah, so bring that to the kids. And I actually showed them the first 10 minutes of the documentary by BBC on Andrew Wiles. And just right, when he tears up, and, you know, I cannot stop tearing up at the same time because it—I don't know, it's just, it's that kind of dedication and perseverance. It's magical, and it's what mathematicians do. And so, you know, hopefully that supports all this productive struggle, and just for the love for mathematics. So, kind of get all geeky on the kids. EL: Yeah, that is a lovely documentary. FN: Yeah. Yeah. It's beautiful. All right. My fifth and final reason for loving this theorem is Pythagoras himself. What a nut! EL: Yeah, I was about to say, he was one weird dude. FN: Yeah, yeah. So, I mean, he was a mathematician and philosopher, astronomer and who knows what else. And the whole mystery wrapped up in the Pythagorean school, right? He has all these students, devotees. I don't know, it's like a cult! It really is like a cult because they had a strict diet, their clothing, their behaviors are a certain way. They couldn't eat meat or beans, I heard. EL: Yeah. FN: Yeah. And something about farting. And they believed that each time you pass gas that part of your soul is gone. EL: That’s pretty dire for a lot of us, I think. FN: Yeah. And what's remarkable also was that, the very theorem that he's named, you know, that's where I guess one of his students—I don't remember his name—but apparently he discovered, you know, the hypotenuse of √2 on the simple 1x1 isosceles triangle, and √2 and what did that do to him. The story goes he was thrown overboard for speaking up. He said, hey, there might be this possibility. So that's always fun, right? Deat…

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    Episode 38 - Robert Ghrist Mar 14, 2019
    Show notes

    Episode 38 - Robert Ghrist Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a podcast that starts with math and goes all kinds of weird places. I'm Kevin Knudson, professor of mathematics at the University of Florida and here is your other host. Evelyn Lamb: Hi. I'm Evelyn Lamb. I’m a freelance math and science writer in Salt Lake City. Happy New Year, Kevin. KK: And to you too, Evelyn. So this will happen, this will get out there in the public later. But today is January 1, 2019. EL: Yes, it is. KK: And Evelyn tells me that it was it's cold in Utah, and I have my air conditioning on. EL: Yes. KK: That seems about right. EL: Yeah. Our high is supposed to be 20 today. And the low is 6 or 7. So we really, really don't have the air conditioner on. KK: Yeah, it's going to be 82 here today in Gainesville. [For our listeners outside of the USA: temperatures are in Fahrenheit. Kevin does not live in a thermal vent.] I have flip flops on. EL: Yeah. Yeah, I’m a little jealous. KK: This is when it's not so bad to live in Florida, I’ve got to say. EL: Yeah. KK: Anyway, Well, today, we are pleased to welcome Robert Ghrist. Rob, you want to introduce yourself? RG: Hello, this is Robert Ghrist. KK: And say something about yourself a little, like who you are. RG: Okay. All right, so I am a professor of mathematics and electrical and systems engineering at the University of Pennsylvania. This is in Philadelphia, Pennsylvania. I've been in this position at this wonderful school for a decade now. Previous to that I had tenured positions at University of Illinois in Urbana Champaign and Georgia Institute of Technology. KK: So you've been around. RG: A little bit. EL: Oh, and so I was just wondering, so it that a joint appointment between two different departments, or is it all in the math department? RG: This is a split appointment, not only between two different departments, but between two different schools. EL: Oh, wow. RG: The math appointment is in the School of Arts and Sciences, and the engineering appointment is in the School of Engineering. This is kind of a tricky sort of position to work out. This is one of the things that I love about the University of Pennsylvania is there are very low walls between the disciplines, and a sort of creative position like this is is very workable. And I love that. KK: Yeah, and your undergraduate degree was actually in engineering, right? RG: That’s correct I got turned on to math by my calculus professor, a swell guy by the name of Henry Wente, a geometer. KK: Excellent. Well, I see you’re continuing the tradition. Well, we’ll talk about that later. And actually, you actually have an endowed chair name for someone rather famous, right? RG: That’s true. The full title is The Andrea Mitchell PIK professor of mathematics, electrical and systems engineering. This is Andrea Mitchell from NBC News. She and her husband Alan Greenspan funded this position. Did not intend to hire me specifically, or a mathematician. I think she was rather surprised when the chair that she endowed wound up going to a mathematician, but there it is, we get along swell. She's great. KK: Nice. EL: Nice. That's really interesting. KK: Yup. So, Rob, what is your favorite theorem? RG: My favorite theorem is, I don't know. I don't know the name. I don't know that this theorem has a name, but I love this theorem. I use this theorem all the time in all the classes I teach, it seems. It's a, it's a funny thing about basically Taylor expansion, or Taylor series, but in an operator theoretic language. And the theorem, roughly speaking, goes like this: if you take the differentiation operator on functions, let's say just single input, single output functions, the kind of things you do in basic calculus class. Call the differentiation operator D. Consider the relationship between that operator and the shift operator. I’m going to call the shift operator E. This is the operator that takes a function f, and then just shifts the input by 1. So E(f(x)) is really— pardon me, E(x) is really f evaluated at x+1. We use shift— EL: I need a pencil and paper. RG: Yeah, I know, right? We use the shift operator all the time in signal processing, in all kinds of things in both mathematics and engineering. And here's the theorem, here's the theorem. There's this wonderful relationship between these two operators. And it's the following. If you exponentiate the differentiation operator, if you take e to the D, you get the shift operator. KK: This is remarkable. RG: What does this mean? What does this mean? KK: Yeah, what does it mean? I actually did work this out once. So what our listeners don't know is that you and I actually had this conversation once in a bar in Philadelphia, and the audio quality was so bad, we're having to redo this. EL: Yeah. KK: So I went home, and I worked this out. And it's true, it does work out. But what does this mean, Rob? Sort of, you know, in a manifestation physically? RG: Yeah, so let me back up. The first question that I ask students, when they show up in calculus class at my university is, is, what is e to the x? What does that even mean? What does that mean when x is an irrational number, or an imaginary number, or something like a square matrix, or an operator? And, of course, that takes us back to the the interpretation of exponentiation in terms of the Taylor series at zero, that I take that infinite series, and I use that to define what exponentiation means. And because things like operators, things like differentiations, or shifts, you can take powers of those by composition, by iteration, and you can rescale them. Then you can exponentiate them. So I can talk about what it means to exponentiate the differentiation operator by taking first D to the 0, which of course, is the identity, the do-nothing operator, and then adding to it D, and then adding to that D squared divided by 2 factorial [n factorial is the product of the integers 1x2,…n], that's the second derivative, then D cubed divided by 3 factorial, that's the third derivative. If I can keep going, I’ve exponentiated the differentiation operator. And the theorem is that this is the shift operator in disguise. And the proof is one line. It's Taylor expansion. And there you go. Now, this isn't your typical sort of my favorite theorem, in that I haven't listed all the hypotheses. I haven't been careful with anything at all. But one of the reasons that this is my favorite theorem is because it's so useful when I'm teaching calculus to students, when I'm teaching basic dynamical systems to students where, you know, in a more advanced class, yeah, we'd have a lot of hypotheses. And, oh, let's be careful. But when you're first starting out, first trying to figure out what is differentiation, what is exponentiation, this is a great little theorem. EL: Yeah, this conceptual trip going between the Taylor series, or going between the idea of e to the x, or 2 to the x or something where we really have a, you know, a fairly good grasp of what exponentiation means in that case, if we, if we're talking about squares or something like that, and going then to the Taylor series, this very formal thing, I think that's a really hard conceptual shift. I know that was really hard for me. RG: Agreed. KK: Yeah. So I, I wonder, though, I mean, so what's a good application of this theorem, like, in a dynamics class, for example? Where does this pop up sort of naturally? And I can see that it works. And I also agree that this idea of—I start calculus there, too, by the way, when I say, you know, what does e to the .1 mean, what does that even mean? RG: What does that even mean? KK; Yeah, and that’s a good question that students have never really thought about. They’re just used to punching .1 into a calculator and hitting the e to the x key and calling it a day. So, but where would this actually show up in practice? Do you have a good example? RG: Right. So when I teach dynamical systems, it's almost exclusively to engineering students. And they're really interested in getting to the practical applications, which is a great way to sneak in a bunch of interesting mathematics and really give them some good mathematics education. When doing dynamical systems from an applied point of view, stability is one of the most important things that you care about. And one of the big ideas that one has to ingest is that of stability criteria for, let's say, equilibria in a dynamical system. Now, there are two types of dynamical systems that people care about, depending on what notion of time you're using— continuous time or discrete time. Most books on the subject are written for one or the other type of system. I like to teach them both at once, but one of the challenges of doing that is that the stability criteria are are different, very different-looking. In continuous time, what characterizes a stable equilibrium is when you look at all of the eigenvalues of the linearization, the real parts are less than zero. When you move to a discrete time dynamical system, that is a mapping, then again, you're looking at eigenvalues of the linearization, but now you want the modulus to be less than 1. And I find that students always struggle with “Why is it different?” “Why is it this way here, and that way there?” And of course, of course, the reason is my favorite little theorem, because if I look at the evolution operator in continuous-time dynamics—that’s the derivative—versus the evolution operator in discrete-time dynamics—that is the shift, move forward one step in time—then if I want to know the relationship between the stability and, pardon me, the stable and unstable regions, it is exponentiation. If I exponentiate the left hand side of the complex plane, what do I get? I get the region in the plane with modulus less than 1. KK: Right. RG: I find that students have a real “aha” moment when they see that relationship, and when they can connect it to the relationship between the evolution operators. EL: I’m having an “aha” moment about this right now, too. This isn't something I had really thought about before. So yeah, this is a really neat observation or theorem. RG: Yeah, I never really see this written down in books. KK: That’s—clearly now you should write a book. RG: Another one? KK: Well, we'll talk about how you spend your time in a little while here. But, no, Rob, I mean, so Rob has this—I don't know if it's famous, but well known—massive open online course through Coursera where he does calculus, and it's spectacular. If our listeners haven't seen it, is it on YouTube, Rob? Can you actually get it at YouTube now? RG: Yes, yes. The the University of Pennsylvania has all the lectures posted on a YouTube channel. KK: Well, I actually downloaded to my machine. I took the MOOC A few years ago, just for fun. And I passed! Remarkably. RG: With flying colors, with flying colors. KK: Yeah, I'm sure you graded my exam personally, Rob. RG: Personally. KK: And anyway, this is evidence for how lucky are students are, I think. Because, you know, you put so much time into this, and these these little “aha” moments. And the MOOC is full of these things. Just really remarkable stuff, especially that last chapter, which is so next level, the digital calculus stuff, which sort of reminds me of what we're talking about. Is there some connection there? RG: Oh yes, it was creating that portion of the MOOC that really, really got me to do a deep dive into discrete analogs of continuous calculus, looking at the falling powers notation that is popular in computer science in Knuth’s work and others, thinking in terms of operators. Yeah, that portion of the MOOC really got me thinking a lot about these sorts of things. KK: Yeah, I really can't recommend this highly enough. It’s really great. EL: Yeah, so I have not had the benefit of this MOOC yet. So digital calculus, is that meaning, like, calculus for computers? Or what exactly is that? What does that mean? RG: One of the things that I found students really got confused about in a basic single variable calculus class is, as soon as you hit sequences and series, their heads just explode because they get sequences and series confused with one another, and it all seems unmotivated. And why are we bothering with all these convergence tests? And where’d they come from? All this sort of thing. EL: And why is it in a calculus class? RG: Why is it even in a calculus class after all these derivatives and integrals? So the way that I teach it is when we get to sequences and series, you know, in the last quarter of the semester, I say, Okay, we've done calculus for functions with an analog input and an analog output. Now, we want to redo calculus for functions with a digital input and an analog output. And such functions we're going to call sequences. But I'm really just going to think of it as a function. How would you differentiate such a thing? How would you integrate such a thing? That leads one to think about finite differences, which leads to some some nice approaches to numerical methods. That leads one to looking at sums and numerical integration. And when you get to improper integrals over an unbounded domain? Well, that's series, and convergence tests matter. KK: Yeah, it's super interesting. We will provide links to this. We’ll find the YouTube links and provide them. EL: Yeah. KK: So another fun part of this podcast, Rob, is that we ask our guests to pair their theorem with something, and I assume you're going to go with the same pairing from our conversation back in Philadelphia. RG: Oh yes, that’s right. KK: What is it? RG: My work is fueled by a certain liquid beverage. KK: Yeah. RG: It’s not wine. It's not beer. It's not whiskey. It's not even coffee, although I drink a whole lot of coffee. What really gets me through to that next-level math is Monster. That's right. Monster Energy Drink, low carb if you please, because sugar is not so good for you. Monster, on the other hand, is pretty great for me, at any rate. I do not recommend it for people who are pregnant or have health problems, problems with hearts, anything like this, people under the age of 18, etc, etc. But for me, yeah, Monster. KK: Yeah. There's lots of empties in your office, too like, up on the shelf there, which I'm sure have some significance. RG: The wall of shame, that’s right. All those empty monster cans. KK: See, I can't get into the energy drinks. I don't know. I mean, I know you're also fond of scotch. But does that does that help bring you down from the Monster, or is it… RG: That’s a rare treat. That's a rare treat. KK: Yeah, it should be. So when did your obsession with Monster start? Does this go back to grad school, or did it even exist when we were in grad school? Rob and I are roughly the same age. Were energy drinks a thing when we were in grad school? I don't remember. RG: No, no. I didn't have them until, gosh, what is it, sometime within the past decade? I think it was when I was first working on that old calculus MOOC, like, what was that, six years ago? Six, seven years ago, is when I was doing that. KK: Yup. RG: That was difficult. That was difficult work. I had to make a lot of videos in a short amount of time. And, yup, the Monster was great. I would love to get some corporate sponsorship from them. You know, maybe, maybe try to pitch extreme math? I don't know. I don't think that's going to work. KK: I don't know. I think it's a good angle, right? I mean, you know, they have this monster truck business, right? So there is th…

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    Episode 37 - Cynthia Flores Feb 28, 2019
    Show notes

    Episode 37 - Cynthia Flores Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello and welcome to My Favorite Theorem, a podcast where we ask mathematicians to tell us about their favorite theorems. I'm Evelyn Lamb. I'm one of your hosts. I am a freelance math and science writer in Salt Lake City, Utah. Here's your other host. Kevin Knudson: Hi, I'm Kevin Knudson, professor of mathematics at the University of Florida. How's it going, Evelyn? All right. I am making some bread right now, and it smells really great in my house. So slightly torturous because I won't be able to eat it for a while. KK: Sure. So I make my own pizza dough. But I always stop at bread. I never that extra step. I don't. I don't know why. Are you making baguettes? Are you doing the whole... EL: No. I do it in the bread machine. KK: Oh. EL: Because I'm not going to make, yeah, I'm not going to knead and shape a loaf. So that's the compromise. KK: Oh. That that's the fun part. So I've had a sourdough starter for, the same one for at least three or four years now. I've kept this thing going. And I make my own pizza crust, but I'm just lazy with bread. I don't eat a lot of bread. EL: So yeah. And joining us to talk about--on BreadCast today--I'm very delighted to welcome Cynthia Flores. So hi. Tell us a little bit about yourself. Cynthia Flores. Oh, thanks for having me on the show. I'm so grateful to join you today, Kevin and Evelyn. Well, I'm an assistant professor of mathematics and applied physics at California State University Channel Islands in the Department of Math and Physics. The main campus is not located on the Channel Islands. EL: Oh, that's a bummer. CF: It's actually located in Camarillo, California. It's one hour south of Santa Barbara, one hour north of downtown Los Angeles, roughly. But the math department does get to have an annual research retreat at the research station located in the Santa Rosa Island. So that's kind of neat. KK: Oh, how terrible for you. EL: Yeah. That must be so beautiful. KK: I was in Laguna Beach about a week and a half ago, which is, of course, further south from there, but still just spectacularly beautiful. Really nice. CF: Yeah, I feel really fortunate to have the opportunity to stay in the Southern California area. I did my PhD at UC Santa Barbara, where I studied the intersections of mathematical physics, partial differential equations, and harmonic analysis and has motivated what I'm going to talk about today. KK: Good, good, good. EL: Yeah. Well, and Cynthia was on another podcast I host, the Lathisms podcast. And I really enjoyed talking with her then about the some of the research that she does. And she had some fun stories. So yeah, what is your favorite theorem? What do you want to share with us today? CF: I'm glad you asked. I have several favorite theorems, and it was really hard to pick, and my students have heard me say repeatedly that my favorite theorem is the fundamental theorem of calculus. EL: Great theorem. KK: Sure. CF: It's also a very, I find, intimidating theorem to talk about on this series, especially with so many creative individuals pairing their favorite theorems with awesome foods and activities. And so I just thought that one was maybe something to live up to. And I wanted to start with something that's a little closer to to my research area. So I found myself thinking of other favorites, and there was one in particular that does happen to lie at the intersection of my research area, which is mathematical physics, PDEs and harmonic analysis. And it's known as Heisenberg's Uncertainty Principle. That's how it's really known by the physics community. And in mathematics, it's most often referred to as Heisenberg's Uncertainty Inequality. EL: Okay. CF: So, is it familiar? I don't know. EL: I feel like I've heard of it, but I don't--I feel like I've only heard of it from kind of the pop science point of view, not from the more technical point of view. So I'm very excited to learn more about it. KK: So I actually have a story here. I taught a course in mathematics and literature a couple years back with a friend of mine in the in the foreign languages department. And we watched A Serious Man, this Coen Brothers movie, which, if you haven't seen is really interesting. But anyway, one of the things I made sure to talk about was Heisenberg's Uncertainty Principle, because that's sort of one of the themes, and of course now I forgotten what the inequality is. But I mean, I remember it involves Planck's constant, and there's some probability distribution, so let's hear it. CF: Mm hmm. Yeah, yeah. So I was, I was like, this is what I'm going to pair it with. Like, I'm going to pair the conversation, like the mathematics description, physical description, with, basically I was thinking of pairing it with something Netflix and chill-like. I'm really glad that you brought that up, and I'll tell you more in a little bit about what I'm pairing it with. But first, I'll start mathematically. Mathematically, the theorem could be stated as follows. Given a function with sufficient regularity and decay assumptions, the L2 norm of the function is less than or equal to 2 over the dimension the function's defined on, multiplied by the product of the L2 norm of its first moment and the L2 norm of its gradient. And so mathematically, that's the inequality. This wasn't stated this way by Heisenberg in the 1920s, which I believe he was recognized with a Nobel Prize for later on. Physically, Heisenberg described this in different ways it could be understood. Uncertainty might be understood as the lack of knowledge of a quantity taken by an observer, for example, or to the uncertainty due to experimental inaccuracy, or ambiguity in some definition, or statistical spread, as, as Kevin mentioned. And actually, I'm going to recommend to the listeners to go to YouTube. There's a YouTuber named Veritasium, I'm not sure if I'm pronouncing that correctly. EL: Oh, yeah, yeah. CF: Yeah, he has a four minute demonstration of the original thought by Heisenberg and an experiment having to do with lasers that basically tells us it's impossible to simultaneously measure the position and momentum of a particle with infinite precision. The infinite precision part would be referring to something that we might call certainty. So in the experiment, that the YouTuber is recreating, a laser is shown through two plates that form a slit, and the split is becoming narrower and narrower. The laser is shone through the slit and then projected onto a screen. And as the slit is made narrower, the spot on the screen, as expected, is also becoming narrower and narrower. And at some point--you know, Veritasium does a really good job of creating this sort of like little "what's going to happen" excitement-- just when the slit seems to completely disappear and become infinitesimally small, the expectation might be that the that the laser projecting onto the screen would disappear too, but actually at a certain point, when the slit is so narrow, it's about to close, the spot on the screen becomes wider. We see spread. And this is because the photons of light have become so localized that the slit and their horizontal momentum has to become less well defined. And this is a demonstration of Heisenberg's Uncertainty Principle. And so according to Heisenberg--and this is from one of his manuscripts, and I wish I would have written down which one, I'm just going to read it-- "at the instant of time when the position is determined, that is, at the instant when the photon is scattered by the electron, the electron undergoes a discontinuous change in momentum, and this change is the greater the smaller the wavelength of the light employed, in other words, the more exact determination of the position. At the instant at which the position of the electron is known, its momentum, therefore, can be known only up to magnitudes which correspond to that discontinuous change; thus, the more precisely the position is determined, the less precisely the momentum is known, and conversely." And there's also, so momentum and position were sort of the original context in which Heisenberg's Uncertainty Principle was stated, mainly for quantum mechanics. The inequality theorem that I presented was really from an introductory book to nonlinear PDEs, which is really what I study, nonlinear dispersive PDEs specifically. So you could use a lot of Fourier transforms and stuff like that. But it has multiple variations, one of which is Heisenberg's Uncertainty Principle of energy and time, which more or less is going to tell you the same thing, which is going to bring me to my pairing. Can I can I share my pairing? KK: Sure. CF: My pairing--I want to pair this with a Netflix and chill evening with friends who enjoy the Adult Swim animated show Rick and Morty. KK: Okay. CF: And an evening where you also have the opportunity to discuss sort of deep philosophical questions about uncertainty and chaos. And so really the show's on Hulu, so you could watch the show. I don't know if either one of you are familiar with the show. KK: Oh, yeah. Yeah. I have a 19 year old son, how could I not be? CF: And actually my students were the ones that brought this show and this specific episode to my attention, and I watched it, and so I'll see a little bit about the show. It's a bit, it's somewhat inspired by The Back to the Future movies. It's a comedy about total reckless and epic space adventures with lots of dark humor and a lot of almost real science. I mean, Rick is a mad scientist and Morty is in many ways opposite to Rick. Morty is Rick's grandson and sidekick. Rick uses a portal gun that he created. It allows him and Morty to travel to different realities where they go on some fun adventures. And there are several references to formulas and theorems describing our everyday life. And so particularly the season premiere of season two, Rick and Morty pays homage to Heisenberg's Uncertainty Principle as well as Schrodinger's cat paradox and includes a mathematical proof of Rick's impression of his grandkids. I won't spoil what he proves about them. I'll let the reader, the listeners, go check that out. But basically season one ended with Rick freezing time for everyone except for him and his two grandkids Morty and Summer. In this premiere, Rick unfreezes time and causes a disturbance in their reference timeline, and any uncertainty introduced by the three individuals gets them removed entirely from time and causes a split in reality into multiple simultaneous realities. The entire episode is following Heisenberg's Uncertainty Principle for energy and time and alluding to the concept from the quantum world that chaos is found in the distribution of energy levels of certain atomic systems. So I'm going to back it up a little bit. We talked about Heisenberg's Uncertainty Principle in terms of momentum and position. Heisenberg's Uncertainty Principle for energy and time is for simultaneous measurements of energy and time, and specifically that the distribution of energy levels and uncertainty and its measurement is a metaphor to chaos within the system. So within a time interval, it's not possible to measure chaos precisely. There has to be uncertainty in the measure, so that the product of the uncertainty and energy and the uncertainty and the time remains larger than h over 4π, the Planck's constant. In other words, you cannot have both, you cannot simultaneously have both small uncertainty in both measurements. In other words, lots of certainty, right? You have small uncertainty, you have lots of certainty. So you can't have that both happening. So less chaos leads to more uncertainty and vice versa. Less than certainty (or more certainty) leads to more chaos. And so this, you know, this episode, if you watch it, seems to present the common misconception that more uncertainty leads to more chaos. And this is where I've thought about this really hard and even tried to find someone who put it nicely, maybe on a video, I couldn't. But I think--this is just my opinion--I think the writers really got it right on this episode, because the moment that the timeline merges in the episode is the moment when the main character Rick has given up on his chances of fixing a broken tool he was counting on for fixing the timeline. So in fact, in this episode, he's shown doing something which is unlike him. He's shown praying and asking God, or his maker, for forgiveness, you know, as the timeline is, as all of these realities are collapsing. And in my opinion of Rick, this is the largest amount of uncertainty he's ever displayed throughout this series. And this happens right at the moment that he restores the timeline and therefore reduces the chaos. So I really think the writers got it right on that. EL: That's really neat. CF: Yeah, yeah, I loved it. And so for me, I also find this as a perfect time to, you know, hang out with friends, Netflix and chill it up, and then afterwards talk, you know. I would really like to challenge the listeners to observe this phenomenon in their real life. And for some people, it might be a stretch. But to some extent, I think we observe Heisenberg's Uncertainty Principle in our daily lives, like in the sense that the more sure that we are about something, or the more plan that we've made for something, the more likely were observe chaos, right? The more things we've gotten planned out, the more things that are actually likely to go wrong. I get to see this at the university, right, with so many young minds planning out their future. And I really see that the more certain a student feels about their plan, the more likely they're going to feel chaos in their life, if things don't go according to plan. So I really enjoy Heisenberg's Uncertainty Principle on so many levels, mathematically, physically, maybe even philosophically, and observing it in our real lives. EL: Yeah, I really like the the metaphorical aspect you brought here. And if I can reveal how naive I am about Heisenberg's Uncertainty Principle, I didn't know that it was applicable in these different things other than just position and momentum. Maybe I'm the only one. But that's really interesting that there, so are there a lot of other places where this is also the case? CF: Well, mathematically, it's just a function defined, for example, on Rn that has regularity and decay properties, and so that function's L2 norm--so the statement of the theorem is under those decay and regularity assumptions, that function's L2 norm has to remain less than or equal to 2 over n multiplied by the product of the L2 norm of the first moment of the function times the L2 norm of the gradient of the function. And so in some sense, we can view that as talking about momentum and position, and so that has applications to various physical systems, both momentum and position. But in some sense, whenever you have a gradient of a function, it can also relate to some system's energy. And so mathematically, I think we have the position to view this in a the more abstract way, whereas physically, you tend to only read about the the momentum and position version and less about the energy and time version. So that's why it took me a long time to think about did the writers of Rick and Morty get it right, because they're basically relying on, it seems they're gi…

    Full show notes at the publisher

    Episode 36 - Nikita Nikolaev & Beatriz Navarro Lameda Feb 14, 2019
    Show notes

    Episode 36 - Nikita Nikolaev & Beatriz Navarro Lameda Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a special Valentine’s Day edition this year. Evelyn Lamb: Yes. KK: I’m one of your hosts, Kevin Knudson, professor of mathematics at the University of Florida. This is your other host. EL: Hi. I’m Evelyn Lamb. I’m a math and science writer in Salt Lake City, Utah. KK: How’s it going, Evelyn. EL: It’s going okay. We got, we probably had 15 inches of snow in the past day and a half or so. KK: Oh. It’s sunny and 80 in Florida, so I’m not going to rub it in. This is a Valentine’s Day edition. Are you and your spouse doing anything special? EL: We’re not big Valentine’s Day people. KK: So here’s the nerdy thing I did. So Ellen, my wife, is an artist, and she loves pens and pencils. There’s this great website called CW Pencil Enterprise, and they have a little kit where you can make a bouquet of pencils for your significant other, so this is what I did. Because we’ve been married for almost 27 years. I mean, we don’t have to have the big show anymore. So that’s our Valentine’s Day. EL: Yeah, we’re not big Valentine’s Day people, but I got very excited about doing a Valentine’s Day episode of My Favorite Theorem because of the guests that we have. Will you introduce them? KK: I know! We’re pleased to have Nikita Nikolaev and Beatriz Navarro, and they had some popular press. So why don’t you guys introduce yourselves and tell everybody about you? Nikita Nikolaev: Hi. My name is Nikita. I’m a postdoctoral fellow at the University of Geneva in Switzerland. I study algebraic geometry of singular differential equations. Beatriz Navarro Lameda: And I’m Beatriz. I’m currently a Ph.D. student at the University of Toronto, but I’m doing an exchange program at the University of Geneva, so that’s why we’re here together. And I’m studying probability, in particular directed polymers in random environments. EL: Okay, cool! So that is actually applicable in some way. BNL: Yes, it is. EL: Oh, great! KK: So why don’t we talk about this whole thing with the wedding? So we had this conversation before we started recording, but I’m sure our listeners would love to hear this. So what exactly happened at your wedding? NN: Both of us being mathematicians, of course, we had almost everybody was either a mathematician or somehow mathematically related, most of our guests. So we decided to have a little bit of fun at the wedding, sprinkle a little bit of maths here and there. And one of the ideas was to, when the guests arrive at the dinner, in order for them to find which table they’re sitting at, they would have to solve a small mathematical problem. They would arrive at the venue there, and they would open their name card, and the name card would contain a first coordinate. BNL: And a question. NN: And a question. And the questions were very bespoke. It really depended on what we know their mathematical background to be. We had many people in my former research group, so I pulled questions from their papers or some of the talks they’ve given. EL: This is so great! Yeah. NN: And there were some people who are, maybe they’re not mathematicians, they’re engineers or chemists or something, and we would have questions which are more mathematically flavored rather than actual mathematical questions just to make everyone feel like they’re at a wedding of mathematicians. EL: Right. NN: So right. They had to find out two coordinates. All the tables were named after regular polyhedra, and they had to find out what their polyhedron of the night was. EL: Okay. NN: In order to do that, there was a matrix of polyhedra. Each one had two coordinates, and once you find out what the two coordinates are, you look at that matrix, and it gives you what polyhedron you’ve got. So as a guest, you would open the name card, and it would contain your first coordinate and a question and a multiple choice answer. And the answers were, BNL: Usually it was one right answer and two crazy answers that had nothing to do with the question. Most of them were 2019 because that’s the year we got married, and then some other options. And then once you choose your answer, you would be directed to some other card that had a name of some mathematical term or some theorem, and that one would give you the second coordinate. NN: So we made this cool what we called maths tree. We had several of these manzanita trees, and we put little cards on them with names, with these mathematical terms, with the answers, with people’s questions, and we just had this tree with cards hanging down, more than 100 cards hanging down. What I liked, in mathematicians it induced this kind of hunting instinct. You somehow look at this tree, and there are all these terms that you recognize, and you’ve seen before in your mathematical career, and you’re searching for that one that you know is the correct one. BNL: And of course we wanted to make sure everyone found their table, so if they for any reason chose the wrong answer, they would also be directed to some card with a mathematical term. And when they opened it, it would say, “Oops, try again.” So that way you knew, okay, I just have to go and try again and find what the correct coordinate would be. KK: This is amazing. NN: And then to foolproof the whole thing, during the cocktail hour, they would do this kind of hunting for mathematical terminology, but then the doors would open into the dinner room, and just like most textbooks, when you open the back of the textbook, there’s the answer key, in the dinner room we had the answer key, which was a poster with everyone’s names BNL: And their polyhedra NN: And their polyhedron of the night. EL: Yeah. NN: So it was foolproof. I think some commentators on the internet were very concerned that some guests wouldn’t find their seat and starve to death. EL: Leave hungry. NN: No, it was all thought through completely. KK: Some of the internet comments, people were just incredulous. Like, “I can’t believe these people forced their guests to do this!” They don’t understand, we would think this was incredible. This is amazing! EL: Yeah! So delightfully nerdy and thoughtful. So, yeah, we’ve mentioned, this did end up on the internet in some way, which is how I heard about it, because I sadly was not invited to your wedding. (Since this is the first time I have looked at you at all.) So yeah, how did it end up making the rounds on some weird corner of the internet? NN: So basically a couple of weeks before the wedding, I made a post on Facebook. It was a private Facebook post, just to my Facebook friends. You know, a Facebook friend is a very general notion. EL: Right. NN: I kind of briefly explained that all our guests are mathematicians, so we’re going to do this cool thing, we’re going to come up with mathematical questions, and one of my Facebook “friends,” a Facebook acquaintance, I later found out who it was, they didn’t like it so much, and they did a screengrab, and then they posted, with our names redacted and everything redacted, they made a post on Reddit which was like, “Maths shaming, look, these people are forcing their guests to solve a mathematical question to find their seat, maths shaming.” BNL: It was in the “bridezilla” thread. “This crazy bride is forcing their guests to solve mathematical problems,” and how evil she is. Which, funny because Nikita was the one who wrote that Facebook post. NN: I actually was the one. BNL: So it was not a bridezilla, it’s what I like to call a Groom Kong. NN: That’s right. So then this Reddit thread kind of got very popular, and later some newspaper in Australia picked it up, and then it just snowballed from there. Fox News, Daily Mail, yeah. KK: Well, this is great. This is good, now you’ve had your 15 minutes of fame, and now life can get back to normal. EL: Yeah. KK: This is a great story. Okay, but this is a podcast about theorems, so what is your favorite theorem? NN: Right, yeah. So we kind of actually thought long and hard about what theorem to choose. Like, what is our favorite theorem is such a difficult question, actually. KK: Sure. NN: It's kind of like, you know, what is your favorite music piece? And it's, I mean, it's so many variables. Depends on the day, right? EL: Yeah. NN: But we ended up deciding that we were going to choose the intermediate value theorem. KK: Oh, nice. NN: As our favorite theorem. KK: Good. BNL: So, yes, the intermediate value theorem is probably one of the first theorems that you learn when you go to university, right? Like calculus you start learning basic calculus, and it's one of the first theorems that you see. Well, what it says is that well, suppose you start with a continuous function f, and you look at some interval (a, b), so the function f sends a to f(a), b to f(b), and then you pick any value y that is between f(a) and f(b). And then you know that you will find a point c that is between a and b, such that f(c) equals y. So it looks like an incredibly simple statement. Obvious. KK: Sure. BNL: Right, but it is a quite powerful statement. Most students believe it without proof. They don't need it. It's, yes, absolutely obvious. But, well, we have lots of things that we like about the theorem. NN: Yeah, I mean, it feels incredibly simple and completely obvious. You look at it, and, you know, it's the only thing that could possibly be true. And the cool thing about it, of course, is that it represents kind of the essence of what we mean by continuous function. KK: Sure. EL: Yeah. NN: In fact, actually, if you look at the history, before our modern formal definition of continuity, that was part of the property that was a required property that people used to use as part of the definition of continuity. In fact, actually, if you look at the history, before we formalize the definition of continuity, people were very confused about what a continuous function actually should mean. And many thought, erroneously, that this intermediate value property was equivalent to continuity. In some sense, it's what you would want to believe because it really is the property that more or less formalizes what we normally tell our students, that heuristically, a continuous function is one that if you want to draw it, you can draw it without taking off your pencil off of a piece of paper, and that's what the Intermediate Value Theorem, this intermediate value property, it represents that really. But for all its simplicity and triviality, if you actually look at it properly, if you look at our modern definition of continuity using epsilon-delta, then it becomes not obvious at all. BNL: Yes. So if you see epsilon, you look at the definition of continuity, and have epsilons, deltas, how is it possible that from this thing, you get such an obvious statement, like the intermediate value theorem? So what the intermediate value theorem is telling us is that, well, continuous functions do exactly what we want them to do. They are what we intuitively think of a continuous function. So in a sense, what the intermediate value theorem is doing for us is serving as a bridge between this formal definition that we encounter in university. So we start first year calculus, and then our professor gives us this epsilon-delta definition of continuity. And it's like, oh, but in high school, I learned that a continuous function is one that I can draw without lifting my pencil. Well, the intermediate value theorem is precisely that. It's connecting the two ideas, just in a very powerful way. NN: Yeah. And also, you know, it cannot be overstated how useful it is. I mean, we use it all the time. As a geometer, of course, you know, you use some generalization of it, that continuous functions send connected sets to connected, and we use it all the time, absolutely, without thinking, we take it absolutely for granted. BNL: So even if you do analysis, you are using it all the time, because you can see that the intermediate value theorem is also equivalent to the least upper bound property, so the completeness axiom of the real numbers. Which is quite incredible, to see that just having the intermediate value theorem could be equivalent to such a fundamental axiom for the real numbers, right? So it appears everywhere. It's surprising. We know, it's very easy, when you see the proof of the intermediate value theorem, you see that it is a consequence of this least upper bound property, but the converse is also true. So in a sense, we have that very powerful notion there. KK: I don't think I knew the converse. So I'm a topologist, right? So to me, this is just a statement that the continuous image of a connected set is connected. But then, of course, the hard part is showing that the connected subsets of the real line are precisely the intervals, which I guess is where the least upper bound property comes in. BNL: Yes, indeed, yes. Exactly yes. KK: Okay. I haven't thought about analysis in a while. As it turns out, we're hiring several people this year. And, for some of them, we've asked them to do a teaching demonstration. And we want them all to do the same thing. And as it so happens, it's a calculus one demonstration about continuity and the intermediate value theorem. BNL: Oh. EL: Nice. KK: So in the last month, I've seen, you know, 10 presentations about the intermediate value theorem. And I've come to really appreciate it as a result. My favorite application is, though, that you can use it to prove that you can always make any table level, or not level, but all four legs touch the ground at the same time. BNL: Yes. KK: Yeah, that's, that's great fun. The table won't be level necessarily, but all four feet will be on the ground, so it won't wobble. BNL: Yes. EL: Right. NN: If only it were actually applied in classrooms, right? KK: Right. EL: Yeah. NN: The first thing you always do when you come to, you sit at a desk somewhere, is to pull out a piece of paper to actually level it. EL: Yeah. So was this a theorem that you immediately really appreciated? Or do you feel like your appreciation has grown as you have matured mathematically? BNL: In my case, I definitely learned to appreciate it more and more as I matured mathematically. The first time I saw the theorem, it's like, "Okay, yes, interesting, very cool theorem." But I didn't realize at the moment how powerful that theorem was. Then as my mathematical learning continued, then I realized, "Oh, this is happening because of the intermediate value theorem. And this is also a consequence of it." So there's so many important things that are a consequence, of the intermediate value theorem. That really makes me appreciate it. NN: Well, there's also somehow, I think this also comes with maturity, when you realize that some very, what appear to be very hard theorems, if you strip away all the complexity, you realize that they may be really just some clever application of the intermediate value theorem. BNL: Like Sharkovskii's theorem, for example, is a theorem about periodic points of continuous functions. And it just introduces some new ordering in the natural numbers. And it tells you that if you have a periodic point of some period m, then you will have periodic points of any period that comes after m in that ordering. You can also look at the famous "period three implies chaos." KK: Right. BNL: A big component of it is period three implies all other periods. And the proof of it is really just a clever use of the intermediate value theorem. It's so interesting, that such an important and famous theorem is just a very kind of immediate--though, you know, it takes some work to get it--but you can definitely do it with just the Intermediate Value Theorem. And I actually like to…

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