TopPodcast.com
Menu
  • Home
  • Top Charts
  • Top Networks
  • Top Apps
  • Top Independents
  • Top Podfluencers
  • Top Picks
    • Top Business Podcasts
    • Top True Crime Podcasts
    • Top Finance Podcasts
    • Top Comedy Podcasts
    • Top Music Podcasts
    • Top Womens Podcasts
    • Top Kids Podcasts
    • Top Sports Podcasts
    • Top News Podcasts
    • Top Tech Podcasts
    • Top Crypto Podcasts
    • Top Entrepreneurial Podcasts
    • Top Fantasy Sports Podcasts
    • Top Political Podcasts
    • Top Science Podcasts
    • Top Self Help Podcasts
    • Top Sports Betting Podcasts
    • Top Stocks Podcasts
  • Podcast News
  • About Us
  • Podcast Advertising
  • Contact
Not in our directory?
Add Show Here
Podcast Equipment
Center

toppodcastlogoOur TOPPODCAST Picks

  • Comedy
  • Crypto
  • Sports
  • News
  • Politics
  • True Crime
  • Business
  • Finance

Follow Us

toppodcastlogoStay Connected

    View Top 200 Chart
    Back to Rankings Page
    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.

    Advertise

    Copyright: © Kevin Knudson & Evelyn Lamb

    • Apple Podcasts
    • Google Play
    • Spotify

    Latest Episodes:
    Episode 75 - Dave Kung Mar 17, 2022
    Show notes

    Episode 75 - Dave Kung Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to my favorite theorem, a math podcast with no quiz at the end. I'm Kevin Knudson, a professor of mathematics at the University of Florida. And I'm joined by my other host. Evelyn Lamb: Hi, I’m Evelyn Lamb, a freelance math and science writer in Salt Lake City, Utah, where we just got a lovely few inches of snow last night. So I've developed a theory that podcasts cause snow here. Although it could be the other way. Maybe snow causes podcasts. KK: Maybe. EL: It’s hard to tell.KK: I don't know. It's 85 degrees for today. Sorry. EL: Yeah. I meant to say don't tell me what the weather is in Florida. KK: It’s very nice. EL: It’s too painful. KK: It’s very nice. Yes, speaking of painful, we were having our pre-banter about I had a little hand surgery yesterday, and I have this ridiculous wrap on my right hand, and it's making me kind of useless today. So I have to do anything left handed, and to control everything on the computer left handed. But you know, it'll resolve my issue on my finger. That'll be good. So anyway, today, we are pleased to welcome Dave Kung. Dave, why don't you introduce yourself? Dave Kung: Hi, there. I'm Dave Kung. I'm a mathematician by training. I spent 21 years at St. Mary's College of Maryland, and I've recently moved on from there, and I work at the Dana Center, the Charles A. Dana Center, at the University of Texas at Austin. I work with Uri Treisman down there on math ed policy. KK: It’s very cool. So you got a really serious taco upgrade. DK: Definitely. I'm still living in Maryland, but I get to visit to Texas every few months. KK: Are you gonna relocate there? Or are you just gonna stay in Maryland? DK: I’ll be here for now. Yeah. KK: I mean, it seems like the sort of work that you could do remotely, it’s policy work mostly, right? DK: A lot of policy work. There's going to be a fair amount of travel once that's more of a thing, but not all of it will be to Texas. Some of it will actually be in DC, in which case, I'm pretty close. EL: Yeah, you’re right there. KK: Yeah. Yeah. Well, they do really great work at the Dana center. I've been involved a little bit with the math pathways business, and it is really vital stuff. And of course, Uri is, like, well, he's the pied piper or something else. I don't know. But when you hear him talk about it, he's an evangelist, you really you can't help but like him. DK: Yeah. And making sure that we know that students have the right math at the right time with the right supports, we're far from that goal right now, but we can get closer. EL: Yeah, very important work for all mathematicians to care about. KK: Yeah, yeah. Okay, but I think we're going to talk a little bit higher-level than math pathways today. So we asked you on to have a favorite theorem. What is it? DK: My favorite theorem is the Banach-Tarski theorem, which is usually labeled the Banach-Tarski paradox. KK: Yes. Yeah. So what is let's hear it. Well, let's let our listeners know. DK: So the Banach-Tarski paradox says the following thing: that you can take a ball, think of a sort of a solid ball, and you can split it up into a finite number of pieces — we’ll come back to that word pieces in a bit — but you can split it up into a finite number of pieces, and then just move those pieces and end up with two balls the same size and the same shape as the original. It is incredibly paradoxical. And I remember hearing this theorem a long time ago, and it just sort of blew my mind. EL: Yeah, it was one — I think I was an undergraduate, I don't think I'd even taken, like, a real analysis class. But I heard about this and read this book, there's this book about it that I think it's called The Pea and the Sun. Because another statement of — I mean, once you can make two of the same size things out of one, you can make kind of anything out of anything. DK: Just repeat that process. That's the other statement, you take a pea and you do it enough times. And if you do it, well, you can reassemble them to form a sun. Absurd. EL: Yeah, I just, I mean, reading it, there was a lot that went over my head at the time, because of what my mathematical background was, but at first, I was like, Okay, this means that math is irrevocably broken. And then after actually reading it, it’s like, okay, it doesn't mean math is broken. So maybe, maybe you should talk a little more about why it doesn't mean math is broken. If you have that perspective. Maybe you do think math is broken. DK: It certainly feels like. I mean, I think my first reaction was: Cool. Let's do that with gold, right? We'll just take a small piece of gold and split it up and keep doing that. I think there's a lot in this theorem, right? And so I mean, it's one thing to understand it, sort of at a deep sense why it's not absurd. And I think it helps me to think about just sort of, you know, once you know a little bit about infinity and the fact that there are different sizes of infinity. And once you know that somehow the even numbers, the even integers, have the same size as all the integers, which is already sort of weird, this feels a little bit like that. I mean, you could sort of somehow take the odd integers and the even integers, and each one of those is the same size as the full integers. But some of the integers themselves, it's weird to be able to split the integers up into two things, which are the same size as itself. EL: Right. DK: And so fundamentally, this is about infinity. And the reason this is a little bit more than that, well, first of all, obviously, the ball here is not countable, right? We're not dealing with a countable number of points. This is uncountable. So now we're talking about the continuum in terms of the cardinality of the points. But I think then the surprising thing is that it works out geometrically. So it's not just about cardinality, but you can do this geometrically. And so you can actually define these sets. And, you know, the word pieces, when we say you can split it up into a number of finite number of pieces. I think the record is somewhere under 10 pieces. EL: It might be just like five or something. It’s been a while. DK: But the word pieces is doing a whole lot of work in that statement. EL: Yes. DK: And these pieces are not something you could ever do with like a knife and fork or, you know, even define easily. It requires the axiom of choice to define these pieces. KK: Uh-oh! DK: So it's really high-level mathematics, to understand how to do these pieces, but the fact that you could do these pieces, and then geometrically it works to just reassemble them, to just rotate and translate these pieces, and get back two balls the size of the origina, that’s just astonishing. KK: Yeah, that's why I've always had a problem with this. I mean, I, I can read it and understand it and go, yes, you can follow every logical step. But you're right, it doesn't work visually, if you think about it. So can you describe these pieces at all? DK: They are screwed up. So the analogy I like to make is, you know, if you're working on the interval from zero to one, you know, so first of all, the Banach Tarski paradox does not work in in one dimension. But in terms of these pieces, if you're thinking about the interval from zero to one, you could think of the rational numbers as a piece of that, right? And so it's a piece in the sense that it's part of the whole, it's not a piece in the sense that you could cut it out with a butter knife, or you could model this with a stick of butter or something like that, right? You have to hit 1/2, 1/3, 2/3, you have to hit, you know, 97/101 in there, right? All of those are rational numbers, but you can certainly think of all of the rational points in the interval from zero to one as a single piece. You can talk about them, you can define them, and then you can talk about moving them. And so you can think about it that way, right? So you have all those rational numbers, and you can think of that as one piece. And it certainly is a lot more complicated than that. You know, when you think about Banach, Tarski, one of the things I love to do is with students is to go back and think about what it would mean for a set to be non-measurable. So we can measure things like intervals, but it doesn't take too much mathematics to sort of dive into the fact that there have to be sets — if you have a sense of measure, which works on intervals and things like that, and you want it to have other properties, like when you take two disjoint sets, the measure of the two disjoint sets together should be the sum of the two measures, like really basic properties, it doesn't take too much to be able to prove that there are sets that are non measurable. And once you can prove that, like Oh, then then the world gets really screwed up. Because in the world, in our everyday living, everything seems measurable, in some sense. Like even if you have some screwed up sculpture, you could measure its volume. You dunk it in water and see how the water level rises, right? I mean, it is, it has a measure. And the idea that there is no way to measure something is just incredibly counterintuitive. But once you get that, then it's it's it's a little bit more of a leap, but to understand at a fundamental level that you can define pieces that are so screwed up that you could just rearrange them and get two copies of the original, it’s fantastic. EL: It’s interesting that you, when you first introduced this, you said it's something about infinity and I remember what — so now I'm thinking I might have taken a real analysis class before I had read this book, because I remember when I read the book, thinking, Oh, this is telling me something about non-measurability and, like, really giving me a concept of what non-measurability does to things. And that's that's how I viewed it. It's a statement about how important measurability is. DK: And just to be really clear, if these pieces were measurable, right, then we would just have some volume or something like that. And there's no way to double the volume. Right? So you can't you just can't do that. So clearly these pieces, at least some of them have to be non measurable. KK: Right? So much for your alchemy idea, right? DK: No more doing this with gold. Okay. EL: It won’t work on atoms. DK: There’s this fundamental idea that's permeates mathematics that we can continue to divide things, right? You can get things as small as you want. You see this — this is basically what calculus is all based on, infinitesimally small things. And it's just a reminder that the real world does not work like that. You take a gold atoms, you could keep splitting something up. But eventually, you’ve got one gold atom in each piece and you can't go any smaller than that. KK: Well, you could, but then DK: It wouldn't be gold anymore. KK: Right. All right. So you sort of hinted at this, but why do you love this theorem so much? DK: I love that it makes you question so many things. I mean, I love paradoxes in general, right? paradoxes are these moments when there's, there's so much cognitive conflict going on and cognitive dissonance, that it forces you, in order to resolve that cognitive dissonance, you have really have to question some other fundamental aspect of the world. So it's something that you were thinking before, is not true, or this paradox is like totally crap, right? So something like that. But in this case, the theorem is true, right? The Banach-Tarski paradox is true. And so it forces you to just go back and question some fundamental ideas about the world. And I love that there are statements like that, that can force you to go back and question so many things. I think we as humans need to do better at this. There are so many things that we just accept, as, as we take for granted, right, and we take them for granted as if they are true with a capital T. And in this case, it's about, like, you can measure all things. But of course, in our in our everyday lives, it's things about the world, it's things about people, it's things about politics, it's things about, you know, topical issues. And we grow up, and it's so normal that we think these things are just part of the world with a capital T on truth. And I love those moments that force us to go back and question those those fundamental “truths,” which all of a sudden turn out to be assumptions, some of which may not be right, or some of which we might want to reassess. KK: So maybe you're arguing that we should study more mathematics to make the world better. DK: We certainly should do that, Kevin. KK: To build a better citizenry, right? DK: Certainly if we all understood mathematics better, we would have been better off during this pandemic. That's certainly true. EL: I have a question. So as you mentioned, this is called a paradox often. Do you think it is a paradox? DK: Yeah, that's a great question. You know, paradox has a couple of different meanings. One is sort of this deep philosophical meaning, like, is it really something which is somehow both true and false simultaneously? And, and this is not in that sense, a paradox. It is a paradox in a sort of weaker sort of more everyday sense of that word where it really throws us into into some cognitive dissonance that forces us to question other things. We can't hold both true that like everything can be measurable and things have volume and you can take a ball, split it into six pieces, rearrange them and get two balls. Those are fundamentally in conflict, and one of them has to go. KK: It does rely on choice, though, right? So there's something to argue about there. You know, there are those people who deny the axiom of choice. DK: They're few and far between in the math community, but they're out there. Yeah, they're not measure zero, so to speak. So yeah, so it does, it does use the axiom of choice. So this idea that you can, you know, you can make infinitely many choices. And you can see in the proof where you have to do that. So you end up with uncountably many sets and you choose one point from each one of those uncountably many sets, and that's part of the way you get one of the sets that creates the the Banach-Tarski paradox. KK: Yeah. EL: I guess I'm a little surprised that the Banach-Tarski paradox hasn't made more people reject the axiom of choice, to say like, Okay, well, clearly, you can't do this. So, therefore, what is this relying on? Well, it's relying on the axiom of choice. KK: Well, there's a lot of things about like that, like the Tychonoff product theorem. So that's equivalent to the axiom of choice. So do you want your product of compact spaces to be compact or not, you know, I mean, I don't know. DK: Yes, I think Evelyn, you're hitting upon this fundamental thing: at a very deep level, math gets weird. Yeah. Right. And you can have things. I mean, you see that in, you know, in Gödel’s work, you see like, well, is that true? Well, you know, it can either be true or false. What's your pleasure today? Right? To the axioms or we can reject it. And, and fundamentally, it kind of doesn't matter. But you know, go ahead like, is there an infinity between the integers and the real line? Like, oh, you know, take your Take your pick? EL: Yeah, what seems more more useful to you right now? Or what sounds like more fun to play with? KK: Sure. DK: And in some sense, that that is kind of the beau…

    Full show notes at the publisher

    Episode 74 - Priyam Patel Feb 11, 2022
    Show notes

    Episode 74 - Priyam Patel Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello, and welcome to My Favorite Theorem, the math podcast with no quiz at the end. I'm your host 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. I almost forgot my name there for a second. EL: It happens. KK: I realized, like, I was hesitating, and I was like, “Who am I again?” Yeah, you know — so our listeners don't know, but it's 5:30 where I am, which, you know, doesn't sound late. But I've been at work all day, and now I'm tired. EL: Yeah. Well, you should have made up something. You know, just tried on a different name for fun just to see. KK: Well, yeah, so even my parents had the deal that if I was a boy, my dad got to name me. So he went with Kevin Patrick. And if I was a girl, my mother was going to get to name me. And should I tell you what I would have been? EL: Yeah. KK: Kandi. Kay Knudson. EL: Yikes! KK: Now, I'll let you work out why that would have been terrible for lots of reasons. Already, there are multiple axes along which that is terrible. EL: Great. Yeah, well, my name if I had been a boy ended up with my younger brother. So it was kind of not that interesting. I mean, if you knew my family, you would be like, Okay, well, that's boring. Anyway, yeah. We are very happy today to have Priyam Patel on the show. So yeah, Priyam, could you introduce yourself a little bit? Priyam Patel: Sure. So my name is Priyam. I am an assistant professor at the University of Utah, and I have been here for three years. Before that I was around everywhere, it feels like, for my postdoc. I was at UCSB for a few years, before that at Purdue for a few years, And I did my PhD at Rutgers, which now feels like ages ago. EL: Yeah, you’ve been in, like, every region of the country, though, I guess not central timezone, because Indiana is right on the west edge of Eastern. KK: That’s right. PP: Yeah. So I was never in the Central time zone. And that's why — in the summer in Indiana, the sun sets at, like, 10:30pm. It's really bizarre. KK: You could call that Central Daylight if you wanted to, right? PP: Yeah. Something like that. EL: Yeah. And as you mentioned, you've been at Utah for about three years. And you you first got here in fall 2019, and I was gone for most of the fall 2019. And then of course, we all know what happened in 2020. So part of the reason I wanted to invite you is because I feel like I should know you better because you've lived here for three years. But, like, with the weirdness of the past three years, I feel like I haven't gotten to talk with you that much. And so of course, obviously the best way to do this is, like, on a podcast that we want to just broadcast to the entire world. PP: Yeah, perfect. So no private conversation over drinks. Just put me on the podcast. EL: Yeah. Excellent. So So yes, I'm excited to get to chat with you. And yeah, hopefully we can do this over drinks in a real venue at some point. KK: Wait a minute, what happened in 2020? EL: I tried to block it out. PP: Nothing at all. EL: For some parts of it, really nothing.PP: It feels like a whole blur since then. So KK: I’m not convinced it isn’t still 2020 somehow. PP: Yeah, yeah. KK: Alright. Anyway, I'm being weird today, and I apologize. So let’s get to math. So Priyam, you have a favorite theorem. Which is it? PP: Yeah. So I chose the Brouwer fixed point theorem, which I learned has been done twice already on this podcast. EL: Yes, I'm very excited to hear more about it because in our emails, you mentioned some aspects of that I wasn't aware of. And so this is very exciting. And this is when people, when we email with people, they’re always like, “well has this been used?” And we're like, “It doesn't matter if it has, you can use it anyway.” We like to talk about theorems because it is interesting, just the different relationships people have with the same math. So for anyone who hasn't been you know, avidly listening and taking notes on every single episode we've done since 2017, can you tell us what the Brouwer fixed point theorem is? PP: Yeah, so I'm just going to state it for the closed disk because that's the only context that I'm going to talk about it in. But basically, if you take in the plane in our two if you take the closed unit disk, then the theorem says that every continuous map from the disk to itself necessarily has a fixed point. So should I go into detail about what a continuous map? Would that help? EL: Yeah. Or at least intuitively. KK: Sure. PP: So I actually did listen to a lot of the previous podcast episodes while I was preparing. And I like this idea of if you take the unit desk, and you, like, kind of shake it around a little bit, and everything kind of moves in a nice smooth fashion where things don't get sent, like, really far away — so if in a little neighborhood, you’re wiggling, one point is not just going to pop out and end up somewhere else, right? I like that idea of continuity. So if you're wiggling around the disk, the unit disk, and you use any continuous map, somehow one of the points has to stay fixed, so it gets sent to itself. And that's kind of surprising. It feels like if you just move things around enough, something, everything, should get moved off of itself. But in fact, that can't happen. So that's kind of my interpretation of Brouwer’s fixed point theorem. EL: Yeah. And it's like I guess I always imagine it made of rubber or something. Because you are allowed to, like, stretch and smush a little bit. It doesn’t — because otherwise, you might think, Oh, the only thing you can do is rotate it. So of course, that central point will be fixed. But you could do a lot of other things. PP: Yeah, absolutely. EL: And fix some different point. PP: Yeah, so I think Evelyn has a great point, like, you can spread things out, like you're making it out of like stretchy fabric or material, you can spread things out in one part of the circle, in the unit disk, and then, you know, string things together in another part and that's okay. It's like, you know, just kind of smoothly moving around is the way I think about it. KK: Yeah, yeah. But something stays put. PP: Something stays put, which is kind of strange sometimes, actually. And there's like, so many proofs of this theorem, I feel like, and so many different perspectives for proving it. But I do have a favorite proof of that, actually. KK: Okay, good. Let’s hear it. PP: So it's unfair, because it uses some algebraic topology. So o be able to get to this point in this in your math life, where you're like, Yeah, this is the proof I like the best, you have to learn some algebraic topology. But essentially, the idea is that when you're in topology, in the field of topology, you're trying to understand when two objects that are made out of bendy, squishable material that you can stretch and shrink, when two of those are really the same. So if you have, let's say, a circle, or a really oblong wiggly circle, those two are the same. It doesn't really matter if one is really beautiful and perfectly symmetric. It's really the same space in topology. So two things that are not the same topologically are the closed unit disk, and just the outer boundary, which is just a circle. Okay, so there's an a thing called an algebraic invariant that you can compute called the fundamental group, that tells you that topologically, formally, these two spaces really aren't the same. And essentially, there's a proof that says, If there wasn't a fixed point, then you could basically take the entire closed unit disc, and shrink every point in the desk to the boundary. This is called a retract. You’re basically saying like, I'm going to retract the entire closed unit disc to just the circle. And retracts are supposed to give you the same fundamental group. And you already know that those two things aren't the same. And so that's my favorite version of this group. And I can slow down on any part of that if you'd like more details. EL: Yeah, that's really nice. Well, I think maybe a good way to see this is like, you know, that example of turning the circle around, you know, like a record spinning on a record player or something. Like if you took away that central point, everything else can move. And you can also imagine pulling that rubber all the way to the edge, making it into a bike tire or something else like that. (Which is actually topologically different.) PP: Right, but as soon as you puncture it. So Evelyn's basically saying, let's just take out the center point. But what corresponds to the origin in R2? Well actually, once you do that, there's no contradiction that you derive, right? You can have every point moving. And in fact, that punctured disk and the circle are the same topologically. That retract that you can use to just pull everything to the boundary shows you, actually, that they're the same topologically. So it's just that one— it’s amazing how much like one point can make such a huge difference, right? EL: Yeah. PP: Adding in that one point. But yeah, so that's my favorite proof. It's fancy in some ways, but once you know the basic material that leads up to it, it's like a three-line proof, right? Which is kind of incredible. EL: Yeah, but it's maybe a little bit like, what is the phrase, like using a sledgehammer to kill a mosquito. PP: Oh yeah. EL: Once you’ve built all of this fundamental group, then sure, you could just whack that. PP: Yeah. And it's so funny because in math, typically I am the opposite of a hammer-striker, right? I never use the hammer. I want to understand the nitty-gritty of why you can just explain this using elementary math or something like that, right? But for some reason, when I saw this proof in, like, Hatcher’s algebraic topology book, I was like, Oh my gosh, that just like makes perfect sense to me, like I totally get why now. So it definitely is one of those use a hammer use a sledgehammer to kill a mosquito type of approaches. KK: I’m actually teaching algebraic topology this year, and so I that is the proof I use for the Bouwer fixed-point theorem. But I use the sledgehammer to prove that every polynomial of odd degree with real coefficients has a root. And I use to use the Lefschetz fixed point theorem to do it. Let’s use the biggest sledgehammer we can find! PP: It’s awesome. I mean, honestly, if you want for this podcast, if you want to talk about a fun theorem that's called theorem at the end, right? Not just some result, but that's actually named, a lot of the ones you come up with in topology are the fixed-point theorems, right? And I was like, Oh, this one's actually my favorite. And there's a reason for it. So yeah, that makes sense. EL: So what else do you love about this theorem? PP: Yeah. So this actually was inspired from talking to a few grad students the other day, but I realized that, you know, I gave them this task, which says, Can you classify all of the isometries of hyperbolic 2-space. Now, that's already a fancy sentence to say. So I can break down all of what that means. But in fact, one of the key ingredients for the approach that is my favorite to solving that problem is to use the Brouwer fixed-point theorem. So I can start off by talking about what hyperbolic space is, and like what metric spaces are. And from there, I can explain what an isometry. It’s kind of similar to a continuous map, but it has a lot more structure and preserves a lot more structure. So let's start off with just hyperbolic space, shall we? Okay, so the way I think about anything that is a non Euclidean geometry, which hyperbolic geometry is one of those, I have to start thinking about, well, Euclidean geometry first, right? And Euclidean geometry, when I think of that, I think axioms, right? There's Euclid axioms and they're written down. You don't need to know what they are. But the last one is the one that people started saying, let's try to break it and see what happens to these models of geometry that we're sort of studying, right? Like, could we come up with a different interpretation than just Euclidean space. And so if you break the parallel postulate, there's a few different types of geometries you can get that satisfy all the other ones, but they don't satisfy the parallel postulate. And hyperbolic geometry is one of them. So what is a geometry, right? It's a space, like we just talked about, let's say the closed unit disk. And to me geometry, you're studying rigid things like distances, angles. And so you want to have a notion of measuring distance on whatever space you choose. So since we're going to talk about the power fixed point theorem, of course, my space is going to be the closed unit disk. In fact, I'll just start off with the open unit disk for now. So let's just get rid of the boundary. So if I start off with the open unit disk, that is my space. And there is a way of measuring distance on that space. So you can say, oh, put in these two points, I want to know the distance between them. There's a way of measuring distance on it where if you want to go from the center point at the origin, out to one of the boundary points, let's say just (0,1), or (1,0) in the plane, it actually takes you an infinite amount of distance to get there. Okay, so in hyperbolic space, this model of hyperbolic space, which is called the Poincaré disk model, the boundary of the disk is sort of off at infinity. And as you get close to that sort of boundary at infinity, points are getting really, really, really far away. That's what it means to get, you know, closer and closer to infinity, is distances go really big. And so that's the idea of what the Poincaré disk model of hyperbolic to space is. And, of course, if it weren't a podcast, I'd be showing like tons of pictures right now. EL: Yeah, it is quite attractive. It's just a lovely, appealing model. PP: Yeah. And like, you can look up all these amazing pictures by Escher. There are these famous paintings where Escher uses the upper half plane model or the disc model, and shows how, like, a bat, or whatever the figure is that he uses to tessellate, a bat of the same area drawn in different parts of the hyperbolic plane can look to our eyes, very different, right? And that's again, coming back to this notion of as you move out towards the boundary of the unit disk, distances are getting really big so the bat would have to look really small to your eye to to have the same area as a bat in the center of the disk. So I highly encourage listeners to Google just hyperbolic space or Escher's hyperbolic paintings, right. And you'll come up with so many things. EL: Yeah, well, fun fact is that my Twitter profile picture is a tiling of the hyperbolic plane with the Poincaré. with like, a picture of me in it. PP: Yeah. So I love it. I know Evelyn really loves all of these, like, hyperbolic geometry, topology type thing. So yeah, that's also partly why I chose this topic. EL: Yeah, you’re definitely speaking my language. PP: Okay, so that's the idea of what hyperbolic space is. There's so many more things you need to do to sort of gain the intuition of what it feels like to live in hyperbolic space, right? And those are the kinds of things that you build over years of studying it in your life. But the real thing I want to talk about is isometries of the hyperbolic disk model. So w…

    Full show notes at the publisher

    Episode 73 - Courtney Gibbons Jan 13, 2022
    Show notes

    Episode 73 - Courtney Gibbons Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a math podcast with no quiz at the end. I'm Kevin Knudson, professor of mathematics at the University of Florida, and I'm joined today by my fabulous co-host. Evelyn Lamb: Hi, I'm Evelyn Lamb, a freelance math and science writer in Salt Lake City where we are preparing for another snowstorm this week after we had one last week, which is great because we are so low on water right now and we need every bit of precipitation. So even though I'm from Texas, and I don't naturally love shoveling snow or being below 50 degrees, I am thrilled that we're supposed to get snow tonight. KK: So when I lived in Michigan — you know, I grew up in North Carolina, so snow was a thing, but we didn't shovel it. We just sort of lived with it — and I had a neighbor across the street who was in his 70s. And he had a snowblower and he let me use it and I thought this is amazing. So if you and John haven't invested in a snowblower yet, you know, maybe it's time. EL: But we'll see. Climate change means that we might have to do less and less snow shoveling. KK: Well, it's true. Actually, I remember growing up, you know skiing was a thing in North Carolina and I think you might still be able to but like the natural snow ski resorts, kind of they have to manufacture all their snow now. It's, it's things have changed even in my lifetime, but it's not real. As we're told, it's not real. Sorry to editorialize. Anyway, let's talk math. Today, we are pleased to welcome Courtney Gibbons. Why don't you introduce yourself? Courtney Gibbons: Hi, I'm Courtney Gibbons. I will see far more snow up here in Clinton, New York, then either of you, I think. EL: Definitely. CG: I just sent in my plowing contract for the year. So that's awesome. Make sure that I don't have to shovel or snowblow my own driveway, which is not long. I'm a professor. I'm an associate professor of mathematics at Hamilton College up here in beautiful Clinton, New York. There is a Hamilton, New York, but that's where Colgate is. So don't get them confused. KK: Oh, right. CG: Yeah. It’s weird. It's a strange thing. EL: Yeah. At least it’s not the whole Indiana University of Pennsylvania thing because that is not okay. CG: Yeah, no, no. I think there was an incident on one of our campuses where, like, Albany sent some kind of emergency response squad to the wrong Hamilton. But they're only 20 minutes away, so it was a quick thing. Yeah. KK: All right. Well, welcome. So, I mean, well, maybe we just get into it. EL: Well, I will say that we have talked to Courtney before on the podcast, although extremely briefly, when we, I guess this must have been the joint meetings that 2019 a decade ago. (Hahaha.) KK: Yeah. EL: We had people give us, you know, little, like, minute or two sound bites of their favorite theorems. And she did talk about a theorem, although I understand it's not the theorem that she's going to talk about today, which, I mean, you don't have one and only theorem in your life? Come on, Courtney. CG: I am a lover of many theorems. I think back then I mentioned Hilbert’s Nullstellensatz, which is the beautiful zero point theorem that links roots of polynomials to factors of polynomials over algebraically closed fields. It's beautiful. It's a really nice theorem. I initially thought I was going to talk about Hilbert’s syzygy theorem today, which by the way, syzygy, excellent hangman word. EL: Yeah.CG: Unless you're playing with people. You've used that word on before, in which case, their first guess will be Y. But I decided today I wanted to talk about Emmy Noether’s isomorphism theorems, in part because they're usually just called the isomorphism theorems. And Emmy Noether’s attribution gets lost somehow. So I wanted to talk about those today because I'm a huge Noether fan. I mean, I'm also a huge Hilbert fan. You kind of have to be a big fan of both. But these theorems are super cool. They're theorems you could see in your first course in abstract algebra, and that's actually where I first saw them. I'm a commutative algebraist and I do a lot of homological algebra. So I love arrows. I love kernels. I love cokernels. I love images. I love anything you can set up in an exact sequence, and I think this was my first exposure to a theorem that was best explained with a diagram. And I remember at that moment being like, “This is what I want to do! I want to draw these arrows.” And I'm lucky because I got to grow up to do what I want to do. EL: That’s kind of funny because I loved abstract algebra when I took it in undergrad, and I think as it got more to, like, you know, having all these kernels and cokernels and arrows, that was when I was like, “I just can't do this,” and ended up more in geometry and topology. So, you know, different, different things for different people. That's fine. So yeah, let's get into it. So what are these theorems? CG: Excellent, well, they are often numbered. I grabbed a couple books off my shelf, and it wasn't consistent, but Rotman and Dummit and Foote, kind of numbered them the same way. So the first one, which is usually the first one that you see, it's true for rings and groups and modules. I most often use it for modules, but I'll state it for groups. And it says that if you've got a homomorphism F from a group G to a group H, then the kernel of that homomorphism is a normal subgroup of your group G. Or if you're working with rings, it's an ideal of your ring, or you know, a submodule of your module. And you can mod out by it. So you take G mod the kernel, and it's going to be isomorphic to the image of your homomorphism. And so if you've got a surjection from G to H, and you are like, “I kind of want to build something isomorphic to H, but built out of the parts of G,” you're like, “Cool, I can just take the kernel and mod out by that and look at the group of cosets of of that normal subgroup.” And you've got this — you're done! You've built this cool isomorphism. And I advertise it to my students as, like, a work-saving thing. Because usually to build an isomorphism, you've got to show one-to-one/injective and onto/surjective. And this is like, well, take the thing you want to be isomorphic to, try to imagine it as a better group, a nicer group, mod the kernel of something, and build that homomorphism. Make it surjective and then you get one-to-one for free from this theorem. So I love this theorem. It's a really, it's a nice theorem. I actually use it. I don't reference it, but I think when you do the first step of finding a free resolution, which is which is what I do, it’s like my bread and butter, I love doing this. If you're calculating it by hand, you take a module, and you surject onto it with a free module. You look at the kernel of that thing, and then you build a map whose image is that kernel. And the big deal here is that your module M is isomorphic to the cokernel of that image map, which is the same thing as what you get from the isomorphism theorem. It's that first free module mod the kernel. So this gives you a nice presentation for a module. You can do this in certain nice cases. And I always sort of give a little thanks to Emmy when I start building free resolutions. I'm like, “I know that this is your theorem in disguise.” EL: Yeah. KK: I don’t think I actually knew that attribution, that Emmy Noether was the first person to explicitly notice these. But you know, she's the one who figured out that, you know, homology is a group, right? CG: Yeah, exactly. So she was thinking about rings and groups. And, you know, a lot of the terminology is thanks to her and Hilbert. When you think about integral domains, I think that was what Hilbert initially called rings. I dug this up at one point for my students, they're like, where don't come from? EL: That makes sense, right? Because I think of a ring as something that's like the integers. CG: Yeah, it is really. Yeah. And like when people started generalizing to super bonkers weird examples, like, you know, the ring of quaternions and stuff, you're like, okay, so everything isn't quite like the integers. But we've got these integral domains, integral being the “like the integers” adjective and domain, I think of like, where stuff lives. The stuff that's like the integers lives here. EL: Yes. CG: Which is nice. But yeah, these these are, these are attributed to me. And it really bugs me to see them without her name attached since we have Hilbert’s syzygy theorem and Hilbert’s Nullstellensatz and it's like, but what about Emmy? KK: Yeah, she she, she doesn't get the recognition she deserves. I mean, everybody knows like, she's like a mathematics mathematician. But yeah, she she definitely doesn't get credit a lot of the times. EL: Yeah, I will say I am so, so tired of the headline, “The most important mathematician you've never heard of,” and I read this, and I know that I know more mathematicians than your average person. But like, if it's Emmy Noether, I’m just like, come on. You can't say you’ve never heard of her. KK: Even the physicists. EL: A lot of people haven’t heard of her. But yeah. CG: But it's also interesting the way she's talked about. Because at the time, of course, it was difficult to be a woman in math even though she somehow was able to be a professor, although unpaid. But you know, you look at the way people described her and there's that one guy, I forget, who was like, “I can testify that she was a mathematician, but I can't testify to the fact that she was a woman.” You know. And it's like she was accepted because she was so unfeminine. And it was not threatening to the status quo of men doing math to have this, in their words, coarse, rough, simple soul wearing men’s shoes, blah, blah, blah, great heft, among them, because it's like, well, she's basically a man. So that always bothered me, too. I'm not a particularly feminine person, but occasionally, I do feel like I get a little bit of a brush off because they're like, “Oh, you're so cute.” I'm not cute! I'm a big strong mathematician! EL: Yeah. CG: That’s okay. I'm aging and rapidly getting less cute. EL: You’ll become invisible soon. CG: Yeah, I know. I'm really I'm excited for that. Yeah, I think I'll enjoy that better than the like, “Oh, aren't you a cute little mathematician?” EL: Yeah. All right. So… KK: So there's a first isomorphism theorem. So there must be a second. CG: There’s a second, there's a third, there's a fourth, there's probably a fifth, then at some point, you just start calling them nth. And I mean, I think I've only seen four. I'm only going to talk about the first three, because I actually don't really know what the fourth one says. There's no quiz at the end. But there's homework. The fourth, look up the fourth. Okay, so the second isomorphism theorem is kind of a special case of the first one. You can prove it using the first one. It says that if you've got a group G, and you've got a normal subgroup, and you've got — we’ll call it N for normal — and you have another subgroup doesn't have to be normal, so we'll just call it S for subgroup, or Su or whatever. This says a couple of things. It says that the product of S and N is a group (and you form the product the way you would expect; you just multiply pairwise elements together, things from S with things from N). The intersection S intersect N is a normal subgroup of S, which is a really nice exercise. And you've got an isomorphism between the product SN mod N, and the quotient group S mod S intersect N. So this is a way of sort of saying like you've got this one group, maybe you don't understand SN really well, and you're working with SN mod N. Instead, replace it with a group you might understand better, which was your original group S mod S intersect N, which is a nice, normal subgroup. So it gives you some opportunity to work with a nicer group to understand the one you don't know. And the proof of this is you build a surjection from S to SN mod N: you’re going to just sort of send S to its coset representative over there and argue that it's onto. And then you calculate that the kernel is S intersect N and then you're like, boom, first isomorphism theorem, take it away. Which is, which is really cool. KK: It’s kind of a corollary. CG: Yeah, it's kind of a corollary, but the diagram is super cool. It looks like a diamond because you've got S and S intersect N and then SN and and then you've got all these things all over, the arrows. I love the arrows! And you can put the group G at the top and you put the identity at the bottom, and then you've got like a nice lattice. So I think it even is sometimes maybe called the lattice theorem. But that may. Yeah, I think so. But I, I wouldn't bet your final exam on that if you need to say something. EL: Yeah. Well, and lattices are a different thing also. So I would be confused if I called it the lattice theorem. I’d think it was about like lattices in the plane or something. CG: Right. Yeah. I think the idea is it looks like like a lattice. And most algebraists are like, “Oh, cool. We can call that a lattice.” KK: It is a lattice though, right? The set of subgroups is a lattice. I mean, it’s a partially ordered set with meets and joins and blah, blah, blah. CG: It is. Yeah, and what's funny is that the person I learned abstract algebra from in undergrad studied lattice-ordered stuff. I forget exactly. It was algebraic stuff. Now I'm of course blanking on it. That was Marlow Anderson, and I am the retirement replacement for Bob Redfield at Hamilton, who studied lattice-ordered subgroups. So I inhabit this space where lattices surround me. It’s kind of exciting. KK: Yeah. But you're right. This one's a little more obscure. I can't remember ever using this in life except for, like doing homework? CG: Yeah, I don't recognize a place in my life where I've used this one. If I have I was ignorant of the fact that that was what was making things work, which is not unusual. This is why I need to have collaborators. So they can be like, “Ah, Gibbons.” Hope only gets you so far. KK: Right? All right. All right, number three. CG: So number three, I think this one is low-key my favorite even though again, this is not one — actually no, I did use this. We used this in my modern algebra class this semester. This says let's say you've got a group G, and you've got a normal subgroup K in G. And you've got a normal subgroup N in K, so you're normal all the way up. This says that if you take G mod N mod K mod N, which kind of looks like — if you think of it as fractions, like if you're taking 1/2 then dividing by 3/2, this basically says you can “cancel” the denominators, and you're going to get G mod K. Which is really nice, because if you think about what G mod N mod K mod N is, G mod N is a group of cosets and K mod N is a subgroup of cosets. And now you're making cosets out of cosets. And that's a level of abstraction that you don't really want to work with directly, and so having this isomorphism that's like, that’s the same thing as G mod K, just work over there, that’s it's a really powerful thing. And in my class, we're doing a bunch of Galois stuff. Like, our first cour…

    Full show notes at the publisher

    Episode 72 - Kameryn Williams Dec 10, 2021
    Show notes

    Episode 72 - Kameryn Williams Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a math podcast with no quiz at the end. 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, a freelance math and science writer in Salt Lake City, Utah, which is very chilly, and I'm trying to warm up from my bike ride just now that I probably should have worn better gloves on. The downhill! KK: Yeah, yeah, that sweater you're wearing those should help this. Yes. EL: Yes, it is. I'm showing all of our listeners that this is a wonderfully rainbow chunky sweater that I recently inherited from my grandmother. Probably purchased in 1973 or something. KK: Yeah. Well, so today's actually, it's November 11. It’s Veterans Day and the University of Florida is closed and so I've done wonderful things, like I went to brunch with my wife this morning. We sat outside. It's not cold here, of course. We sat outside, and then I fixed her, the light in her closet, it was one of those pull cord deals, you know, the core just broke. I mean, inside the lamp, so I had to actually go to Ace Hardware, which I highly recommend. Your local Ace is much better than the big box places and and get the stuff to fix it. So that's been my day. EL: Thanks to our sponsors, Ace Hardware. No. They care so much about math podcasts, I’m sure. KK: I’m sure. All right. Well, today we are pleased to welcome Kameryn Williams. Kameryn, why don’t you introduce yourself? Kameryn Williams: Hi, I’m Kameryn Williams. I'm a mathematician at Sam Houston State University about an hour north of Houston, Texas. It's good to be on. EL: Yeah, and I bet it's nice and warm there where you are. KW: It’s in the 70s today, you might think that's warm, being in Salt Lake. EL: Yeah, I went to grad school in Houston. So I do — November, February, March. Great months there. KW: Oh, yeah, definitely. Well, like I have family in Idaho, and they've been complaining they got snow recently. And I'm like, come on, it’s not that bad. Quit complaining. EL: Did you grow up in a warm place? Or? KW: I spent some time in California growing up but moved around a fair bet. EL: Okay. Yeah. KK: All right. Well, so what sort of math are you interested in? KW: I’m a logician, mostly specializing in set theory. KK: Oh, okay. Well, that’s, that’s — EL: I guess that’s okay. KW: You’ve had a few logicians on, I’ve seen. KK: We have. EL: We just had your advisor on a couple of months ago. KW: That’s true, Joel [David Hamkins] was on. So he took my favorite theorem, so I had to come up with a new one. EL: So rude. KK: Oh, well, okay. Well, that's the perfect segue. So what is it? What is your what do you want to talk about today? KW: Okay. Yeah, so I guess maybe I should hedge? It's hard for me to pick favorites. It's like your children. They're all my favorite. KK: Sure. Sure. KW: But definitely among my favorites, one of them is Gödel’s condensation lemma. KK: Okay, so I don't know this one. I mean, everybody knows his famous incompleteness theorems, right, but — KW: Yeah, so this comes, I don't know, what, about 10 years after his incompleteness theorems. Okay, so maybe I should step back a bit and not talk about Gödel for a second and kind of set the stage for the theorem. KK: Okay, that would be great. EL: That would be fantastic. KW: By itself, you kind of need to know why it's there to understand why anyone would love it. So set theory, like the study of well-foundedness, we kind of care about these transfinite iterative constructions. So here's a good example: Let’s generate all the sets. You can start with whatever non-sets you have — numbers, whatever — and then you just look at sets of those, sets of sets of those, sets of sets of sets of those, and so on transfinitely. So you just iterate the power set operator, along the ordinals transfinitely out to infinity, you get all the sets that way. Okay, so it looks like if we want to do this, we need all these sorts really large, weird infinite objects going on. But 1922, Thoralf Skolem made an interesting theorem now known as Skolem’s paradox — whether you think it's a paradox is on you — and he noticed that you could just have a single small countable collection of things that had all the properties what it looks like you needed all these really large sets for. So there's this small little countable thing, it’s got what it thinks are all the real numbers. You know, externally you can see it's countable, but internally, it doesn't know that. It thinks there's uncountably many real numbers. You can do everything with that. EL: Whoa. Sorry, so you can tell I've not done much with this kind of stuff here, so you’re blowing my mind here. KK: This is good because that’s kind of how it was when I first learned this. Like, if you would have brought me on as an undergrad, I would have said downward Löwenheim-Skolem, that’s my favorite theorem. Given any structure, you can find a countable elementary substructure, a countable thing that has all the same sort of intrinsic properties. EL: Okay. KK: Okay, so from the outside the reals look countable, but inside they don’t? KW: Yeah, well, so what does it mean that the reals are— EL: That seems way too powerful. KW: What does it mean that something’s countable? It means there exists a bijection with the natural numbers. Well, if you don't have all the objects, you just have a small piece, you might be missing that bijection. KK: Okay. KW: So you might think it's uncountable, but it's just because your model was too small to see all the bijections. KK: Okay. KW: Okay, so Skolem, he called it a paradox. He thought this, you know, revealed all the set theory people were doing was nonsense. Like come on, you can’t even nail down what it means for something to be uncountable. And I mean, the other people kind of had your reaction, you know, it’s blowing my mind. That’s kind of where I fell. But it was really not treated as anything more than a curiosity. So maybe you thought it was a paradox, maybe you thought it was curiosity, but no one really had any use for it beyond that. And then, late 1930s, about 15 to 20 years after Skolem did this, Gödel was interested, you know, he was fresh off the incompleteness theorems. Everyone loves those. He wants to go onto something a bit more difficult. And so he's concerned about Hilbert’s first problem. Can you prove Cantor's continuum hypothesis? Can you prove that there's no cardinality intermediate between the natural numbers and the real numbers? KK: Right. EL: Right. KW: Okay. Skolem says, you know, maybe it's hard to say what that means, but let's set that aside. Okay, so how is he going to do this is he wants to do something like this construction I mentioned, iterate the power set, but you want to have a bit more control. The problem is if you take the power of sort of the natural numbers, that's going to contain basically every countable object, and how do you get any sort of handle and what this says? So Gödel’s idea was to restrict this to as small as possible to just the intrinsic things. So rather than taking all the sets of natural numbers, you just want to take the ones that you can define just by quantifying over natural numbers. So you'd have, like, the set of prime numbers, because you can say what it means for something to be prime, you can have the set of even numbers, you can say that, but you wouldn't have a weird thing, for example, like these, you know, weird countable models of set theory, you can't really define that just by quantifying over natural numbers, okay, or isomorphic copies of them, however you want to say it. KK: Okay. KW: So he iterates this process where you just take the definable sets. And then if you iterate that transfinitely instead of the full power set operator, you get some other hierarchy, some other transfinite hierarchy of construction. So this is called the L hierarchy. I looked this up because I didn't know: L stands for law. Not entirely sure why he chose law. EL: Like, rules, that kind of law? KW: Yeah, I'm not entirely sure what he thought with it, but that's what it was. Okay. So just like with this, like, iterating the power set hierarchy you can do like a sort of Skolem collapse there. You can do the same thing with Gödel’s L hierarchy. So you can build it up to a certain stage. And then you can say, okay, I built it up to some uncountable stage. I'm going to take just a countable piece that looks like this. It's got all the same elementary, or to use a bit of jargon, first-order properties. KK: Okay. KW: Okay, so Gödel’s condensation lemma just says that if you do this Skolem collapse process, take this elementary sub-model, it's just going to be isomorphic to an initial segment of the construction. So maybe you built up to stage alpha, you take this countable piece, it's like building up to stage beta, some stage beta below of alpha. KK: Okay. KW: Okay. And looking at your faces, it's not clear why this is interesting. EL: Right. Well, I do have a question that — sometimes it's really fun to be very naive and ask ridiculous questions. So like, okay, if we're trying to do this process for the real numbers, say, and find this countable thing underneath it, like, is there a canonical way to say, okay, these are the real numbers that are in this countable piece of it. Like, can I say is pi in here is, is 1/4 in here? Could I ask you that? And you could say yes or no. KW: I can say yes to both pi and 1/4. Other numbers might be harder to say. But those are easy. Yeah. EL: All right. KW: This is the point, is you have a very firm control over what's going on. This is kind of the opposite of taking the power set construction where, what is the power set of the natural numbers? That's kind of the vague question that the continuum hypothesis is trying to get at. This is we're going to get at it very firmly. But this is interesting because Skolem’s paradox gives us this condensation lemma when you apply it in Gödel’s constructible universe. So this paradox becomes a theorem. And this theorem then is kind of the key lemma to prove the continuum hypothesis for L. So you want to now prove that you can't have too many reals. Well, any real you have, it shows up at some countable stage. So if every real shows up at a countable stage, there's aleph-one many countable stages. Aleph-one is the smallest uncountable cardinal. That means there's aleph-one many reals, the smallest uncountable cardinal, nothing in between. EL: I feel like you're pulling one over on us. KK: I don't think that's a proof of the continuum hypothesis because, you know — KW: It’s a proof of the continuum hypothesis in Gödel’s model he built. KK: Okay. KW: So this is how he showed, then, that it's, you can't disprove it from this. EL: Right. It's not a contradiction. KW: Yeah. So it's consistently true because it's true here. Now, this isn't necessarily true in the full universe of sets. You might miss out on a lot of things in Gödel’s construction, and then later Paul Cohen showed, okay, you can't prove it for the full universe of sets. KK: Right. KW: But yes, this is just one step, this thing that was a paradox became a key lemma in order to kind of do half of Hilbert first problem. And I think it's kind of interesting, something that’s first is introduced, maybe it's a paradox, maybe it's a cute little curiosity, and then Gödel, you know, 15, 20 years later or so, realizes, oh, no, this is actually — a version of this is a really important step in this proof. KK: Hm. Okay, this is completely foreign to me. EL: Yeah. Well, I'm a little confused on the timeline here. Had Gödel already done the incompleteness theorems, but then this is something else that he did later? KW: This is after. Yeah, yes. The stuff with L, this is about 1940. Incompleteness theorems are about 1930. So it's about 10 years later. EL: Okay. But he kind of figured out that this work fits in with the incompleteness theorems. KW: Well, I mean, the incompleteness theorems are one of the first forays into a larger program. Like, okay, you know, they established, you can't write down a computable list of axioms that decides all the things you want. Well, can you then find natural questions that you can decide? Well, here's one that was a candidate: the continuum hypothesis. So it's easy to say, okay, here's these weird diagonalization tricks to get something. How do you show that a “natural statement” is undecidable? That takes more work. It’s a more difficult problem. KK: Okay. EL: Yeah. And so we — I emailed you to be on the show, because I saw a tweet you had done about how maybe you felt it was a little unfair that Gödel is so known for the incompleteness theorems, whereas you feel like this work is actually more impressive, and people should be more, I guess, should like it more than the incompleteness theorems. KW: Ah, that was maybe a bit of a hot take. But I think it's it's uncontroversially mathematically more impressive. The proof of the incompleteness theorems is basically he noticed the same sort of similar sort of weird thing people had kind of dismissed, and he realized this could be important, this diagonalization trick. If you can somehow code proofs and formulas into number theory, they can refer to themselves and you get this self-reference. So, you know, cute little curiosity, and he takes this cute little curiosity and he turns into a theorem. So he did the same thing here, but it was a much more difficult theorem, much harder to do. And not to pooh-pooh the incompleteness theorems, but if you take a senior undergraduate-level logic class, you'll prove the incompleteness theorems by the end. They’re not that difficult of results. KK: Well, that's probably why they get more love, right? KW: Yeah. I mean, that's a big part of it. And so that's maybe where a bit of a hot take came from. KK: Right, right. Yeah, this is all very Kafkaesque to me, right? So, you know, Kafka has these — he uses infinity a lot in his in his writing, you know. It reminds me of — so the L standing for law made me think of Kafka’s story “Before the Law,” which is part of The Trial. KW: I’m not familiar with that one. KK: It’s part of The Trial, but it's often pulled out as a separate story. So the deal is that the man goes to see the law, and there's a gatekeeper, and he can't get through because the guy won't let him through. But he says even if you do, there's another door inside. It’s sort of like these infinite gatekeepers that you can never, so you can never get to the law. And then the man who came to see the law, eventually he just stays because he needs to get in there, but he can never get in. And he keeps asking the guard, and the guard says, eh, maybe tomorrow. And then finally he just dies. You know, it's very, it is a very Kafka sort of story. But in a way, sort of these hierarchies are embedded in all of that. Okay. So I mean— KW: I mean, what he should have done is he should have taken an elementary sub-model. EL: Yeah. KW: Don't wait till the very end. Just get it down to a smaller piece. EL: Exactly. We're about to publish “Mathematicians Rewrite Kafka.” Mathematicians rewrite all 20th century literature, solve any problems anyone ran into, just take care of it. KK: So you've loved this theorem for how long? Is this a love at first sight kind of thing? KW: Ah, I mean, I'll be honest, it took me a good am…

    Full show notes at the publisher

    Episode 71 - Emily Howard Nov 11, 2021
    Show notes

    Episode 71 - Emily Howard Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello and welcome to my favorite theorem, a math podcast with no quiz at the end. I'm Evelyn Lamb, a freelance math and science writer based in Salt Lake City but currently podcasting from my parents’ house in Dallas, which is actually not any warmer than Salt Lake City right now, unfortunately. This is your other host. Kevin Knudson: I’m Kevin Knudson, professor of mathematics at the University of Florida. I'm in my faculty office. I'm usually — I'm the chair of the department. But I'm hiding out in the faculty office today. Actually, I was looking for better wireless. And it seems to be working a little better in here. But it's so weird because I have nothing in this office, like nothing. It's very strange. So anyway, how are things going for you? EL: Not too bad. Yeah, seeing my family, which is nice, and very excited about today's episode. So let's get right to it! We're happy today — both of us are music lovers, and we're very happy to introduce our guest, Emily Howard, a composer. Emily, do you want to tell us a little bit about yourself? Emily Howard: Yeah. So I'm based in the UK, in Manchester. I'm originally from Liverpool. And I'm a composer. I love writing for large ensembles, large acoustic ensembles, such as the orchestra. I also write vocal music, choral music, and also chamber music. So a lots of different areas. And I suppose probably the reason that you've got me on here is that I've got a real interest in mathematics. And actually, I have a degree in mathematics and computer science, my undergraduate is in mathematics and computer science. And I suppose that, you know, definitely it's one of the key influences on my work. EL: Yeah, I was listening to — it might have been the BBC Proms a few years ago — I was listening and saw this piece that I think was called Torus. And I thought, “You don't accidentally name something Torus.” So I decided to try to find out more about this person who had named her composition Torus. And so yeah, I found out that you had a math background, and thought it would be just really fun to talk to you on the podcast. So yeah, can you talk a little bit about the — I know, you've done some collaborations with mathematicians, you know, written pieces, like kind of in conversation with mathematicians in the composition process, and I would love to hear about that. EH: Yeah, so I mean, I suppose actually, in 2015, I had I think it was a Leverhulme fellowship at the University of Liverpool, working within the mathematics department. I had been invited by Lasse Rempe-Gillen. He's a professor in dynamical systems. And I think he'd been in touch because he had himself played the violin in an amateur orchestra in Liverpool, and they had performed a piece of mine called Mesmerism. Actually, it was after Ada Lovelace. Ada Lovelace used to dabble in all types of things, including mesmerism, sort of a form of early hypnosis. And the piece, I mean, that piece was a piece for solo piano and orchestra, and he was playing in it. And I think he thought it will be great to invite me as someone with a mathematics background back — after 15 years away in the music world— actually back into the maths department. And, I'm so glad that this happened, because going back in and speaking to lots of different mathematicians in a different way, rather than, I don't know, having to take exams and study, I was more being an observer, having amazing conversations about people's research. Regularly I'd speak to someone in one area of mathematics and another, and I suppose I felt — what I realized was that mathematicians often don't understand what each other are speaking about. So I didn't feel so bad about it, that I could sort of dip in and I suppose it helped me to take a more global approach, or to take in more general ideas, because I think that's one thing, perhaps, you lose if you don't practice mathematics regularly, you know, you lose this very detailed approach, and that's kind of annoying. It’s really annoying in many ways, but I also find it annoying when you can’t completely understand something because you'd have to spend quite a few years really thinking about it in depth. But then something is also gained from sitting back and taking a look at everything and absorbing it in a very different way. So anyway, we worked together on a set of chamber pieces. You can find them on my website. One’s called Leviathan, another is called, well, these are all pieces, exploring ideas from lattice research in dynamical systems. And I suppose they're all based on thinking about perturbations, and thinking about — I’ve got some etudes as well, they’re called Etudes in Dynamical Systems — just trying out very particular things with datasets, but also conceptual ideas about reaching stasis and loops from something. I also spoke a lot with colleagues in mathematical biology and also in topology. And I remember, I think this is where, kind of, talking about Torus, it was kind of born, the idea, because this period of time gave me more confidence to actually do something with mathematics. Just to go back back a bit, after my undergraduate in mathematics, I'd spent a couple of years doing a Masters at the Royal Northern College of Music in composition, and then more years at Manchester University doing a PhD in composition as well. And I suppose, when I was doing this, I really wanted to use these ideas or concepts from mathematics, but I didn't really have a musical technique to do that. I suppose that grew. And at this point, it sort of all merged, and I was able to perhaps achieve something that I wanted to achieve with these mathematical ideas through music. And so yeah, just to go on, you mentioned Torus. And I'm immensely proud of Torus. It's a big orchestral work, it's like 23 minutes long, based on the mathematical shape, a torus, a doughnut. And this is when I had actually just met a mathematician, Marcus du Sautoy, who I’ve worked with quite quite a lot. He's based at Oxford, and we met because of this thing. And when we spoke first, actually, he was writing about, no, he’d written a play about a torus. And I was sort of saying, “Well, actually my piece is about a torus,” so we really bonded on this. And one of the things that interested me most was, you know, the, the BBC Proms are held at the Royal Albert Hall in London. And the first thing he said was, “Well, that is a torus.” And I just thought, How funny. It really is, because you can run round and round it, you know, it's just just really nice to, you know, hear this. KK: So I'm especially fascinated here, because my son is a composer. Well, he just finished his undergraduate degree, and now he's in graduate school for composition. And so I'm always — first, I'm encouraged that composers can actually make a living, right, because whenever I would tell a mathematician, one of my friends, “Oh, my son's studying composition,” they say, “Well, how’s he going to live?” And I say, “Well, I don't know,” but clearly it’s possible to figure it out. So I'm super encouraged. Yeah, so Evelyn, you had a real question, though. EL: Oh, well, I was just saying this is an existence proof then for your son. KK: Yeah, that's right. That's right. EL: Yeah, well, I was — you know, music is a very non representational art form, unlike a canvas where you could — I mean, it's still hard to represent mathematical ideas on a painting. But can you say anything about how you do use the form of music to represent mathematical ideas? EH: I suppose — it's really interesting that you say that, because I completely agree with you actually, that it is very difficult to do that. And I wonder if, even if I, especially in abstract music, if we take aside — you know, if it has text in it, that's a very different thing — but in an abstract form, actually, I wonder if I ever tried to absolutely represent something from mathematics within music, whether in fact, anyone would know. And I suspect, actually, there are cases when I've worked very closely with people and they really know what I'm doing. I think they can certainly tell, but actually, I don't think that that's necessarily happens. And also, it's definitely not what I'm trying to do. Absolutely not. So I suppose my aim is not to represent mathematics. My aim is to — I mean, I love hearing about mathematics, and I'm completely inspired by processes and systems and patterns. And I suppose what I'm doing is taking them, and that’s a catalyst for my creative process. And so I do think something comes through, but I think it's more that I couldn't make what I'm making without doing this. And it's more that something new is occurring that came from thinking about these things. But it's definitely not a representational thing. I mean, that's definitely the case for maybe the more large-scale pieces we could talk about, like Torus. But I think also there have been a couple of pieces when I have tried my very best to represent ideas, and one of them would be this set of dynamical system etudes. That was one of them. And then another one would be the music of Proof, which is, I wrote a string quartet because Marcus and I were discussing proof and different ways you can prove things: proof by contradiction, by induction. He wanted to, to put those to me, and then I would create responses to different kinds of proofs. And so I suppose that’s as representational as I've been, and actually, it was really useful, because in doing that, then I gained a whole set of like, I thought about — I've never tried to write music before as though I'm solving a mathematical proof. But actually, in doing that, that led me to new places. So that's kind of what I'm trying to do, find new ways to do things and make new sounds. EL: Yeah, maybe respond more than represent you? EH: Definitely. KK: I mean, certainly, some music is sort of, you can tell, it's not deliberately mathematical, I don't think. I think of like, like Steve Reich's minimalism percussion pieces, right? EH: Clapping Music. KK: Yeah, they’re so cyclical. And you know, you can kind of, if you think about it as a mathematician, you can kind of imagine, well, if I saw this on the page, it would almost look like — our listeners can't see my hands doing this — but you can imagine sort of, you know, intersecting sine waves and things like that. So I can see how you could do that. EH: And to take that further, I wonder if all music in some way can be — well, I say reduced, and I don't like to reduce — but you could certainly represent ideas in very complex music, I think mathematically probably. Not that anyone necessarily has. But perhaps the secret to lots of things is in really complex music represented. I mean, I don't know. KK: Well, there’s a whole journal of mathematics and music. I mean, you could you could certainly, I sat on a PhD committee for composer on campus here, who really was trying to do these very strange time signatures that were sort of approximations of pi and things like that. EH: Wow. KK: And I put the question to him. I said, “Okay, I mean, you can make a machine do these. But, you know, can a human do this?” And he said, “Well, no, not really.” But it was interesting. EL: Can humans do math anyway? KK: This is not a philosophy podcast. I don't know. EL: Yeah. Thankfully,. EH: Just to say on that on that subject. I mean, I've got certain things — there are certain things you do put in notation that are absolutely beautiful mathematically, you know, but in fact, yes, they're not really possible. But they they do make a performer think in a certain way, and it will give you certain results. So I mean, I think there can be, I think they're beautiful, and they can be there. But you just can't expect perfection, perhaps. EL: Yeah. All right. So we always like to ask our guests, what is your favorite theorem? EH: I mean, we've kind of been speaking about it. That's, that's the main problem with this. I mean, actually, I would say, rather than a theorem, it's definitely these shapes. So I mean, let's take the torus, but I've got this fascination, I'd say, with thinking about mathematical shapes and thinking about, you know, them being far away, and also thinking about being on them. And I suppose, I mean, so you've got a torus. I mean, so, if you think about the difference between, say, flat geometry and a sphere with the spherical geometry, and then, I mean, there's a pseudosphere, and then I would call, like a negatively curved geometry, I've got a piece actually called Antisphere because I've worked out that an antisphere, there's a word antisphere, which is the same as the pseudosphere, and the word antisphere is a lovely word, and I don't like the word pseudosphere. So I called the piece Antisphere. And I suppose I've just got this — because, you know, my music is usually notated. So I find it very interesting as a process to be in my mind wandering around these shapes. And I suppose, I also think it’s very useful for music because they don't necessarily need to be 3d, I mean music, potentially, you could perhaps hear really high-dimensional mathematics. Because, you know, you could be traveling around and you're not necessarily visualizing it. So my answer to your question is that I like these mathematical shapes. And I've been thinking about them and really studying them and using them to influence these large-scale orchestral work. So that's Torus, Sphere, and Antisphere. And going forward, I'm now looking at, well, I'm trying to look at Thurston’s eight geometries and pushing myself in this direction with the aid of a number of very kind mathematical friends, because they're very difficult. And yeah, so that's kind of the direction where I’m going. And I find, you know, I suppose that's what we were saying earlier about the representation thing. So there are a number of stages. So I'm thinking about this tours, I'm thinking about traveling around it. Shall I give an example of the compositional process for writing Torus? KK: Sure, please. EL: Yeah. EH: Okay so Torus is a piece, yes, it was for the BBC Proms. It was performed first in 2016, by the Royal Liverpool Philharmonic Orchestra with Vasily Petrenko. Now I’d say, I wrote it over at least a year or so. And I'd also been thinking a lot about this just previously, thinking about traveling on this torus. And what I was thinking about when I wrote it was the idea that you're traveling round and round one direction of the torus, you know, and I took these very consonant chords, see, I've got major sixths. If you hear it, they’re a very constant chord. And I start with this major sixth, and I travel up one and down one and up another — they're going up in semitones, and down, and they reach a point and they come back again. So that the harmony is very much also shaped like a torus all the way through. And it's skewed, when you hear it, it's more complex than just listening to it timed because I've got a real combined fascination with exponential functions and the idea of really big things becoming absolutely minuscule, but also kind of being related. So, you know, this kind of journey around the torus, the first loop in this orchestral work is about five minutes long. And if you do listen, you'll hear it in the strings. And to my mind, I've got this sort…

    Full show notes at the publisher

    Episode 70 - Joel David Hamkins Sep 22, 2021
    Show notes

    Episode 70 - Joel David Hamkins Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello, and welcome to my favorite theorem, a math podcast with no quiz at the end. I'm Evelyn Lamb, one of your hosts. 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. I like your background, Evelyn. Our listeners can't can't see it. But it looks like you had a nice camping trip somewhere in Utah. EL: Yes, that's actually a couple years ago — we’re going to describe this in great detail for all the listeners who can't see it, no — this was a camping trip in Dinosaur National Monument on the Utah-Colorado border, which is a really cool place to visit. KK: Excellent. It’s beautiful over there. Yeah. So how's things? EL: Oh, not too bad. A bit smoky here, we're getting a lot of wildfire smoke from the west coast. And it, you know, makes some of those outdoor activities that are so fun, a little less fun. So I hope it clears out. KK: Right. Yeah, well, my big adventure lately was getting my son settled into his new apartment in Vancouver. And it was my first time on an airplane in a year and a half. That was weird. And then of course, Vancouver, such a lovely city, though. We had a good time. So he's all set up and starting grad school and his nice new adventure. EL: Yeah. And he's kind of, almost as far as he can be in a populated place in North America from you. KK: That’s correct. That's, like, almost 3000 miles. That's far. EL: Yeah. KK: It’s great. Anyway, enough about that. EL: Yeah. Well, today, we're very happy to have Joel David Hamkins join us. Joel, would you like to introduce yourself and say a little bit about yourself? Joel David Hamkins: Yeah, sure. I am a mathematician and philosopher at the University of Oxford. Actually it’s something of an identity crisis that I have, whether I'm a mathematician or a philosopher, because my original training was in mathematics. My PhD was in mathematics. And for many, many years, I counted myself as a mathematician. But somehow, over the years, my work became increasingly concerned with philosophical issues, and I managed somehow to turn myself into a philosopher. And so here in Oxford, my main appointment is in the philosophy faculty, although I'm also affiliated with the mathematics department. And so I don't really know what I am, whether a mathematician or a philosopher. I do work in mathematical logic and philosophical logic, really all parts of logic, and especially connected with the mathematics and philosophy of the infinite. KK: Well, math is just kind of, it's just applied philosophy anyway, right? EL: This might be a dangerous question. But what are — like, do you feel a big cultural difference between being in a math department and a philosophy department? JDH: Oh, there's huge cultural differences between math and philosophy, I mean, on many different issues, and they come up again and again, when I'm teaching especially, or interacting with colleagues and so on. I mean, for example, there's a completely different attitude about reading original works. In philosophy, this is very important to read the original authors, but in mathematics, we tend to read the newer accounts, even of old theorems, and maybe for good reason, because oftentimes, those newer accounts, I think, become improved with greater understanding or more connections with other work and so on. Okay, but one can certainly understand the value of reading the original authors. And there's many other issues like that, cultural differences between math and philosophy. EL: Yeah, well, I, when I was at the University of Utah, I taught math history a couple times. And I was trying to use some original sources there. And it is very difficult to read original sources in math. I don't know if part of that is just because we aren't used to it. But part of it, I do feel like the language, and the way we talk about things changes a lot really quickly. It makes it so that even reading papers from the 1920s or something, you sometimes feel like, “What are they talking about?” And then you find out Oh, they're just talking about degree four polynomials, but they're using terms that you just don't use anymore? JDH: Yeah, I think that's absolutely right. And oftentimes, though, it's really interesting when there's an old work that uses what we consider to be modern notation. Like if you look at Cantor's original writings on the ordinals, say, it's completely contemporary. His notation, he writes things that contemporary set theorists would be able to understand easily, and even even to the point of using the same Greek letters, alpha and beta to represent ordinals, and so on, which is what we still do today. And so it's quite remarkable when the original authors’ notation survives. That's amazing, I think. EL: Yeah. So we invited you here to share your favorite theorem. What have you decided to share with us today? JDH: Well, I found it really difficult to decide what my favorite theorem is. But I want to tell you about one of my most favorite theorems, which is the fundamental theorem of finite games. So this is the theorem, it was proved by Zermelo in 1913. And it's the theorem that asserts that in any two player finite game of perfect information, one of the players has a winning strategy, or else, both players have drawing strategies if it's a game that allows for draws. So that's the theorem. EL: So what is the definition of a game? This is, like, the most mathematical thing to set, you know, like, yeah, okay, games, we've all played them starting from when we were, you know, two years old or something. But now we need to sit down and define it. JDH: Yeah. Well, that's one of the things that really excites me about this theorem, because it forces you to grapple with exactly that question. What is a game? What is a finite game? What does it mean? And it's not an easy question to answer. I mean, the theorem itself, I think, is something that you might think is obvious. For example, if you think about the game of chess, well, maybe you would think it's obvious that, look, either one of the players has a winning strategy, or they both have drawing strategies. But when it comes to actually proving that fact, then maybe it's not so obvious, even for such a game as chess. And then you're forced to grapple with exactly the question that Evelyn just asked, you know, what is a finite game? What is a strategy? What is a winning strategy? What does it mean to have a winning strategy, and so on? So there's a wonderful paradox that surrounds the issue of finite games called the hypergame paradox. So if you have a naive account of what a finite game is, maybe you think a finite game is a game so that all plays of the game finish in finitely many moves or something. Okay, so that seems like a kind of reasonable definition of a finite game. And then there's this game called hypergame. And the way that you play hypergame is, say, if you and I are going to play hyper game, then the first player, maybe you go first, you choose a finite game. And then we play that game. And that's how you play hypergame. So the first player gets to pick which finite game you're going to play. And then you play that game. And if we said every finite game is a game, so that all plays end in finitely many moves, then it seems like this hypergame would be a finite game, because you picked a finite game, and then we play it, and then the game would have to end in finitely many moves. So it seems like hyper game itself is a finite game. But then, the paradox is that if hypergame is a finite game, then you could pick hypergame as your first move. KK: Right. JDH: Okay, but then we play hypergame. But we just said when playing hypergame, it's allowed to play hypergame as the first move. So then I would pick hypergame as my first move in that game. And then the next move would be to start playing hypergame. And then you could say, hypergame again. And then I could say hypergame, and so on, and we could all just say hypergame, all day long, forever. But that would be an infinite play. And so what's going on? Because it seems contradictory. We proved first, that every play of hyper game ends in finitely many moves, but then we exhibited a play that didn't end. So that's a kind of paradox that results from being naive about the answer to Evelyn's question, what is a finite game? If you're not clear on what a finite game is, then you're going to end up in this kind of Russell paradox situation. KK: Exactly. Yeah. That's what I was thinking of, the sets that don't contain themselves. Right. JDH: Right, exactly. It's actually a bit closer to the what's called the Burali-Forti paradox, which is the paradox of the class of all ordinals is well ordered, but it's bigger than any given ordinal. And so it's something like that. So if you think a lot about what a finite game is, then you're going to be led to the concept of a game tree. And a game tree is the sort of tree of all possible positions that you might get to. So there's the initial position at the start of the game, and the first player has some options that they can move to, and those are the sort of the child nodes of that root node. And then those nodes lead to further nodes for the choices of the second player, and so on. And so you get this game tree. And the thing about a finite game is that — well, one reasonable definition of finite game is that the whole game tree should be finite. So that in finite infinitely many moves, you're going to end up at a leaf node of the tree, and every leaf node should be labeled as a win for one of the players or the other, or as a draw, or whatever the outcomes are. And so if you have the finite tree conception of what it means to be a finite game, then hypergame is not a finite game, because at the first move, there are infinitely many different games you could choose, and so the game tree of hyper game won't be a finite tree, it will be an infinite tree. It will be infinitely branching at its first node. And so if you have this game tree conception, then a finite game can mean a game with a finite game tree. And then we can understand the fundamental theorem of finite games. So my favorite theorem is the assertion that in any finite game like that, with a finite game tree, then one of the players has a winning strategy, or both players have drawing strategies. And what does it mean to be a strategy? I mean, what is a strategy? If you think about chess strategies or strategies as they're talked about conventionally, then oftentimes, people just mean I kind of heuristic, you know, the strategy of control the center or something, but in mathematics, we want a more precise notion of strategy. So controlling the center isn't really a strategy; that’s just a heuristic. It doesn't tell you actually what to do. So a strategy is a function on the game tree that tells you exactly which moves to make whenever it's your turn. And then a play of the game is basically a branch through the game tree. And it conforms with this strategy if whenever it was your turn at a node in the game tree, then it did, what the strategy was telling you to do at that node. So a strategy is winning if all the plays that conform with that strategy end up in a win for that player. KK: Okay, so is this the point of view Zermelo took when he proved this? JDH: So yes, Zermelo didn't have, he didn't quite have it all together. And this is maybe related to the fact that we don't actually read Zermelo’s original paper now when we want to prove the fundamental theorem of finite games because we have a much richer understanding, I think, of this theorem now. I mean, for example, in my in my proof-writing book, I gave three different proofs of this theorem. And Zermelo didn't have the concept of a game tree, or even a finite game in terms of game trees, like I just described. Rather, he was focused specifically on the game of chess, and he was thinking about positions in chess as pictures of the board with the pieces and where they are. But nowadays, we don't really think of positions like that, and there's a kind of problem with thinking about positions like that. Because if you think about a position in chess, like a photograph of the board, then you don't even know whose turn it is, really, because the same position can arise, and it could be different players’ turns, and you don't know, for example, whether the king has moved yet or not, but that's a very important thing, because if the king has moved already, then castling is no longer an option for that player. KK: Right. JDH: Or, or you need to know also what the previous move was, in order to apply the en passant rule correctly, and so on. So if you just have the board, you don't know whether en passant is allowed or not. I mean, en passant is one of these finicky rules with the pawn captures where you can take it if the previous player had moved two steps, and your pawn is in a certain situation, then you can capture anyway. So I mean, it's a technical thing, it doesn't matter too much. But the point is that just knowing the photograph of the board doesn't tell you doesn't tell you whose turn it is, and it doesn't tell you all the information that you need to know in order to know what the valid moves are. So we think of now a position in a game is a node in the game tree, and that has all the all of the information that you need. So Zermelo’s proof was concerned with games that had the property that there were only finitely many possible configurations, like chess. There are only finitely many situations to be in, in chess. And he argued on the basis of that, but really, it amounts to arguing with the finiteness of the game tree in the end. KK: Yeah. So is this at all related to Nash's equilibrium theorem? So I was scrolling Twitter the other day, as one does when one has nothing else to do, and I saw a tweet that had, it was a screenshot. And it was the entire paper that Nash published in the Proceedings of the National Academy proving his equilibrium theorem. I mean, it's a remarkable thing that it's just a few paragraphs. Is there any connection here? JDH: Right, so actually, this kind of nomenclature, there are three different subjects connected with game theory. There’s game theory, which, Nash equilibrium and so on is usually considered part of game theory. And then there's another subject standing next to it, which is often called combinatorial game theory, or it's sometimes also called the the theory of games. And this is the the study of actual games like Nim, or chess, or Go, and so on, or the the Conway game values are part of this subject. And then there's a third subject, which I call the the logic of games, which is a study of things like the fundamental theorem of finite games, and the sort of the logical properties. So to my way of thinking, the Nash equilibrium is not directly connected with the theory of games, but rather is a core concept of game theory, which is studying things like the stability of probabilistic strategies, and so on, whereas combinatorial game theory isn't usually about those combinatorial strategies, but about sort of logically perfect strategies, and optimal play, and so on. And that isn't so much connected to my way of thinking with the Nash equilibrium. KK: So fine, I hand you a finite game. It has a winning s…

    Full show notes at the publisher

    Episode 69 - Ranthony Edmonds Aug 14, 2021
    Show notes

    Episode 69 - Ranthony Edmonds Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a math podcast with no quiz at the end. 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, a freelance math and science writer in Salt Lake City, Utah, where there is a family of quail that live outside my window. And they don't know that I'm here so I can watch them scurrying around in the bushes. There are at least five young ones right now. KK: Cool. EL: They are so cute. Oh, it's just like, sometimes — they're not here right now, which is good. Because otherwise, I would just be like, staring out my window. Looking at these cute little quail. KK: Oh, see, so in Florida, we're in actually the boring birds season because you know, it's just, this is the locals. So I see my cardinals and the titmice and all of that, but it's still fun. I still feed them. I'm out there every day. They’re eating me out of house at home. It's true. It's a good thing. All right, well, so today we are very pleased to welcome Ranthony Edmonds. Why don't you introduce yourself, please? Ranthony Edmonds: Hi. Yes. So I'm Ranthony Edmonds. I'm a postdoctoral researcher at The Ohio State University. KK: The Ohio State. RE: The “the” is very important in Columbus. We take this very seriously. And I've actually become one of those people who corrects and makes sure that they add the “the” in conferences and notes and things like this. It’s very obnoxious. Yeah. So I'm a postdoctoral researcher at The Ohio State University. I study commutative ring theory, classically, specifically factorization theory. And I'm in the midst of this sort of interesting transition, where I am looking into applications of algebraic topology. So I spent the last year learning a bit about topological data analysis, and I’m specifically interested in applying that to redistricting. So I, you know, I my interests are kind of broad, but specifically, to kind of give you some keywords, commutative ring theory, topological data analysis, redistricting. And, of course, you know, my general mission is to increase access to mathematics for Black Americans and members of other traditionally underrepresented groups in the mathematical sciences. And I'm trying to do that through a combination of inclusive pedagogy. academic research, and community engaged scholarship. EL: Nice, and I was perusing your website before this to familiarize myself a little bit, and I saw that you're working on a kind of a history-related project about Black mathematicians at The Ohio State University historically? RE: Yes. I think a lot of people had a lot of different reactions to what happened last summer with with George Floyd and the protests that swept over the country. And one thing that I sort of questioned is this idea of, well, if we're going to try to improve access to mathematics for Black Americans, for other traditionally underrepresented groups, how do we really begin to do impactful work if we're not really aware of what's happened historically? And I've always been interested in history. I think a lot of this comes from a previous project I'm still doing with the Hidden Figures story and sort of using that to center discussions about diversity and equity in the discipline. But yeah, I just love math history. I have a team that's very interdisciplinary, and we're looking at the history of the math department, specifically at Ohio State this summer, and then on into the fall. So there are really two things that we want to do. One is, you know, a lot of the narratives of these the pioneers who graduate from the department with PhDs, with master's degrees, they're kind of just hidden. There’s not a lot of recognition about the work that they've done. But we have discovered that there are seven Black PhDs who have graduated with a doctorate degree in math from Ohio State. And we’ve got two former university presidents among that midst. We've got lawyers, authors, you know, program officers in the NSF, just people who have gone on to do really prolific things, and yet are still somewhat kind of unacknowledged by the university themselves, and then just in the wider math community, and I think that there are a lot of hidden stories out there. And I think when I reflect on the Hidden Figures story, this is what made that so impactful is because people didn't know. So I think that there's a lot of work out there that's being done by wonderful people that people just don't know about. And so what we're trying to do is to highlight and amplify those stories, one. And then two, examine and contextualize their experiences at the university. So what was happening when they were students here? What influenced their trajectories after graduation, where they went to work, if they went to industry or academia? I think if we think about trying to get more people in graduate school or get more people at the Faculty level, well, we should start by thinking about how we're serving our undergrads who are in that actual population and how we've done that historically. So we're doing a lot of things. I'm working with some people in strategic communication, some people in our Office of Diversity and Inclusion here at Ohio State. Also we have a connection with the National math Alliance. So they are an organization that I'm very intimately familiar with from my time in graduate school at the University of Iowa, but they are really focused on trying to increase the number of minorities entering PhD programs in the mathematical sciences. And this is pretty broad, right? It's not just math. It’s statistics, it’s economics, it’s something that requires quantitative training as an undergraduate. So we're working with them. And we're also working with a local museum, the Ohio History Connection, and there is a specific branch which is, they call themselves Afro-Am, but the official title is the National Afro American Museum and Cultural Center. And they're located in Wilberforce, Ohio. We're working with them with some of our archival research, and also our community programming. So we've learned a lot of really interesting things. We’ve sort of broken up the history of the department starting at 1963, when the first Black male PhD, his name was William McWorter, graduated from Ohio State up to the present and just identifying individuals who earned degrees during that time period and interviewing them, as well as sort of contextualizing what are the big things? How did selective admissions affect Black student enrollment in general and specifically in the math department? How did you know the protest of the ‘60s and ‘70s impact the campus environment? We have students who are wonderful who are helping us look at these different questions. And then there's actually like a lot of cool math. For instance, the the first Black male PhD from Ohio State, his name was William McWorter. And he was part of this camp along with like Axler and others that was like “death to determinants.” We don't need them, why are we teaching them to students? He felt like it was a very crippling tool pedagogically, in that students just used them for computations and had no idea what they were. And which makes sense, because I think I've experienced that on the student end of things. EL: I’d say guilty as charged there. RE: So he wrote a couple of papers for that were published in Math Magazine about determinant-free methods in linear algebra. And specifically he competed came up with an algorithm for computing the characteristic polynomial of a matrix and computing eigenvalues and eigenvectors of a matrix without using the determinant. And I say, “a matrix,” there are obviously conditions imposed upon it, but it was really cool. So I'm working with a student this summer, and we're reading through this paper, and then we want to create a lesson plan related to that algorithm. Because it mainly focuses on dependency relationships, like do you understand the difference between, like, given a list of vectors, can you determine if they're linearly independent or dependent? And then it requires doing that via elementary row operations. So it's just sort of hitting some of the high points from introductory linear algebra, without getting into the weeds of what the determinant really is. So we're working to create a lesson plan from that, and then hopefully, that'll be incorporated back into the honors track here. It was when he taught here. And disseminating that. Our main goal is learn it and then disseminate it, you know, so other people can can learn from what we're figuring out. So there's this history component, and then there's a lot of math that we're uncovering from the history that's just really interesting in its own right, that we hope to, over time turn into lesson plans that other people can use for their classrooms. EL: That’s really cool. Like, so I've done a little bit of dabbling in math history and stuff. And it's always really interesting to me how much the language has changed. And you'll see an abstract for a paper written it decades ago and realize, like, we just talk about things differently now, and it's kind of hard to dig down and figure out, Okay, how would I think about what they're doing here? You know, they have these names for different curves that aren't names I use anymore, and like, how do I translate it? It's like almost a translation project. Even going back just to the ‘60s, maybe. RE: Yeah. EL: So that must be really interesting. And I think that's a great project for students to do. So yeah, that sounds so cool. KK: It is. You're very busy. And you know, your list of mathematical interests is super interesting to me too. I mean, I'm not a ring theorist, but the whole TDA and redistricting. RE: Yeah. We’ll have to talk a bit after the podcast. But yeah, it is really interesting. And I think, you know, it's part of this whole approach of just trying to humanize mathematics. We're studying it, and we're getting into the nitty details, but we're also thinking about how people came to be mathematicians, and how this has actually been affected historically, especially for Black Americans, by policies. You know, a lot of the PhDs that we're studying about were supported by NSF fellowships, and this is a direct response to the space race that was happening. And they saw the influx of federal funding. And so it's all really interesting. I feel like I'm learning a lot about — even though it's focused on Ohio State — I feel like I'm learning a lot about the math community. And one thing that is really cool about us is sort of how we do our lineage. Right? And so the math genealogy websites are really cool, because you can sort of track back, very easily, Oh, this person who would who would they have been working with? You know, I think in another discipline, if you are trying to figure some information out about the person that you want to know, who their academic “siblings” were, that might be actually difficult to discern, but we have the Math Genealogy site where we can get that information easily. KK: Yeah. All right. So this podcast, though, is called My Favorite Theorem, so we asked you on here for a reason. So, Ranthony, what is your favorite theorem? RE: Yeah, so I am taking it back, back, back, back. So I actually would say that my favorite theorem is the fundamental theorem of arithmetic. KK: Okay. RE: It’s very classic. And the reason that I like it is because it's sort of the first introduction to really meeting math in disguise, because I think a lot of people are at least aware of the concept of it in grade school, even if maybe we don't get into the implications. And so, you know, the fundamental theorem of arithmetic, it states that, given an integer, so positive whole numbers greater than one. So yeah, greater than one, excluding zero, you know, it can be written uniquely as the product of prime numbers. And that this decomposition into primes is unique except for the order. So in practice, it means give me a number, like any number that's an integer, and I can factor it uniquely into small pieces called primes. And that’s it. That's the only way I can factor this number. And it gives it a unique signature. And it's telling us that in the same way that atoms are the building blocks of ordinary matter, these prime numbers build up the integers. And I love it, because there are a lot of implications in the work that I do in factorization theory that can all sort of be traced down to this fundamental idea. And I also love it because when I talk to younger students about ring theory or things like this, I always start with the fundamental theorem of arithmetic. And I tell them when they're drawing factor trees — at least that's how I learned, I'm not sure how you guys — is that what you remember? You had the number and then you do the branches? KK: Yeah. EL: I loved doing that when I was a kid. I don't know if you two were also like that. That it was kind of a soothing little exercise. Like, write down a big number — not too big; I wasn't super ambitious — but like, write write down a number and just do a little tree figuring out, you know, yeah, that kind of thing. I don't know. I thought it was fun. RE: Yeah, I usually start off with when I’m talking with — I don't want to say little kids, right — with general audiences. I'll start off by asking people to pick their favorite three-digit number. So I guess maybe, do you guys have a phone or calculator handy? KK: Sure. RE: So this may not pack the same sort of punch, but I ask people to pick their favorite three-digit number. And then I ask them to create a six-digit number by taking that three-digit number and repeating it twice. So I usually use 314 because it's an approximation for pi. I was also married on Pi Day. And so 314 is is my number, and then I create a six-digit number, so that's going to be 314,314. Yeah. Okay. KK: I chose 312. RE: Okay, all right. And so I want you to take your six-digit number and divide it by 11. KK: Okay. RE: Okay. I have 28,574 right now. And then I want you to take that number and divide it by 13. KK: It’s amazing that you're getting integers here. RE: Yeah. EL: Or is it? RE: So now I have 2198. Okay, and so now I want you to take this number and divide it by your original three-digit number. KK: Yep. RE: And did everyone get seven? KK: Yes. EL: Yay! RE: So yeah, so it's like this really cool thing where if you take a number and you multiply it by 1001, it has the effect of creating a new six-digit number. That's your original original number repeated twice. And so essentially, because we know that 1001 factors uniquely into primes, which is guaranteed to us by the fundamental theorem of arithmetic, you know, 1001 is 7×11×13. And so if you divide away 11, then divide away 13, if you divide away that original number, no matter what it was, you're going to be left with 7. And so it's really exploiting this property of the integers. This is really cool. And so I don't know, I just really love the theorem. So why, I guess maybe, do I care about it? In terms of the mathematical sense, besides the fact that it's cool? It’s because there's a lot of deeper underlying mathematics here. So it's like, we have this statement that tells us, given any integer, we can decompose it uniquely into the p…

    Full show notes at the publisher

    Episode 68 - Rekha Thomas Jul 08, 2021
    Show notes

    Episode 68 - Rekha Thomas Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello, and welcome to My Favorite Theorem, a math podcast with no quiz at the end. I'm Evelyn Lamb, one of your hosts. 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. How are you doing, Evelyn? EL: I’m doing okay. I was trying to think of something interesting to talk to you about at the beginning of this and life isn't very interesting, but in a good way. So that's good. KK: Yeah. Yeah. You know, monotony is underrated, isn't it? EL: Yeah. KK: We just had our 29th wedding anniversary on Sunday. EL: Congratulations. KK: Thank you. That's our big news. We went out and sat at a restaurant for the first time in nearly a year. So we were very excited EL: Excellent. I hope that, you know, this podcast is going to go on for decades and decades, and someone is going to be catching up on old episodes, you know, in 15 years and say, “Why do they keep talking about all these very boring things that they're doing for the first time in a year?” So that'll be great. You know, last time it was haircuts, this time it’s restaurants. KK: That’s right. That's right. Yeah. EL: But whether they are saying that or not, they will be very excited that we are talking today to Rekha Thomas, and Rehka, would you like to introduce yourself? Rekha Thomas: Hi, thanks for having me on the show. So I am a professor of math at the University of Washington in Seattle. And I come originally from optimization. My PhD is actually in operations research. But I have worked in a math department ever since I graduated. And my work lies somewhere at the intersection of optimization, applied algebraic geometry and combinatorics. And I very much like problems that have an applied background, or a motivation. Not necessarily because I work in applied things, but because I very much like the problem to be motivated by something real. And in the last 10 years or so I've been working quite a bit in the mathematics behind questions that come from computer vision. And this has been especially fun, but optimization in general does have that applied side to it. So I like problems like that. KK: Right. You know, I talked to my colleagues over an industrial and systems engineering, where they do a lot of that work all the time. And I think, “You guys are basically mathematicians. Why don't you come over here?” Heavy overlap there. Yeah. EL: Well, and I just want to say, how we came to invite Rehka onto the podcast. KK: Yes. EL: I think it's pretty cool. So one of her students sent in an email to our little submission form on Kevin's website, saying that he thought that his linear algebra teacher was great and would be a great guest for My Favorite Theorem. And this is especially remarkable because this is in the semester, we're all teaching has been online and stuff. And I just think to be connecting with students enough when you're doing Zoom teaching, that they actually reach out to a podcast to get you as a guest on a podcast, just you must have really made an impression. So I think that's very cool. RT: Thank you. I was very honored by that piece of news. I thought, “Okay. We finally made contact.” That was great. But especially for an undergraduate to take that initiative to write to you was very touching. EL: Yeah. So you know, not to build things up too much, but I'm sure that this is going to be a great episode. KK: Yeah. EL: So with that introduction done, what is your favorite theorem? RT: Yeah, so I announced my favorite theorem in that class and told them about your podcast. So when you invited me, I thought, “Oops, is that really my favorite theorem? Is that what I really want to speak about?” And I think yes, so I'm going to stick with that theorem. And this is a theorem from linear algebra. It was a linear algebra class. And I think it's one that's not very well known to pure mathematicians. So it goes by the name of Eckart-Young theorem or the Eckart-Young-Mirsky theorem, but apparently the history is more complicated. I've been reading about this so I can tell you about it if we get to that. So the theorem is basically says the following. So if you have a matrix, say a real matrix of size N by N, and we know its singular value decomposition [SVD] — which is a very special decomposition of the matrix as a sum of rank-one matrices — then the closest rank-K matrix to the given matrix is just made up of summing the first K rank-ones in that decomposition. So if I wanted the closest rank-one matrix, I just take the first rank-one in the singular value decomposition of A. If I wanted the closest rank-two matrix, I take the first plus the second rank-one matrices in the singular value decomposition, and so on. So there is this neatly arranged set of rank-one matrices that add up to give you A, and if you truncate that sum anywhere at the K-th spot, you get precisely the closest rank-K matrix to the given matrix. This is the theorem. KK: Closest in what sense? What’s the metric? RT: Yeah, so that's a very good question. So closest in either Frobenius norm or spectral norm. So these are standard norms on matrices. Frobenius norm is just, you think of your matrix as a long vector, where you just take every row, let's say, and concatenate it to make make a long vector, and then take the usual Euclidean norm. Okay. So the sum of Ai,j squared, square root. [Maybe a little easier to read in math notation: (Σ Ai,j2 )1/2.] Spectral norm is the largest singular value, so it is the biggest stretch that the matrix can make on a unit vector. KK: Okay. RT: So in either norm this works. KK: That’s pretty remarkable. EL: Yeah. So just doing a little bit of mathematical free association, is this, can this in any way be thought of as like a Taylor's Theorem, like, you're kind of saying like, Okay, if you want your approximation to be this good, you go out this far, if you want it to be this good, you go out this far, maybe that’s — I don't I don't know if that's a good analogy. RT: No, I think it is. So I think there are many constructs in mathematics like this, right? Like Fourier series is another one, where you have a break down into pieces, and then you take whichever part you want. Taylor series is similar. The SVD is special in the sense that the breakdown, this breakdown into rank-one matrices is actually tailored to the matrix itself. Like, for example, as opposed to, say, in Fourier series, where the the basic functions that we are trying to write the function as a combination of, they are fixed, right? It is always cos(θ) + i sin(θ), or cos(Nθ) + i sin(Nθ). So it's not particularly tailored to the function. It's just a fixed set of bases, and you're trying to write any function as a combination of those basis functions. But in the SVD, the basis that you construct, the factorization that you get, is tailored to the actual data that's sitting inside the matrix. So it's very, very nice and is incredibly powerful. So it's similar, and yet, I think, slightly different. KK: Right. In other words, yeah, that's a good explanation. Because, as you say, with Taylor series, you're choosing a basis for a subspace of the space of all smooth functions or whatever. Whereas here, you're taking a particular matrix. Does it matter what — so if you change the basis of your vector space, do you get a different SVD? RT: No. So what the SVD is — there is a very nice geometric way to think of the SVD, which may answer that question better. So the SVD is sort of a reflection of how the matrix works as an operator. So what it's telling you is if you take the unit sphere in the domain, so let's say we have an N by M matrix, so the domain is Rn, take the unit sphere, under the map A, the sphere goes to an ellipsoid. KK: Right. RT: In general, a hyperellipsoid, right? And this ellipsoid has semi-axes. The singular values are the lengths of those semi-axes. Oh, so it tells you, yeah, the length of the semi axes, and the unit vectors in the directions of those semi-axes are one set of basis vectors. So that's one set of singular vectors, and the pre-images in the domain are the other set of singular vectors. So if you're willing to change basis to the special bases, one in the domain, one in the codomain, then your matrix essentially behaves just as diagonal matrix. It just scales coordinates by these singular values. So it's a generalization of diagonalization in some ways, but one that works for any matrix. You don't need any special condition. So they're very canonical bases that are tailored to the action of the matrix. KK: I’m learning a lot today. So I have to say, so before we started recording, I was mentioning that I think students need to take more linear algebra. But the truth is, I need to take more linear algebra. RT: I think we all do. EL: Yeah, I mean, I am really realizing how long it's been since I thought seriously about linear algebra. So this is fun. And it goes really well with our episode from a few months ago. I believe that was Tai-Danae Bradley who chose a singular value decomposition as her favorite theorem. So we've got, you know, a little chaser that you can have after this episode, if you want to catch up on that. So Rekha, you said that there was a bit of a complicated history with this theorem. So do you want to talk a little bit about that? RT: Yeah, sure, I'd be happy to. So I always knew this theorem as the Eckart-Young theorem. I only recently learned that it had Mirsky attached to it. But then in trying to prepare for this podcast, I started looking at some of the history of the singular value decomposition. And there's a very nice SIAM review article by Stewart, written in 1993, about the history of the singular value decomposition. And according to him, singular value decomposition should be attributed, or the first version of it, is due to Beltrami and Jordan from 1873 — so the Eckart Yang theorem is from 1936, so almost 60 years before — and they were looking at more special cases, they were looking at square real matrices that are non singular, perhaps. So they, you know, people were interested in that special case. Then there were several other people, like Sylvester walked on it. Schmidt from Gram-Schmidt, he worked on it, Hermann Weyl worked on it. So from 1873 to 1912, this went on. And this article says that this approximation theorem that I mentioned, the Eckart-Young theorem, is really due to Schmidt from Gram-Schmidt fame. And he was interested not so much in matrices, but he was studying integral equations, where you have both symmetric and asymmetric kernels, non-symmetric kernels. And he wrote down this approximation theorem. So really, Stuart claims that that this theorem should actually be attributed to Schmidt. And then in 1936, Eckart and Young actually wrote down the SVD for general rectangular matrices. So that is in that paper, for sure. And they seem to have rediscovered this approximation theorem. So, that is my understanding of the history of how it is, but I did not know this till two days ago. And I'm not really a bonafide historian in any way. So, but this is what I've understood. It's an interesting story. EL: Yeah, that sounds like an interesting article. I mean, I guess in the history of math, there are an uncountable number of places where unravelling back to where the idea first appeared is more complicated than you think. RT: Right. KK: And also, this sort of gets at your interests more generally, how you like things to sort of come from an actual application. Well, if this really came from integral equations, right, that's really an application. RT: Absolutely. KK: So it's working on lots of levels for you. RT: That’s right. So he, of course, did this in infinite-dimensional vector spaces. And approximation will allow you to approximate an operator as opposed to a matrix, right? And apparently, that really elevated this whole theory from just a theoretical tool to something that's actually widely used. It became much more of a practical tool. And I guess, in modern day, the SVD, and versions of the SVD in exists in all kinds of mathematical sciences. So in signal processing, and fluid dynamics, all sorts of places. So it's, in some sense, one of our biggest exports from the math world, and yet we don't quite teach it normally, to math people. So yeah. KK: Right. So was this like a love at first sight thereom? Was this the sort of thing that came up a lot in your work, and that's why you're now so enamored? RT: So I did learn of at first in the process of writing a paper. I did not know about this theorem before, maybe about 10 years ago. But I think this theorem sort of perfectly fits me, which is why I love this theorem. So for different reasons, right? So first of all, it's an optimization problem, it's about minimizing distance from a matrix to the set of rank-whatever matrices. So it's an optimization problem. The space that you're trying to minimize to, which is the space of rank at most k matrices, that is an algebraic variety. So it can be written as the set of solutions to polynomial equations. So there's the applied algebraic geometry side, or at least the algebraic geometry side. And it's not a very simple variety. It's actually a complicated variety. So it's an interesting one. And lastly, this problem is sort of a prototypical problem in many, many applications. So a lot of statistical estimation problems are of this flavor. You have a model, which, let's say is our rank-K variety, so the rank being some measure of complexity. And then you have an observation that you have in the field with instruments, and it tends to be noisy, so it's not on the model. So that's your observed matrix. And now you're trying to find the maximum likelihood estimate, or the closest true object, that fits the observation. So this is a very standard problem that comes up in many, many applications. So in some sense, I feel it really lives at this intersection of optimization, algebraic geometry and applications, which is sort of what I do. KK: That’s you. RT: Yeah. So that's one reason that I think this theorem is so cool. And the other thing is, I think it's a very rare instance of an optimization problem where the object, the observed matrix, knows the answer in its DNA. It doesn't need to know the the landscape that it's trying to get to, which is your space of matrices of rank at most K. it doesn't need to know anything about that variety. Just inside its own DNA, it has this SVD. And from the SVD, it knows exactly where to go. So this is completely unusual in an optimization problem, right? Like even if you're minimizing, say, a univariate function over the interval [0,1], I really need to know the interval [0,1], to figure out what the minimum value is. The function doesn't know it. But this is, I think, kind of a gem in that sense. You don't need to know the constraint set. And then lastly, it appears all over the place in applications. So in things like image compression, you know, the Netflix problem is sort of a version of this, distance realization, you know, things that would come up in areas like molecular modeling, or protein folding, and so on. It's all — many of these problems can be thought of as low rank approximation to a given matrix. KK: Very cool. Yeah, I'm now I'm thinking about that variety. It's not a Grassmannian. But it's sort of like, is it stratified by…

    Full show notes at the publisher

    Episode 67 - Liz Munch Jun 10, 2021
    Show notes

    Episode 67 - Liz Munch Kevin Knudson & Evelyn Lamb Download Kevin Knudson: Welcome to My Favorite Theorem, a math podcast with no quiz at the end. I'm Kevin Knutson, professor of mathematics at the University of Florida. I am joined today by your fabulous and glorious other host. Evelyn Lamb: Hi, I'm Evelyn Lamb, a freelance math and science writer in Salt Lake City, Utah. And I assume the “fabulous and glorious” means my new haircut, which I actually got professionally cut for the first time in quite a while recently. KK: Good for you. EL: I had been playing around with, you know, scissors — not official hair cutting scissors, just you know, the scissors that are lying in the kitchen drawer — and, like, my clippers and stuff for a while, but I went in and I look very sleek and chic right now. KK: You do. You look very sleek. I’m getting a haircut tomorrow, I look less sleek. EL: Not too bad, though. KK: You know, I still have the plague beard though. I don't know, I can't decide what to do about that. And my hair has gotten longer, but you know, thinning, and I don't know what to do anymore. EL: My husband actually decided to try for the ponytail. So he had been very excited about, like, two weeks after the second shot going and like getting a real haircut for the first time in a while. And then by that time, it was long enough that it's almost ponytailable. So he's like, “I've never had a ponytail. I think I want to try this.” So so now we're like at opposite, you know, going in opposite directions. KK: I have a ponytail and my wedding pictures and then that’s the last time. EL: Okay. KK: Anyway, today we are pleased to welcome our good friend Liz Munch. Liz, you want introduce yourself? Liz Munch: Hi. Yep, so I'm Liz Munch. So I am an assistant professor at Michigan State University. I'm in the departments of Computational Mathematics, Science, and Engineering, which is a mouthful, but we just say CMSE. And I'm also in the Department of Mathematics. EL: All right, and what is your recent hair story? LM: It’s the kind of, like, what can I do rolling out of the shower to like, make it do anything? And it's kind of like, wherever it lands. I did get a haircut, though, recently. It was very exciting. I had — it was getting so long that it was going out of control. And usually what happens is I I go get a haircut and I do like extreme versions. I'm like, “I hate my hair!” And then I chop it off to my ears. And then I grow it out again. And then “I hate my hair!” and I chop it off to my ears. This time, she convinced me not to chop it off all the way, but I'm due for one. So we're gonna work on getting that one soon. KK: Looks good. Looks good. Yeah. So CMSE, that's interesting. You have this joint appointment. How's that? Yeah. LM: I like it. It's different. So at least for me. So I do very interdisciplinary math, very applied math, but not your usual applied math. And so for me, it's a it's a really nice setting to be in. So CMSE is — so the faculty in CMSE are all sorts of different backgrounds. So there are mathematicians, but there are also statisticians and biostatisticians and biologists and plant biologists and geologists and physicists and engineers, and I'm sure I'm forgetting people. But it's basically rigged to be interdisciplinary. And so it's made a lot of fun, because you can find these interesting projects that are sort of in the intersection of complicated math and interesting stuff to do there with applied projects I would never have thought of before. So at least for me, that's been a really good fit for trying to do interesting applied math research explicitly outside of the usual academic silos. KK: Yeah. Well, that's nice, because I think a lot of us in math departments often want to engage in these activities, but you know, the physics department’s in another building, and it's more effort, right? You know, it's true, there’s this idea of these collision spaces. So you know, if you have colleagues in all these different disciplines just right down the hall, it might lead to more interesting stuff. So that's a nice model. I wish we could do more of that. Anyway. So you have a favorite theorem, I hear. LM: I do have a favorite theorem. KK: You going to tell us what it is? LM: I was going to talk about max flow min cut. KK: Okay. LM: Which is sort of my, I don't know — so the reason I like max flow min cut, is partially because, again, I do interdisciplinary applied mathematics, and I tend to fall into the theoretical computer science land a lot, just based on a lot of things we want to do. Because I work in TDA, topological data analysis research, and so a lot of things come down to here's a complicated thing I want to compute, and I’ve got to go figure out how to do it in a computer in a reasonable amount of time. So this is an example of that. So max flow min cut. So here's the game. So you get yourself a directed graph. And this graph has a source and a sink. So you've got your S vertex source and your T vertex sink. And you essentially have capacities on each of the edges. So my directed edges, I now have an amount of stuff I can push across the edge. So I like to think about this with, like, water tube flows kind of thing, right? So I've got how much capacity each of the edges can take. And so the game is, try to see how much stuff you can push along from the source to the sink, basically playing nice with those capacities, so flowing from one side to the other. So if you're at any vertex, all of your inward flow amounts should be equal to all of your outward flow amounts, because you can't have anything hanging out at the vertex. And so your other restriction is your flow values — this is essentially like a second choice of weighting on all your edges — your flow values have to be less than your capacity values, right? So you can't flow more across an edge than the capacity allows. KK: Sure. LM: And so the trick is, what's the most amount of flow you can get across? I hand you one of these graph setup problems, what's the most flow you can get across these things? So the max flow min cut, as the title would suggest, is that you also need to know something about the minimum cut. So what's what's a minimum cut? So same starting input information. What you can do is you can try to divide your collection of vertices into two piles. And the rule is that you have to have your source in your sink in different piles. And the cost of whatever choice of piles has to do with, essentially the cost of the capacities that go from one pile to the other, right? So you sort of add up those values, there's some negatives if things are backwards, etc. But that's the cost of a cut. KK: Okay. LM: And so the game with that part of the problem is you want to make that as small as possible, right? Can I reorganize these vertices in such a way to make that cut cost low? And so the max flow min cut theorem says the capacity of the highest possible flow value is equal to the cut, the minimum possible cut value you could get. And so this is cool, because this means that you could answer your problem either by going and hunting for a maximum flow value or by hunting for a minimum cut value. And so it gives you two very different ways of looking at this problem to try to solve something. And these show up in all sorts of different application settings, right? So you can imagine, like, railway networks, where you're trying to move stuff around, you could try to do things with electric grids, and you can do things with water flow. There are lots of places where this thing would show up. And so having access to two different ways of looking at the problem is super useful. EL: Yeah. So I have never thought about this before. So I'll just kind of — I’m not even sure if I can formulate the right question. But I'm trying to think, my mind immediately goes to extremes. So like, if you put everything except your source in one pile, or everything except your sink in one pile, is it obvious that usually that's not going to be good? LM: I guess it kind of depends on the sort of setup you have, right? So I guess the simplest version would be like, okay, let's start with a graph that's just a path, right? And so in that case, I can stick a bunch of random numbers as capacities on my path, and so the maximum flow I can get across this path is going to be something like the minimum weight capacity that I've got, right? KK: Yes. LM: That minimum weight capacity is also going to be if I chop my path graph in half at that particular edge, that's as low as my cost for my path can go. EL: Sorry, just to slow down a little bit for me. So that capacity is like, this one edge can only hold, you know, whatever amount of whatever stuff? LM: Yeah. EL: Okay. Great. LM: This is kind of like the bottleneck in the system, right? Like I could have pushed, like, 30 gallons back here. But if this particular tube only allows for five gallons, it doesn't matter how much I can push in the backend, right? It's got to be, it's limited by this one, right? EL: Yeah. LM: And so then you basically take that, that path problem and scale it up. So then you've got all these other possible paths where you could push stuff through. You could imagine a vertex where you have, like, a capacity of 10 coming in, and maybe five edges coming out. And so your flow could be 10 in and then it got split up and there's all sorts of things. And so the way you prove this, so this is the Ford–Fulkerson algorithm from the 1950s, I think? It basically comes from looking for paths like that. So the whole problem comes down to basically that path example I was giving you. So you essentially rig up this modified version of your graph, and then look for paths from your start to your sink, where you try to see places where I could push some amount of flow across that line, across that path. And what you do is you essentially update some sort of flow that you're trying to build as big as possible. So if I've got a path where the bottleneck value was five, I now push value of five across each of the edges in that path. And then I update this other graph I can use to keep track of things. This is called a residual graph. Basically, then I update this flow, and then I go look for a new path. And then I update this flow, and then I look for a new path. And it all comes down to just finding these paths that have some sort of bottleneck in them and updating accordingly. KK: That seems slow. LM: Oh, yeah. Oh, yeah. This is not nice. KK: So there must be better algorithms for doing this. But that’s the easiest to explain algorithm? LM: Yeah, right, easiest to explain, and the one you get the proof off of. So that's the the Ford–Fulkerson one. And of course, I did not check this beforehand, but I believe the running time has something to do with the value of your maximum flow. And there are some caveats in here. So if you have integer-valued flows, so I'm only allowed to put integers on each of my edges, that process will terminate, which is a good sign, right? It will terminate at the best possible flow, which is a good thing, right? And worst-case scenario, you basically updated by one every time, and so the the order of the number of times you have to do that update procedure has to do with essentially the maximum flow value. KK: Okay. LM: So yeah, it's not pretty, but it works. This is another one of these examples where in practice, it tends to not be that bad. But of course, you can construct the nasty examples. KK: Sure. Right. So yeah, the theory is bad, but the practice is good. So that's fine. LM: Yeah. KK: So do you use this much? Does it come up for you? LM: I haven’t in a long time. The reason I was thinking of this was a while ago, I was teaching this summer program for bright high school kids, where we were doing a topology class for high school kids that basically have seen calculus. And so one of the reasons I like this was I was remembering I had rigged up this thing where I created a graph on the ground. And I put capacities on it involved in the — sort of like, I don't know, I think it was squares of paper or something like that — and messing around with getting them to actually manually push stuff around on this graph and start thinking about flows and how you can update information. Yeah, it hasn't come up in my research recently. But I think in general, just the messing around with problems where you can have multiple ways to find a solution, that's been really interesting lately, because trying to rig, trying to look at a problem from a different angle where you might have an easier ability to go solve something than something else, right? So like, if you told me go find the minimum cut in this particular graph, the knee jerk reaction is like, “Okay, let's test all possible ways of splitting the two piles!” which you really don't want to do, right? Bad idea. Never do that, right? And I don't have any good intuition for how — if you had to be that problem, I would start going at any sort of algorithm that would get me anywhere. But because we've got this duality idea, right, I can go back and do this pushing flows through a network thing that gives you access to a way to solve a problem that wasn't there before. EL: Nice. And so do you remember, like really loving this theorem when you first saw it? Or is it something where your appreciation has grown over time? LM: Ah, there's probably — now I;ve got to remember where I saw it. I'm going to guess I saw it in an algorithms class in my PhD. Yeah, I don't know. I don't know that I appreciated it at the time. I feel like a lot of the beginning of my math career was like, “Okay, this theorem exists, but I'm not sure why I care.” Right? It's taken me much longer to start getting to the like, “Oh, the point is the duality. The point is accessing a problem from different sorts of viewpoints.” And having the ability to sort of question the vantage that you're looking at something from because there might be a better way of doing something. And “better” here is now a subjective term, right? Like, mathematically, the theorem says, I don't care. But practically, right, there is a better and a worse way of doing something. And so you're going to end up having choices, as you go about doing math research, that are going to be better or worse in a practical sense, KK: So it's clear listening to you that you work in an interdisciplinary environment, right? Because, you know, the mathematician loves the duality, but you know, practically, the engineer wants to find the most efficient way to do it. Yeah, very cool. EL: So I wanted to come back to something you said a little while ago, and I think it just percolated through my brain. So you said that, like, if your your capacities on these are all integers, then the process terminates. And the fact that you needed that “if” actually just made me really nervous. So is it the case that if you don't have integers, and possibly even rational numbers or something like this? So like, maybe if you had like a bunch of irrational or transcendental values on these, that it might not terminate? And then you’d be sad. LM: Yep, yep, yep. Yeah, then you'd be sad. EL: Or maybe you would be sad. I don't know. LM: Well, yeah. Okay. So I actually, I'm not sure of the answer if it's rational. I know that if you allow irrational values on the edges, you can definitely have examples that don't terminate. And basically what ends up happening — okay, so the way i…

    Full show notes at the publisher

    Episode 66 - Érika Roldán May 15, 2021
    Show notes

    Episode 66 - Érika Roldán Kevin Knudson & Evelyn Lamb Download Evelyn Lamb: Hello and welcome to my favorite theorem, the math podcast with no quiz at the end. I'm Evelyn Lamb. I am 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. How are you, Evelyn? EL: I’m all right. It's been raining a little here, which is very good, because we are in a perhaps somewhat historic drought and every bit of moisture we can get is fantastic. Probably not very close to your experience in Florida right now. KK: It’s been pretty dry. But yeah, it's not really an issue for us. I mean, it’s actually really lovely right now, and I'm looking forward to kayaking some this week. EL: Oh, fun. KK: And my son graduates college in two weeks. And yeah, all kinds of fun stuff on the horizon for us. So anyway, let's talk math, though. EL: That is exciting. Yes. We are very happy today to have Érika Roldan joining us. So yes, Érika, would you like to introduce yourself? Érika Roldán: Yeah, thank you. Thank you very much for the invitation. I'm super happy to be here. And, well, I'm a postdoc right now. I finished my PhD thesis in 2018. And then I started jumping here and there, from Mexico to the states and now Europe. I’m at Munich, Technical University of Munich, and École polytechnique fédérale de Lausanne in Switzerland is my co-host. And yeah, I have this fellowship, the Marie Curie fellowship, for 11 more months, and then jumping again. Yes. EL: Yeah. Well, that's very exciting. And what is your field of research? ÉR: Well, it's stochastic topology and topological and geometric data analysis. I think most of the time, most of the brain time, goes to that. But also, there is something that is related because it gives extremal examples — you will never see them typically when you're using kind of random processes — but these extremal examples allow is to contrast with random ones, so I also do extremal topological combinatorics a bit. EL: Okay, and I also am familiar with some work that you've done in recreational mathematics, which I guess might have to do with this extremal combinatorics too. And so if I can self-promote and Érika-promote a little bit, a couple of the puzzles that Érika has worked on appeared in the mathematics-themed calendar that I put together a couple years ago, which is still available for purchase through the bookstore of the American Mathematical Society and which is not specific to a year, so you can still enjoy this calendar whatever year it is when you are listening to this episode. So anyway, yeah, you did a couple of fun puzzles. I don't know if you want to talk about any of those. I am actually blanking right now on there's one with like, polyomino things, right? ÉR: Yes. So there are two. One of them actually has a very special place in my heart because it was my first paper, and I wrote it for the Gathering for Gardner, this meeting that is every two years in Atlanta. It is a wonderful meeting. It is the first mathematical community, research community, that I got in contact with. And yeah, I did a complete analysis and characterization of a type of puzzles with colored cubes, and you have to stack them, and you have to do a tower and have some interesting coloring properties. And the most famous one is called Instant Insanity, just to give you the name. The name has a little bit of a clue of how interesting it is to try to solve them by trial and error. And yeah, I guess I did some computations and everything to characterize all possible different kinds of puzzles like this. And, yes, that's one of the of the entries of the calendar, this this wonderful calendar. Thank you for sending it to me. I enjoy it very much. And yes, and the other one was about maximally many holes with polynomials. EL: Yes, that’s right. ÉR: And for sure, we're going to go back to polynomials. Because it's related with the story that I want to talk about today about my favorite theorem. EL: Yes. And you've provided the perfect segue now. What is your favorite theorem? ÉR: Yes. Well, first of all, I want to say that I was thinking, and I changed my mind different times during the past two weeks. And I decided that I wanted to talk today about my favorite theorem, choosing it in particular for my mother to be able to follow the podcast. So today's the 10th of May. And, yes, because of the corona crisis, I think a lot of people have not been able to travel, and I haven't seen my mother for more than, almost a year and a half. And this is a way of sending her all my love and appreciation. So my favorite theorem, okay, so one of the things that my mom used to do, or does still very, very well, is shuffling cards. So every time that we gather together with my grandma, and in Mexico, this is very common that you gather with your family very often. It doesn't matter what, you always want to be in a huge crowd, just eating and playing. And yeah, she's a very good shuffler of cards. And my favorite theorem is a theorem by Perci Diaconis, the theorem for today is by Persi Diaconis, where he proves, and actually there is like a set of different papers that have different ways of modeling this shuffling, the usual shuffling that we know, and they prove how many shuffles you need to do to be a sampling from a fairly uniform distribution from all possible ways of having 52 cards of a deck in a certain order. KK: This is a very famous result, and it's a surprising number, you're gonna tell us the number, right? ÉR: Actually, it depends. It depends on the distribution that we use. And then this depends on the algorithm. And but yes, it's a very famous theorem. And it's very well-appreciated by by mathemagicians. Persi Diaconis is one of the most well-known mathemagicians. KK: I have to say, I'm not surprised this is your favorite theorems. I've been at various TDA [topological data analysis] conferences with you. And topologists like to play fast and loose with distributions, and you are always quizzing us about which distribution we're trying to use. It's always “Wait a minute, wait a minute, what's the distribution?” EL: So, KK: Go ahead, Evelyn. EL: Yeah. Um, so for someone who has not thought about card shuffling a whole lot, what do you mean by different distributions? ÉR: So let me withdraw the word “distributions” for the first part. EL: Okay. ÉR: So you want to play poker or any other game, and you want to shuffle the cards in such a way that you feel comfortable betting $100. So, I have the deck of cards. And let's say that I decide to shuffle like this: I take the uppermost card, I take it, and then I put it in one of the possible positions that is not on top, or could be on top actually. And I choose where to put it uniformly randomly. That means I select one of the possible 51 spots, 52 spots, and I put it there. And let's say that I do this five times. Question for both of you: Will you bet $100 with me in this poker game? KK: Uh, no. EL: Probably not. KK: It doesn’t feel very well-shuffled. ÉR: Exactly. And I think no one will do it. Like, it doesn't matter if you know or not probability. We have a sense of when things are shuffled, even if we haven't heard the word “probability.” And so, let me just tell you that actually, for doing this, if I do this 10 minutes, you will start getting more and more comfortable, right? EL: Yeah. ÉR: At some point, you can say, “Okay, let's play.” That's enough after an hour, right? And the solution, or roughly the number from starting from which you feel comfortable and theoretically can be proven that is the kind of right stopping time, is around 200 shuffles. EL: So this is not a very efficient way to shuffle, in case that wasn't already clear to everyone. One card at a time. Not so good. ÉR: Yeah, well, in a Mexican family it will be okay, because everyone will be talking everything. No one will get, just, bored. But perhaps if you are there just for playing, it's not the right way to go. Now, in general, I want to say here, the right rate. In general, if you have n cards, with this shuffling you have to wait nlogn time. So n times logn is the rate that with this kind of shuffle will give you a uniform distribution as a convergence, and when you start to feeling that yeah, it is uniformly sampled. Now let's compare these to the actual way that my mom shuffles the cards. And this is commonly referred to as a riffle shuffle. So you take the deck of cards, and then you kind of try to cut it in into two packs, half perhaps. And then what you do is you hold each one of the packets with each one of your hands. And then you make — I’m trying to generate a mental image that everyone knows — and then you start with your hands, like, trying to put one after the other one, alternating from the packets from one and the other one. And there are different things to do here to convince you that this is for sure a different way of shuffling than the first one. That is one thing, and the other thing is, so if I do it one time, perhaps you will not be very happy. But whenever we're playing with friends usually is what? Five times, six times. EL: Yeah. KK: It depends on how many times I drop them. You know, when you do it, you do it the down and then you do that little flip where you reverse the cards to get that little “shhhh” sound. It took me for—I was an adult before I could finally ever do that. But then a few cards always go flying, right? ÉR: And this is one of the things that I love seeing my mom doing because I think she has some secrets that she doesn't tell us because she does it so well. Like amazingly well. KK: My mom was good at that too, actually. That's interesting. Okay. ÉR: We have these two different ways of shuffling. And we we kind of feel that, or we're comfortable with saying a riffle shuffle shuffles faster than the other way. And so now I want to use to use this shuffling perspective to go and shuffle other mathematical structures. Because what happens is that I ended up using this way of shuffling objects, or mathematical entities, in my research. And that was a very pleasant outcome of my PhD thesis. Yeah. EL: So you're saying that like with these polyominoes with holes and stuff, there's some way to think about shuffling polyominoes, or do you shuffle entire configurations of them? ÉR: Yes, exactly. Yes. It's super fun. So okay, let me start with Tetris. EL: Okay. ÉR: I think if we start with Tetris, everyone who's listening to us will go directly to the picture, the image of what is a polyomino. EL: Oh, that's right. Yeah, we didn't actually say what a polyomino is. So in Tetris, you know that each picture is made up of four blocks. And so that's a tetromino is. So it's like a polyomino, I guess, is something that's made up of any number of these blocks. ÉR: Yes, yes. Exactly. So. I think my, my favorite definition of a polyomino was the first mathematical definition given by Solomon Golomb when he was a PhD student. He gave a talk, and he said, well, a polyomino is a rook-wise, connected subset of squares of the infinite checkerboard. EL: Okay. KK: Rook-wise. ÉR: So you can imagine you select a finite number of squares, and then you place a rook in any one of the squares, and you have to be able, with only rows and column moves, to go and visit any other square of the structure. So rook-wise connected. EL: Yup. ÉR: Yeah. And with Tetris, what happens is that we — let's now come back to to the mindset of sampling. And when we're playing Tetris, there is some entity that is giving us one polyomino after another one. Question: I don't know if people have thought about this. But it's a fun thing to do. I did one morning, and I was like, “I don't know.” Yes. So how is this done? How does the game decide how to give you the next polyomino? KK: It’s not just random? EL: Yeah, I guess I assumed it was — there was a however many Tetris pieces there are. I actually can't think off the top of my head, but six or seven, maybe different pieces? ÉR: Seven. EL: Okay, then just so a 1/7 chance of each one, is that not the case? ÉR: Well, you soon will be able to play the game that you're describing, because I'm programming a version with uniform distribution, just to make people really mad and upset. EL: Okay. KK: I can see, actually, now that I think about it, if it were just random, uniform, that would be bad. You’d lose pretty quickly, right? I mean, now that I think about it, often you'll get that long, the straight four in a row right when you need it, right? And and if it were uniform, you wouldn't necessarily expect that to happen. ÉR: It could, you could have bad luck. Yeah. You could have had as long as you want bad luck. KK: Right? Sure. That's right. ÉR: Right. So the algorithm is very interesting. What they do is, they take the seven, the possible seven polyominoes with four squares that they give, and then they shuffle these seven in a way that you have a one over seven factorial, each one of the possibilities of the order has one over seven factorial, that means it’s a uniformly distributed sample. Okay, so what what can happen? Well, the worst that you can wait for that “I” shape is that if you have at the beginning of a set of seven, and then in the next set of seven, you have it at the end. That’s the worst that can happen in terms of these wonderful pieces that allow you to kill four rows. KK: Interesting. Okay, so they're just choosing a random shuffle. Oh, wow. Well, that is better. ÉR: It is for playing. EL: I guess with, like, computer games like this. We, as you're saying earlier, Kevin, we don't actually want it completely random because we don't want to get, you know, five squares in a row or something like this, which can definitely happen when you play it randomly. Especially when you're me and my brother when we were, like seven and nine years old, always going in and playing for two hours to try to erase the other ones’ high scores. KK: I still remember, so when I was in graduate school, I had a Nintendo Gameboy. My mother gave me one of these, and I played Tetris. And I can still hear that song. The music that went along with it, and I would go to sleep at night picturing these damn pieces just falling. I can hear this song over and over and over my head. ÉR: That is a thing I think that happened when we were doing a PhD because I was always with my Nintendo playing Tetris and procrastinating, beautifully procrastinating. And it turns out. EL: I have a question. How do you — like, did you find how Tetris is programmed? Or did you just do some — Like, how did you figure out that this is how Tetris works? KK: Yeah. ÉR: Well, so I started reading about it. But first of all, I started playing, and I started noticing this pattern, right? This pattern, because you never see — I think, I think what what tells you immediately that it cannot be a random distribution, uniform random period, is that you will never see three times the the same shape. EL: Okay, you could see two in a row, if it happened, right at the end and then the beginning, but you can't see three in a row. ÉR: You can’t see three in a row! So this is one of the immediate signs. So whenever you don't see three in a row, and you have a decently small set — and we're going to go back on the size of the set, because it plays a role. So it plays a role, the distribution that you have in the set, and it plays a role also the size of the set. And so in this case, because it's such a small set, not seeing three i…

    Full show notes at the publisher

    Previous 1 2 3 4 5 10 Next

    Related Podcasts

    More or Less

    1

    More or Less Business
    Breaking Math Podcast

    2

    Breaking Math Podcast Mathematics
    Relatively Prime: Stories from the Mathematical Domain

    3

    Relatively Prime: Stories from the Mathematical Domain Mathematics
    Sum of All Parts

    4

    Sum of All Parts Mathematics
    Opinionated History of Mathematics

    5

    Opinionated History of Mathematics Mathematics
    Casual Inference

    6

    Casual Inference Mathematics
    footer-logo

    Contact Us

    Toll Free: 844-670-7747

    Links

    • Home
    • Top Charts
    • Networks
    • Apps
    • Independents Podcasts
    • Podcast Advertising
    • Podcast News
    • Contact Us
    • About Us
    • Analytics & Insights

    Stay Connected

      Privacy, Terms of Use & Our Code of Ethics Protecting Content Creators Copyrights