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  • Joseph Nebus 6:00 pm on Wednesday, 16 August, 2017 Permalink | Reply
    Tags: , , , polynomials, rank,   

    The Summer 2017 Mathematics A To Z: Height Function (elliptic curves) 


    I am one letter closer to the end of Gaurish’s main block of requests. They’re all good ones, mind you. This gets me back into elliptic curves and Diophantine equations. I might be writing about the wrong thing.

    Height Function.

    My love’s father has a habit of asking us to rate our hobbies. This turned into a new running joke over a family vacation this summer. It’s a simple joke: I shuffled the comparables. “Which is better, Bon Jovi or a roller coaster?” It’s still a good question.

    But as genial yet nasty as the spoof is, my love’s father asks natural questions. We always want to compare things. When we form a mathematical construct we look for ways to measure it. There’s typically something. We’ll put one together. We call this a height function.

    We start with an elliptic curve. The coordinates of the points on this curve satisfy some equation. Well, there are many equations they satisfy. We pick one representation for convenience. The convenient thing is to have an easy-to-calculate height. We’ll write the equation for the curve as

    y^2 = x^3 + Ax + B

    Here both ‘A’ and ‘B’ are some integers. This form might be unique, depending on whether a slightly fussy condition on prime numbers hold. (Specifically, if ‘p’ is a prime number and ‘p4‘ divides into ‘A’, then ‘p6‘ must not divide into ‘B’. Yes, I know you realized that right away. But I write to a general audience, some of whom are learning how to see these things.) Then the height of this curve is whichever is the larger number, four times the cube of the absolute value of ‘A’, or 27 times the square of ‘B’. I ask you to just run with it. I don’t know the implications of the height function well enough to say why, oh, 25 times the square of ‘B’ wouldn’t do as well. The usual reason for something like that is that some obvious manipulation makes the 27 appear right away, or disappear right away.

    This idea of height feeds in to a measure called rank. “Rank” is a term the young mathematician encounters first while learning matrices. It’s the number of rows in a matrix that aren’t equal to some sum or multiple of other rows. That is, it’s how many different things there are among a set. You can see why we might find that interesting. So many topics have something called “rank” and it measures how many different things there are in a set of things. In elliptic curves, the rank is a measure of how complicated the curve is. We can imagine the rational points on the elliptic curve as things generated by some small set of starter points. The starter points have to be of infinite order. Starter points that don’t, don’t count for the rank. Please don’t worry about what “infinite order” means here. I only mention this infinite-order business because if I don’t then something I have to say about two paragraphs from here will sound daft. So, the rank is how many of these starter points you need to generate the elliptic curve. (WARNING: Call them “generating points” or “generators” during your thesis defense.)

    There’s no known way of guessing what the rank is if you just know ‘A’ and ‘B’. There are algorithms that can calculate the rank given a particular ‘A’ and ‘B’. But it’s not something like the quadratic formula where you can just do a quick calculation and know what you’re looking for. We don’t even know if the algorithms we have will work for every elliptic curve.

    We think that there’s no limit to the height of elliptic curves. We don’t know this. We know there exist curves with ranks as high as 28. They seem to be rare [*]. I don’t know if that’s proven. But we do know there are elliptic curves with rank zero. A lot of them, in fact. (See what I meant two paragraphs back?) These are the elliptic curves that have only finitely many rational points on them.

    And there’s a lot of those. There’s a well-respected that the average rank, of all the elliptic curves there are, is ½. It might be. What we have been able to prove is that the average rank is less than or equal to 1.17. Also that it should be larger than zero. So we’re maybe closing in on the ½ conjecture? At least we know something. I admit this essay I’ve started wondering what we do know of elliptic curves.

    What do the height, and through it the rank, get us? I worry I’m repeating myself. By themselves they give us families of elliptic curves. Shapes that are similar in a particular and not-always-obvious way. And they feed into the Birch and Swinnerton-Dyer conjecture, which is the hipster’s Riemann Hypothesis. That is, it’s this big, unanswered, important problem that would, if answered, tell us things about a lot of questions that I’m not sure can be concisely explained. At least not why they’re interesting. We know some special cases, at least. Wikipedia tells me nothing’s proved for curves with rank greater than 1. Humanity’s ignorance on this point makes me feel slightly better pondering what I don’t know about elliptic curves.

    (There are some other things within the field of elliptic curves called height functions. There’s particularly a height of individual points. I was unsure which height Gaurish found interesting so chose one. The other starts by measuring something different; it views, for example, \frac{1}{2} as having a lower height than does \frac{51}{101} , even though the numbers are quite close in value. It develops along similar lines, trying to find classes of curves with similar behavior. And it gets into different unsolved conjectures. We have our ideas about how to think of fields.).


    [*] Wikipedia seems to suggest we only know of one, provided by Professor Noam Elkies in 2006, and let me quote it in full. I apologize that it isn’t in the format I suggested at top was standard. Elkies way outranks me academically so we have to do things his way:

    y^2 + xy + y = x^3 - x^2 -  20,067,762,415,575,526,585,033,208,209,338,542,750,930,230,312,178,956,502 x + 34,481,611,795,030,556,467,032,985,690,390,720,374,855,944,359,319,180,361,266,008,296,291,939,448,732,243,429

    I can’t figure how to get WordPress to present that larger. I sympathize. I’m tired just looking at an equation like that. This page lists records of known elliptic curve ranks. I don’t know if the lack of any records more recent than 2006 reflects the page not having been updated or nobody having found a rank-29 curve. I fully accept the field might be more difficult than even doing maintenance on a web page’s content is.

     
  • Joseph Nebus 6:00 pm on Monday, 7 August, 2017 Permalink | Reply
    Tags: , , , , , , polynomials, ,   

    The Summer 2017 Mathematics A To Z: Diophantine Equations 


    I have another request from Gaurish, of the For The Love Of Mathematics blog, today. It’s another change of pace.

    Diophantine Equations

    A Diophantine equation is a polynomial. Well, of course it is. It’s an equation, or a set of equations, setting one polynomial equal to another. Possibly equal to a constant. What makes this different from “any old equation” is the coefficients. These are the constant numbers that you multiply the variables, your x and y and x2 and z8 and so on, by. To make a Diophantine equation all these coefficients have to be integers. You know one well, because it’s that x^n + y^n = z^n thing that Fermat’s Last Theorem is all about. And you’ve probably seen ax + by = 1 . It turns up a lot because that’s a line, and we do a lot of stuff with lines.

    Diophantine equations are interesting. There are a couple of cases that are easy to solve. I mean, at least that we can find solutions for. ax + by = 1 , for example, that’s easy to solve. x^n + y^n = z^n it turns out we can’t solve. Well, we can if n is equal to 1 or 2. Or if x or y or z are zero. These are obvious, that is, they’re quite boring. That one took about four hundred years to solve, and the solution was “there aren’t any solutions”. This may convince you of how interesting these problems are. What, from looking at it, tells you that ax + by = 1 is simple while x^n + y^n = z^n is (most of the time) impossible?

    I don’t know. Nobody really does. There are many kinds of Diophantine equation, all different-looking polynomials. Some of them are special one-off cases, like x^n + y^n = z^n . For example, there’s x^4 + y^4 + z^4 = w^4 for some integers x, y, z, and w. Leonhard Euler conjectured this equation had only boring solutions. You’ll remember Euler. He wrote the foundational work for every field of mathematics. It turns out he was wrong. It has infinitely many interesting solutions. But the smallest one is 2,682,440^4 + 15,365,639^4 + 18,796,760^4 = 20,615,673^4 and that one took a computer search to find. We can forgive Euler not noticing it.

    Some are groups of equations that have similar shapes. There’s the Fermat’s Last Theorem formula, for example, which is a different equation for every different integer n. Then there’s what we call Pell’s Equation. This one is x^2 - D y^2 = 1 (or equals -1), for some counting number D. It’s named for the English mathematician John Pell, who did not discover the equation (even in the Western European tradition; Indian mathematicians were familiar with it for a millennium), did not solve the equation, and did not do anything particularly noteworthy in advancing human understanding of the solution. Pell owes his fame in this regard to Leonhard Euler, who misunderstood Pell’s revising a translation of a book discussing a solution for Pell’s authoring a solution. I confess Euler isn’t looking very good on Diophantine equations.

    But nobody looks very good on Diophantine equations. Make up a Diophantine equation of your own. Use whatever whole numbers, positive or negative, that you like for your equation. Use whatever powers of however many variables you like for your equation. So you get something that looks maybe like this:

    7x^2 - 20y + 18y^2 - 38z = 9

    Does it have any solutions? I don’t know. Nobody does. There isn’t a general all-around solution. You know how with a quadratic equation we have this formula where you recite some incantation about “b squared minus four a c” and get any roots that exist? Nothing like that exists for Diophantine equations in general. Specific ones, yes. But they’re all specialties, crafted to fit the equation that has just that shape.

    So for each equation we have to ask: is there a solution? Is there any solution that isn’t obvious? Are there finitely many solutions? Are there infinitely many? Either way, can we find all the solutions? And we have to answer them anew. What answers these have? Whether answers are known to exist? Whether answers can exist? We have to discover anew for each kind of equation. Knowing answers for one kind doesn’t help us for any others, except as inspiration. If some trick worked before, maybe it will work this time.

    There are a couple usually reliable tricks. Can the equation be rewritten in some way that it becomes the equation for a line? If it can we probably have a good handle on any solutions. Can we apply modulo arithmetic to the equation? If it is, we might be able to reduce the number of possible solutions that the equation has. In particular we might be able to reduce the number of possible solutions until we can just check every case. Can we use induction? That is, can we show there’s some parameter for the equations, and that knowing the solutions for one value of that parameter implies knowing solutions for larger values? And then find some small enough value we can test it out by hand? Or can we show that if there is a solution, then there must be a smaller solution, and smaller yet, until we can either find an answer or show there aren’t any? Sometimes. Not always. The field blends seamlessly into number theory. And number theory is all sorts of problems easy to pose and hard or impossible to solve.

    We name these equation after Diophantus of Alexandria, a 3rd century Greek mathematician. His writings, what we have of them, discuss how to solve equations. Not general solutions, the way we might want to solve ax^2 + bx + c = 0 , but specific ones, like 1x^2 - 5x + 6 = 0 . His books are among those whose rediscovery shaped the rebirth of mathematics. Pierre de Fermat’s scribbled his famous note in the too-small margins of Diophantus’s Arithmetica. (Well, a popular translation.)

    But the field predates Diophantus, at least if we look at specific problems. Of course it does. In mathematics, as in life, any search for a source ends in a vast, marshy ambiguity. The field stays vital. If we loosen ourselves to looking at inequalities — x - Dy^2 < A , let's say — then we start seeing optimization problems. What values of x and y will make this equation most nearly true? What values will come closest to satisfying this bunch of equations? The questions are about how to find the best possible fit to whatever our complicated sets of needs are. We can't always answer. We keep searching.

     
  • Joseph Nebus 4:00 pm on Wednesday, 7 June, 2017 Permalink | Reply
    Tags: , , , , polynomials   

    What Second Derivatives Are And What They Can Do For You 


    Previous supplemental reading for Why Stuff Can Orbit:


    This is another supplemental piece because it’s too much to include in the next bit of Why Stuff Can Orbit. I need some more stuff about how a mathematical physicist would look at something.

    This is also a story about approximations. A lot of mathematics is really about approximations. I don’t mean numerical computing. We all know that when we compute we’re making approximations. We use 0.333333 instead of one-third and we use 3.141592 instead of π. But a lot of precise mathematics, what we call analysis, is also about approximations. We do this by a logical structure that works something like this: take something we want to prove. Now for every positive number ε we can find something — a point, a function, a curve — that’s no more than ε away from the thing we’re really interested in, and which is easier to work with. Then we prove whatever we want to with the easier-to-work-with thing. And since ε can be as tiny a positive number as we want, we can suppose ε is a tinier difference than we can hope to measure. And so the difference between the thing we’re interested in and the thing we’ve proved something interesting about is zero. (This is the part that feels like we’re pulling a scam. We’re not, but this is where it’s worth stopping and thinking about what we mean by “a difference between two things”. When you feel confident this isn’t a scam, continue.) So we proved whatever we proved about the thing we’re interested in. Take an analysis course and you will see this all the time.

    When we get into mathematical physics we do a lot of approximating functions with polynomials. Why polynomials? Yes, because everything is polynomials. But also because polynomials make so much mathematical physics easy. Polynomials are easy to calculate, if you need numbers. Polynomials are easy to integrate and differentiate, if you need analysis. Here that’s the calculus that tells you about patterns of behavior. If you want to approximate a continuous function you can always do it with a polynomial. The polynomial might have to be infinitely long to approximate the entire function. That’s all right. You can chop it off after finitely many terms. This finite polynomial is still a good approximation. It’s just good for a smaller region than the infinitely long polynomial would have been.

    Necessary qualifiers: pages 65 through 82 of any book on real analysis.

    So. Let me get to functions. I’m going to use a function named ‘f’ because I’m not wasting my energy coming up with good names. (When we get back to the main Why Stuff Can Orbit sequence this is going to be ‘U’ for potential energy or ‘E’ for energy.) It’s got a domain that’s the real numbers, and a range that’s the real numbers. To express this in symbols I can write f: \Re \rightarrow \Re . If I have some number called ‘x’ that’s in the domain then I can tell you what number in the domain is matched by the function ‘f’ to ‘x’: it’s the number ‘f(x)’. You were expecting maybe 3.5? I don’t know that about ‘f’, not yet anyway. The one thing I do know about ‘f’, because I insist on it as a condition for appearing, is that it’s continuous. It hasn’t got any jumps, any gaps, any regions where it’s not defined. You could draw a curve representing it with a single, if wriggly, stroke of the pen.

    I mean to build an approximation to the function ‘f’. It’s going to be a polynomial expansion, a set of things to multiply and add together that’s easy to find. To make this polynomial expansion this I need to choose some point to build the approximation around. Mathematicians call this the “point of expansion” because we froze up in panic when someone asked what we were going to name it, okay? But how are we going to make an approximation to a function if we don’t have some particular point we’re approximating around?

    (One answer we find in grad school when we pick up some stuff from linear algebra we hadn’t been thinking about. We’ll skip it for now.)

    I need a name for the point of expansion. I’ll use ‘a’. Many mathematicians do. Another popular name for it is ‘x0‘. Or if you’re using some other variable name for stuff in the domain then whatever that variable is with subscript zero.

    So my first approximation to the original function ‘f’ is … oh, shoot, I should have some new name for this. All right. I’m going to use ‘F0‘ as the name. This is because it’s one of a set of approximations, each of them a little better than the old. ‘F1‘ will be better than ‘F0‘, but ‘F2‘ will be even better, and ‘F2038‘ will be way better yet. I’ll also say something about what I mean by “better”, although you’ve got some sense of that already.

    I start off by calling the first approximation ‘F0‘ by the way because you’re going to think it’s too stupid to dignify with a number as big as ‘1’. Well, I have other reasons, but they’ll be easier to see in a bit. ‘F0‘, like all its sibling ‘Fn‘ functions, has a domain of the real numbers and a range of the real numbers. The rule defining how to go from a number ‘x’ in the domain to some real number in the range?

    F^0(x) = f(a)

    That is, this first approximation is simply whatever the original function’s value is at the point of expansion. Notice that’s an ‘x’ on the left side of the equals sign and an ‘a’ on the right. This seems to challenge the idea of what an “approximation” even is. But it’s legit. Supposing something to be constant is often a decent working assumption. If you failed to check what the weather for today will be like, supposing that it’ll be about like yesterday will usually serve you well enough. If you aren’t sure where your pet is, you look first wherever you last saw the animal. (Or, yes, where your pet most loves to be. A particular spot, though.)

    We can make this rigorous. A mathematician thinks this is rigorous: you pick any margin of error you like. Then I can find a region near enough to the point of expansion. The value for ‘f’ for every point inside that region is ‘f(a)’ plus or minus your margin of error. It might be a small region, yes. Doesn’t matter. It exists, no matter how tiny your margin of error was.

    But yeah, that expansion still seems too cheap to work. My next approximation, ‘F1‘, will be a little better. I mean that we can expect it will be closer than ‘F0‘ was to the original ‘f’. Or it’ll be as close for a bigger region around the point of expansion ‘a’. What it’ll represent is a line. Yeah, ‘F0‘ was a line too. But ‘F0‘ is a horizontal line. ‘F1‘ might be a line at some completely other angle. If that works better. The second approximation will look like this:

    F^1(x) = f(a) + m\cdot\left(x - a\right)

    Here ‘m’ serves its traditional yet poorly-explained role as the slope of a line. What the slope of that line should be we learn from the derivative of the original ‘f’. The derivative of a function is itself a new function, with the same domain and the same range. There’s a couple ways to denote this. Each way has its strengths and weaknesses about clarifying what we’re doing versus how much we’re writing down. And trying to write down almost anything can inspire confusion in analysis later on. There’s a part of analysis when you have to shift from thinking of particular problems to how problems work then.

    So I will define a new function, spoken of as f-prime, this way:

    f'(x) = \frac{df}{dx}\left(x\right)

    If you look closely you realize there’s two different meanings of ‘x’ here. One is the ‘x’ that appears in parentheses. It’s the value in the domain of f and of f’ where we want to evaluate the function. The other ‘x’ is the one in the lower side of the derivative, in that \frac{df}{dx} . That’s my sloppiness, but it’s not uniquely mine. Mathematicians keep this straight by using the symbols \frac{df}{dx} so much they don’t even see the ‘x’ down there anymore so have no idea there’s anything to find confusing. Students keep this straight by guessing helplessly about what their instructors want and clinging to anything that doesn’t get marked down. Sorry. But what this means is to “take the derivative of the function ‘f’ with respect to its variable, and then, evaluate what that expression is for the value of ‘x’ that’s in parentheses on the left-hand side”. We can do some things that avoid the confusion in symbols there. They all require adding some more variables and some more notation in, and it looks like overkill for a measly definition like this.

    Anyway. We really just want the deriviate evaluated at one point, the point of expansion. That is:

    m = f'(a) = \frac{df}{dx}\left(a\right)

    which by the way avoids that overloaded meaning of ‘x’ there. Put this together and we have what we call the tangent line approximation to the original ‘f’ at the point of expansion:

    F^1(x) = f(a) + f'(a)\cdot\left(x - a\right)

    This is also called the tangent line, because it’s a line that’s tangent to the original function. A plot of ‘F1‘ and the original function ‘f’ are guaranteed to touch one another only at the point of expansion. They might happen to touch again, but that’s luck. The tangent line will be close to the original function near the point of expansion. It might happen to be close again later on, but that’s luck, not design. Most stuff you might want to do with the original function you can do with the tangent line, but the tangent line will be easier to work with. It exactly matches the original function at the point of expansion, and its first derivative exactly matches the original function’s first derivative at the point of expansion.

    We can do better. We can find a parabola, a second-order polynomial that approximates the original function. This will be a function ‘F2(x)’ that looks something like:

    F^2(x) = f(a) + f'(a)\cdot\left(x - a\right) + \frac12 m_2 \left(x - a\right)^2

    What we’re doing is adding a parabola to the approximation. This is that curve that looks kind of like a loosely-drawn U. The ‘m2‘ there measures how spread out the U is. It’s not quite the slope, but it’s kind of like that, which is why I’m using the letter ‘m’ for it. Its value we get from the second derivative of the original ‘f’:

    m_2 = f''(a) = \frac{d^2f}{dx^2}\left(a\right)

    We find the second derivative of a function ‘f’ by evaluating the first derivative, and then, taking the derivative of that. We can denote it with two ‘ marks after the ‘f’ as long as we aren’t stuck wrapping the function name in ‘ marks to set it out. And so we can describe the function this way:

    F^2(x) = f(a) + f'(a)\cdot\left(x - a\right) + \frac12 f''(a) \left(x - a\right)^2

    This will be a better approximation to the original function near the point of expansion. Or it’ll make larger the region where the approximation is good.

    If the first derivative of a function at a point is zero that means the tangent line is horizontal. In physics stuff this is an equilibrium. The second derivative can tell us whether the equilibrium is stable or not. If the second derivative at the equilibrium is positive it’s a stable equilibrium. The function looks like a bowl open at the top. If the second derivative at the equilibrium is negative then it’s an unstable equilibrium.

    We can make better approximations yet, by using even more derivatives of the original function ‘f’ at the point of expansion:

    F^3(x) = f(a) + f'(a)\cdot\left(x - a\right) + \frac12 f''(a) \left(x - a\right)^2 + \frac{1}{3\cdot 2} f'''(a) \left(x - a\right)^3

    There’s better approximations yet. You can probably guess what the next, fourth-degree, polynomial would be. Or you can after I tell you the fraction in front of the new term will be \frac{1}{4\cdot 3\cdot 2} . The only big difference is that after about the third derivative we give up on adding ‘ marks after the function name ‘f’. It’s just too many little dots. We start writing, like, ‘f(iv)‘ instead. Or if the Roman numerals are too much then ‘f(2038)‘ instead. Or if we don’t want to pin things down to a specific value ‘f(j)‘ with the understanding that ‘j’ is some whole number.

    We don’t need all of them. In physics problems we get equilibriums from the first derivative. We get stability from the second derivative. And we get springs in the second derivative too. And that’s what I hope to pick up on in the next installment of the main series.

     
    • elkement (Elke Stangl) 4:20 pm on Wednesday, 7 June, 2017 Permalink | Reply

      This is great – I’ve just written a very short version of that (a much too succinct one) … as an half-hearted attempt to explain Taylor expansions that I need in an upcoming post. But now I won’t feel bad anymore about its incomprehensibility and simply link to this post of yours :-)

      Like

  • Joseph Nebus 6:00 pm on Thursday, 29 December, 2016 Permalink | Reply
    Tags: , China, , , , , Mersenne numbers, , polynomials,   

    The End 2016 Mathematics A To Z: Yang Hui’s Triangle 


    Today’s is another request from gaurish and another I’m glad to have as it let me learn things too. That’s a particularly fun kind of essay to have here.

    Yang Hui’s Triangle.

    It’s a triangle. Not because we’re interested in triangles, but because it’s a particularly good way to organize what we’re doing and show why we do that. We’re making an arrangement of numbers. First we need cells to put the numbers in.

    Start with a single cell in what’ll be the top middle of the triangle. It spreads out in rows beneath that. The rows are staggered. The second row has two cells, each one-half width to the side of the starting one. The third row has three cells, each one-half width to the sides of the row above, so that its center cell is directly under the original one. The fourth row has four cells, two of which are exactly underneath the cells of the second row. The fifth row has five cells, three of them directly underneath the third row’s cells. And so on. You know the pattern. It’s the one that pins in a plinko board take. Just trimmed down to a triangle. Make as many rows as you find interesting. You can always add more later.

    In the top cell goes the number ‘1’. There’s also a ‘1’ in the leftmost cell of each row, and a ‘1’ in the rightmost cell of each row.

    What of interior cells? The number for those we work out by looking to the row above. Take the cells to the immediate left and right of it. Add the values of those together. So for example the center cell in the third row will be ‘1’ plus ‘1’, commonly regarded as ‘2’. In the third row the leftmost cell is ‘1’; it always is. The next cell over will be ‘1’ plus ‘2’, from the row above. That’s ‘3’. The cell next to that will be ‘2’ plus ‘1’, a subtly different ‘3’. And the last cell in the row is ‘1’ because it always is. In the fourth row we get, starting from the left, ‘1’, ‘4’, ‘6’, ‘4’, and ‘1’. And so on.

    It’s a neat little arithmetic project. It has useful application beyond the joy of making something neat. Many neat little arithmetic projects don’t have that. But the numbers in each row give us binomial coefficients, which we often want to know. That is, if we wanted to work out (a + b) to, say, the third power, we would know what it looks like from looking at the fourth row of Yanghui’s Triangle. It will be 1\cdot a^4 + 4\cdot a^3 \cdot b^1 + 6\cdot a^2\cdot b^2 + 4\cdot a^1\cdot b^3 + 1\cdot b^4 . This turns up in polynomials all the time.

    Look at diagonals. By diagonal here I mean a line parallel to the line of ‘1’s. Left side or right side; it doesn’t matter. Yang Hui’s triangle is bilaterally symmetric around its center. The first diagonal under the edges is a bit boring but familiar enough: 1-2-3-4-5-6-7-et cetera. The second diagonal is more curious: 1-3-6-10-15-21-28 and so on. You’ve seen those numbers before. They’re called the triangular numbers. They’re the number of dots you need to make a uniformly spaced, staggered-row triangle. Doodle a bit and you’ll see. Or play with coins or pool balls.

    The third diagonal looks more arbitrary yet: 1-4-10-20-35-56-84 and on. But these are something too. They’re the tetrahedronal numbers. They’re the number of things you need to make a tetrahedron. Try it out with a couple of balls. Oranges if you’re bored at the grocer’s. Four, ten, twenty, these make a nice stack. The fourth diagonal is a bunch of numbers I never paid attention to before. 1-5-15-35-70-126-210 and so on. This is — well. We just did tetrahedrons, the triangular arrangement of three-dimensional balls. Before that we did triangles, the triangular arrangement of two-dimensional discs. Do you want to put in a guess what these “pentatope numbers” are about? Sure, but you hardly need to. If we’ve got a bunch of four-dimensional hyperspheres and want to stack them in a neat triangular pile we need one, or five, or fifteen, or so on to make the pile come out neat. You can guess what might be in the fifth diagonal. I don’t want to think too hard about making triangular heaps of five-dimensional hyperspheres.

    There’s more stuff lurking in here, waiting to be decoded. Add the numbers of, say, row four up and you get two raised to the third power. Add the numbers of row ten up and you get two raised to the ninth power. You see the pattern. Add everything in, say, the top five rows together and you get the fifth Mersenne number, two raised to the fifth power (32) minus one (31, when we’re done). Add everything in the top ten rows together and you get the tenth Mersenne number, two raised to the tenth power (1024) minus one (1023).

    Or add together things on “shallow diagonals”. Start from a ‘1’ on the outer edge. I’m going to suppose you started on the left edge, but remember symmetry; it’ll be fine if you go from the right instead. Add to that ‘1’ the number you get by moving one cell to the right and going up-and-right. And then again, go one cell to the right and then one cell up-and-right. And again and again, until you run out of cells. You get the Fibonacci sequence, 1-1-2-3-5-8-13-21-and so on.

    We can even make an astounding picture from this. Take the cells of Yang Hui’s triangle. Color them in. One shade if the cell has an odd number, another if the cell has an even number. It will create a pattern we know as the Sierpiński Triangle. (Wacław Sierpiński is proving to be the surprise special guest star in many of this A To Z sequence’s essays.) That’s the fractal of a triangle subdivided into four triangles with the center one knocked out, and the remaining triangles them subdivided into four triangles with the center knocked out, and on and on.

    By now I imagine even my most skeptical readers agree this is an interesting, useful mathematical construct. Also that they’re wondering why I haven’t said the name “Blaise Pascal”. The Western mathematical tradition knows of this from Pascal’s work, particularly his 1653 Traité du triangle arithmétique. But mathematicians like to say their work is universal, and independent of the mere human beings who find it. Constructions like this triangle give support to this. Yang lived in China, in the 12th century. I imagine it possible Pascal had hard of his work or been influenced by it, by some chain, but I know of no evidence that he did.

    And even if he had, there are other apparently independent inventions. The Avanti Indian astronomer-mathematician-astrologer Varāhamihira described the addition rule which makes the triangle work in commentaries written around the year 500. Omar Khayyám, who keeps appearing in the history of science and mathematics, wrote about the triangle in his 1070 Treatise on Demonstration of Problems of Algebra. Again so far as I am aware there’s not a direct link between any of these discoveries. They are things different people in different traditions found because the tools — arithmetic and aesthetically-pleasing orders of things — were ready for them.

    Yang Hui wrote about his triangle in the 1261 book Xiangjie Jiuzhang Suanfa. In it he credits the use of the triangle (for finding roots) was invented around 1100 by mathematician Jia Xian. This reminds us that it is not merely mathematical discoveries that are found by many peoples at many times and places. So is Boyer’s Law, discovered by Hubert Kennedy.

     
    • gaurish 6:46 pm on Thursday, 29 December, 2016 Permalink | Reply

      This is first time that I have read an article about Pascal triangle without a picture of it in front of me and could still imagine it in my mind. :)

      Like

      • Joseph Nebus 5:22 am on Thursday, 5 January, 2017 Permalink | Reply

        Thank you; I’m glad you like it. I did spend a good bit of time before writing the essay thinking about why it is a triangle that we use for this figure, and that helped me think about how things are organized and why. (The one thing I didn’t get into was identifying the top row, the single cell, as row zero. Computers may index things starting from zero and there may be fair reasons to do it, but that is always going to be a weird choice for humans.)

        Liked by 1 person

  • Joseph Nebus 6:00 pm on Monday, 5 December, 2016 Permalink | Reply
    Tags: , , , , Daffy Duck, , , , polynomials,   

    The End 2016 Mathematics A To Z: Osculating Circle 


    I’m happy to say it’s another request today. This one’s from HowardAt58, author of the Saving School Math blog. He’s given me some great inspiration in the past.

    Osculating Circle.

    It’s right there in the name. Osculating. You know what that is from that one Daffy Duck cartoon where he cries out “Greetings, Gate, let’s osculate” while wearing a moustache. Daffy’s imitating somebody there, but goodness knows who. Someday the mystery drives the young you to a dictionary web site. Osculate means kiss. This doesn’t seem to explain the scene. Daffy was imitating Jerry Colonna. That meant something in 1943. You can find him on old-time radio recordings. I think he’s funny, in that 40s style.

    Make the substitution. A kissing circle. Suppose it’s not some playground antic one level up from the Kissing Bandit that plagues recess yet one or two levels down what we imagine we’d do in high school. It suggests a circle that comes really close to something, that touches it a moment, and then goes off its own way.

    But then touching. We know another word for that. It’s the root behind “tangent”. Tangent is a trigonometry term. But it appears in calculus too. The tangent line is a line that touches a curve at one specific point and is going in the same direction as the original curve is at that point. We like this because … well, we do. The tangent line is a good approximation of the original curve, at least at the tangent point and for some region local to that. The tangent touches the original curve, and maybe it does something else later on. What could kissing be?

    The osculating circle is about approximating an interesting thing with a well-behaved thing. So are similar things with names like “osculating curve” or “osculating sphere”. We need that a lot. Interesting things are complicated. Well-behaved things are understood. We move from what we understand to what we would like to know, often, by an approximation. This is why we have tangent lines. This is why we build polynomials that approximate an interesting function. They share the original function’s value, and its derivative’s value. A polynomial approximation can share many derivatives. If the function is nice enough, and the polynomial big enough, it can be impossible to tell the difference between the polynomial and the original function.

    The osculating circle, or sphere, isn’t so concerned with matching derivatives. I know, I’m as shocked as you are. Well, it matches the first and the second derivatives of the original curve. Anything past that, though, it matches only by luck. The osculating circle is instead about matching the curvature of the original curve. The curvature is what you think it would be: it’s how much a function curves. If you imagine looking closely at the original curve and an osculating circle they appear to be two arcs that come together. They must touch at one point. They might touch at others, but that’s incidental.

    Osculating circles, and osculating spheres, sneak out of mathematics and into practical work. This is because we often want to work with things that are almost circles. The surface of the Earth, for example, is not a sphere. But it’s only a tiny bit off. It’s off in ways that you only notice if you are doing high-precision mapping. Or taking close measurements of things in the sky. Sometimes we do this. So we map the Earth locally as if it were a perfect sphere, with curvature exactly what its curvature is at our observation post.

    Or we might be observing something moving in orbit. If the universe had only two things in it, and they were the correct two things, all orbits would be simple: they would be ellipses. They would have to be “point masses”, things that have mass without any volume. They never are. They’re always shapes. Spheres would be fine, but they’re never perfect spheres even. The slight difference between a perfect sphere and whatever the things really are affects the orbit. Or the other things in the universe tug on the orbiting things. Or the thing orbiting makes a course correction. All these things make little changes in the orbiting thing’s orbit. The actual orbit of the thing is a complicated curve. The orbit we could calculate is an osculating — well, an osculating ellipse, rather than an osculating circle. Similar idea, though. Call it an osculating orbit if you’d rather.

    That osculating circles have practical uses doesn’t mean they aren’t respectable mathematics. I’ll concede they’re not used as much as polynomials or sine curves are. I suppose that’s because polynomials and sine curves have nicer derivatives than circles do. But osculating circles do turn up as ways to try solving nonlinear differential equations. We need the help. Linear differential equations anyone can solve. Nonlinear differential equations are pretty much impossible. They also turn up in signal processing, as ways to find the frequencies of a signal from a sampling of data. This, too, we would like to know.

    We get the name “osculating circle” from Gottfried Wilhelm Leibniz. This might not surprise. Finding easy-to-understand shapes that approximate interesting shapes is why we have calculus. Isaac Newton described a way of making them in the Principia Mathematica. This also might not surprise. Of course they would on this subject come so close together without kissing.

     
  • Joseph Nebus 3:00 pm on Thursday, 30 June, 2016 Permalink | Reply
    Tags: , factorials, , , polynomials, , , ,   

    Theorem Thursday: Liouville’s Approximation Theorem And How To Make Your Own Transcendental Number 


    As I get into the second month of Theorem Thursdays I have, I think, the whole roster of weeks sketched out. Today, I want to dive into some real analysis, and the study of numbers. It’s the sort of thing you normally get only if you’re willing to be a mathematics major. I’ll try to be readable by people who aren’t. If you carry through to the end and follow directions you’ll have your very own mathematical construct, too, so enjoy.

    Liouville’s Approximation Theorem

    It all comes back to polynomials. Of course it does. Polynomials aren’t literally everything in mathematics. They just come close. Among the things we can do with polynomials is divide up the real numbers into different sets. The tool we use is polynomials with integer coefficients. Integers are the positive and the negative whole numbers, stuff like ‘4’ and ‘5’ and ‘-12’ and ‘0’.

    A polynomial is the sum of a bunch of products of coefficients multiplied by a variable raised to a power. We can use anything for the variable’s name. So we use ‘x’. Sometimes ‘t’. If we want complex-valued polynomials we use ‘z’. Some people trying to make a point will use ‘y’ or ‘s’ but they’re just showing off. Coefficients are just numbers. If we know the numbers, great. If we don’t know the numbers, or we want to write something that doesn’t commit us to any particular numbers, we use letters from the start of the alphabet. So we use ‘a’, maybe ‘b’ if we must. If we need a lot of numbers, we use subscripts: a0, a1, a2, and so on, up to some an for some big whole number n. To talk about one of these without committing ourselves to a specific example we use a subscript of i or j or k: aj, ak. It’s possible that aj and ak equal each other, but they don’t have to, unless j and k are the same whole number. They might also be zero, but they don’t have to be. They can be any numbers. Or, for this essay, they can be any integers. So we’d write a generic polynomial f(x) as:

    f(x) = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \cdots + a_{n - 1}x^{n - 1} + a_n x^n

    (Some people put the coefficients in the other order, that is, a_n + a_{n - 1}x + a_{n - 2}x^2 and so on. That’s not wrong. The name we give a number doesn’t matter. But it makes it harder to remember what coefficient matches up with, say, x14.)

    A zero, or root, is a value for the variable (‘x’, or ‘t’, or what have you) which makes the polynomial equal to zero. It’s possible that ‘0’ is a zero, but don’t count on it. A polynomial of degree n — meaning the highest power to which x is raised is n — can have up to n different real-valued roots. All we’re going to care about is one.

    Rational numbers are what we get by dividing one whole number by another. They’re numbers like 1/2 and 5/3 and 6. They’re numbers like -2.5 and 1.0625 and negative a billion. Almost none of the real numbers are rational numbers; they’re exceptional freaks. But they are all the numbers we actually compute with, once we start working out digits. Thus we remember that to live is to live paradoxically.

    And every rational number is a root of a first-degree polynomial. That is, there’s some polynomial f(x) = a_0 + a_1 x that’s made zero for your polynomial. It’s easy to tell you what it is, too. Pick your rational number. You can write that as the integer p divided by the integer q. Now look at the polynomial f(x) = p – q x. Astounded yet?

    That trick will work for any rational number. It won’t work for any irrational number. There’s no first-degree polynomial with integer coefficients that has the square root of two as a root. There are polynomials that do, though. There’s f(x) = 2 – x2. You can find the square root of two as the zero of a second-degree polynomial. You can’t find it as the zero of any lower-degree polynomials. So we say that this is an algebraic number of the second degree.

    This goes on higher. Look at the cube root of 2. That’s another irrational number, so no first-degree polynomials have it as a root. And there’s no second-degree polynomials that have it as a root, not if we stick to integer coefficients. Ah, but f(x) = 2 – x3? That’s got it. So the cube root of two is an algebraic number of degree three.

    We can go on like this, although I admit examples for higher-order algebraic numbers start getting hard to justify. Most of the numbers people have heard of are either rational or are order-two algebraic numbers. I can tell you truly that the eighth root of two is an eighth-degree algebraic number. But I bet you don’t feel enlightened. At best you feel like I’m setting up for something. The number r(5), the smallest radius a disc can have so that five of them will completely cover a disc of radius 1, is eighth-degree and that’s interesting. But you never imagined the number before and don’t have any idea how big that is, other than “I guess that has to be smaller than 1”. (It’s just a touch less than 0.61.) I sound like I’m wasting your time, although you might start doing little puzzles trying to make smaller coins cover larger ones. Do have fun.

    Liouville’s Approximation Theorem is about approximating algebraic numbers with rational ones. Almost everything we ever do is with rational numbers. That’s all right because we can make the difference between the number we want, even if it’s r(5), and the numbers we can compute with, rational numbers, as tiny as we need. We trust that the errors we make from this approximation will stay small. And then we discover chaos science. Nothing is perfect.

    For example, suppose we need to estimate π. Everyone knows we can approximate this with the rational number 22/7. That’s about 3.142857, which is all right but nothing great. Some people know we can approximate it as 333/106. (I didn’t until I started writing this paragraph and did some research.) That’s about 3.141509, which is better. Then there’s 355/113, which is not as famous as 22/7 but is a celebrity compared to 333/106. That’s about 3.141529. Then we get into some numbers only mathematics hipsters know: 103993/33102 and 104348/33215 and so on. Fine.

    The Liouville Approximation Theorem is about sequences that converge on an irrational number. So we have our first approximation x1, that’s the integer p1 divided by the integer q1. So, 22 and 7. Then there’s the next approximation x2, that’s the integer p2 divided by the integer q2. So, 333 and 106. Then there’s the next approximation yet, x3, that’s the integer p3 divided by the integer q3. As we look at more and more approximations, xj‘s, we get closer and closer to the actual irrational number we want, in this case π. Also, the denominators, the qj‘s, keep getting bigger.

    The theorem speaks of having an algebraic number, call it x, of some degree n greater than 1. Then we have this limit on how good an approximation can be. The difference between the number x that we want, and our best approximation p / q, has to be larger than the number (1/q)n + 1. The approximation might be higher than x. It might be lower than x. But it will be off by at least the n-plus-first power of 1/q.

    Polynomials let us separate the real numbers into infinitely many tiers of numbers. They also let us say how well the most accessible tier of numbers, rational numbers, can approximate these more exotic things.

    One of the things we learn by looking at numbers through this polynomial screen is that there are transcendental numbers. These are numbers that can’t be the root of any polynomial with integer coefficients. π is one of them. e is another. Nearly all numbers are transcendental. But the proof that any particular number is one is hard. Joseph Liouville showed that transcendental numbers must exist by using continued fractions. But this approximation theorem tells us how to make our own transcendental numbers. This won’t be any number you or anyone else has ever heard of, unless you pick a special case. But it will be yours.

    You will need:

    1. a1, an integer from 1 to 9, such as ‘1’, ‘9’, or ‘5’.
    2. a2, another integer from 1 to 9. It may be the same as a1 if you like, but it doesn’t have to be.
    3. a3, yet another integer from 1 to 9. It may be the same as a1 or a2 or, if it so happens, both.
    4. a4, one more integer from 1 to 9 and you know what? Let’s summarize things a bit.
    5. A whopping great big gob of integers aj, every one of them from 1 to 9, for every possible integer ‘j’ so technically this is infinitely many of them.
    6. Comfort with the notation n!, which is the factorial of n. For whole numbers that’s the product of every whole number from 1 to n, so, 2! is 1 times 2, or 2. 3! is 1 times 2 times 3, or 6. 4! is 1 times 2 times 3 times 4, or 24. And so on.
    7. Not to be thrown by me writing -n!. By that I mean work out n! and then multiply that by -1. So -2! is -2. -3! is -6. -4! is -24. And so on.

    Now, assemble them into your very own transcendental number z, by this formula:

    z = a_1 \cdot 10^{-1} + a_2 \cdot 10^{-2!} + a_3 \cdot 10^{-3!} + a_4 \cdot 10^{-4!} + a_5 \cdot 10^{-5!} + a_6 \cdot 10^{-6!} \cdots

    If you’ve done it right, this will look something like:

    z = 0.a_{1}a_{2}000a_{3}00000000000000000a_{4}0000000 \cdots

    Ah, but, how do you know this is transcendental? We can prove it is. The proof is by contradiction, which is how a lot of great proofs are done. We show nonsense follows if the thing isn’t true, so the thing must be true. (There are mathematicians that don’t care for proof-by-contradiction. They insist on proof by charging straight ahead and showing a thing is true directly. That’s a matter of taste. I think every mathematician feels that way sometimes, to some extent or on some issues. The proof-by-contradiction is easier, at least in this case.)

    Suppose that your z here is not transcendental. Then it’s got to be an algebraic number of degree n, for some finite number n. That’s what it means not to be transcendental. I don’t know what n is; I don’t care. There is some n and that’s enough.

    Now, let’s let zm be a rational number approximating z. We find this approximation by taking the first m! digits after the decimal point. So, z1 would be just the number 0.a1. z2 is the number 0.a1a2. z3 is the number 0.a1a2000a3. I don’t know what m you like, but that’s all right. We’ll pick a nice big m.

    So what’s the difference between z and zm? Well, it can’t be larger than 10 times 10-(m + 1)!. This is for the same reason that π minus 3.14 can’t be any bigger than 0.01.

    Now suppose we have the best possible rational approximation, p/q, of your number z. Its first m! digits are going to be p / 10m!. This will be zm And by the Liouville Approximation Theorem, then, the difference between z and zm has to be at least as big as (1/10m!)(n + 1).

    So we know the difference between z and zm has to be larger than one number. And it has to be smaller than another. Let me write those out.

    \frac{1}{10^{m! (n + 1)}} < |z - z_m | < \frac{10}{10^{(m + 1)!}}

    We don’t need the z – zm anymore. That thing on the rightmost side we can write what I’ll swear is a little easier to use. What we have left is:

    \frac{1}{10^{m! (n + 1)}} < \frac{1}{10^{(m + 1)! - 1}}

    And this will be true whenever the number m! (n + 1) is greater than (m + 1)! – 1 for big enough numbers m.

    But there’s the thing. This isn’t true whenever m is greater than n. So the difference between your alleged transcendental number and its best-possible rational approximation has to be simultaneously bigger than a number and smaller than that same number without being equal to it. Supposing your number is anything but transcendental produces nonsense. Therefore, congratulations! You have a transcendental number.

    If you chose all 1’s for your aj‘s, then you have what is sometimes called the Liouville Constant. If you didn’t, you may have a transcendental number nobody’s ever noticed before. You can name it after someone if you like. That’s as meaningful as naming a star for someone and cheaper. But you can style it as weaving someone’s name into the universal truth of mathematics. Enjoy!

    I’m glad to finally give you a mathematics essay that lets you make something you can keep.

     
    • Andrew Wearden 3:29 pm on Thursday, 30 June, 2016 Permalink | Reply

      Admittedly, I do have an undergrad math degree, but I thought you did a good job explaining this. Out of curiosity, is there a reason you can’t use the integer ‘0’ when creating a transcendental number?

      Liked by 1 person

      • Joseph Nebus 6:45 am on Sunday, 3 July, 2016 Permalink | Reply

        Thank you. I’m glad you followed.

        If I’m not missing a trick there’s no reason you can’t slip a couple of zeroes in to the transcendental number. But there is a problem if you have nothing but zeroes after some point. If, say, everything from a_9 on were zero, then you’d have a rational number, which is as un-transcendental as it gets. So it’s easier to build a number without electing zeroes rather than work out a rule that allows zeroes only in non-dangerous configurations.

        Like

  • Joseph Nebus 3:00 pm on Thursday, 2 June, 2016 Permalink | Reply
    Tags: , , , , , polynomials, , ,   

    Theorem Thursday: The Intermediate Value Theorem 


    I am still taking requests for this Theorem Thursdays sequence. I intend to post each Thursday in June and July an essay talking about some theorem and what it means and why it’s important. I have gotten a couple of requests in, but I’m happy to take more; please just give me a little lead time. But I want to start with one that delights me.

    The Intermediate Value Theorem

    I own a Scion tC. It’s a pleasant car, about 2400 percent more sporty than I am in real life. I got it because it met my most important criteria: it wasn’t expensive and it had a sun roof. That it looks stylish is an unsought bonus.

    But being a car, and a black one at that, it has a common problem. Leave it parked a while, then get inside. In the winter, it gets so cold that snow can fall inside it. In the summer, it gets so hot that the interior, never mind the passengers, risk melting. While pondering this slight inconvenience I wondered, isn’t there any outside temperature that leaves my car comfortable?

    Scion tC covered in snow and ice from a late winter storm.

    My Scion tC, here, not too warm.

    Of course there is. We know this before thinking about it. The sun heats the car, yes. When the outside temperature is low enough, there’s enough heat flowing out that the car gets cold. When the outside temperature’s high enough, not enough heat flows out. The car stays warm. There must be some middle temperature where just enough heat flows out that the interior doesn’t get particularly warm or cold. Not just one middle temperature, come to that. There is a range of temperatures that are comfortable to sit in. But that just means there’s a range of outside temperatures for which the car’s interior stays comfortable. We know this range as late April, early May, here. Most years, anyway.

    The reasoning that lets us know there is a comfort-producing outside temperature we can see as a use of the Intermediate Value Theorem. It addresses a function f with domain [a, b], and range of the real numbers. The domain is closed; that is, the numbers we call ‘a’ and ‘b’ are both in the set. And f has to be a continuous function. If you want to draw it, you can do so without having to lift pen from paper. (WARNING: Do not attempt to pass your Real Analysis course with that definition. But that’s what the proper definition means.)

    So look at the numbers f(a) and f(b). Pick some number between them, and I’ll call that number ‘g’. There must be at least one number ‘c’, that’s between ‘a’ and ‘b’, and for which f(c) equals g.

    Bernard Bolzano, an early-19th century mathematician/logician/theologist/priest, gets the credit for first proving this theorem. Bolzano’s version was a little different. It supposes that f(a) and f(b) are of opposite sign. That is, f(a) is a positive and f(b) a negative number. Or f(a) is negative and f(b) is positive. And Bolzano’s theorem says there must be some number ‘c’ for which f(c) is zero.

    You can prove this by drawing any wiggly curve at all and then a horizontal line in the middle of it. Well, that doesn’t prove it to mathematician’s satisfaction. But it will prove the matter in the sense that you’ll be convinced. It’ll also convince anyone you try explaining this to.

    A generic wiggly function, with vertical lines marking off the domain limits of a and b. Horizontal lines mark off f(a) and f(b), as well as a putative value g. The wiggly function indeed has at least one point for which its value is g.

    Any old real-valued function, drawn in blue. The number ‘g’ is something between the number f(a) and f(b). And somewhere there’s at least one number, between a and b, for where the function’s equal to g.

    You might wonder why anyone needed this proved at all. It’s a bit like proving that as you pour water into the sink there’ll come a time the last dish gets covered with water. So it is. The need for a proof came about from the ongoing attempt to make mathematics rigorous. We have an intuitive idea of what it means for functions to be continuous; see my above comment about lifting pens from paper. Can that be put in terms that don’t depend on physical intuition? … Yes, it can. And we can divorce the Intermediate Value Theorem from our physical intuitions. We can know something that’s true even if we never see a car or a sink.

    This theorem might leave you feeling a little hollow inside. Proving that there is some ‘c’ for which f(c) equals g, or even equals zero, doesn’t seem to tell us much about how to find it. It doesn’t even tell us that there’s only one ‘c’, rather than two or three or a hundred million candidates that meet our criteria. Fair enough. The Intermediate Value Theorem is more about proving the existence of solutions, rather than how to find them.

    But knowing there is a solution can help us find them. The Intermediate Value Theorem as we know it grew out of finding roots for polynomials. One numerical method, easy to set up for any problem, is the bisection method. If you know that somewhere between ‘a’ and ‘b’ the function goes from positive to negative, then find the midpoint, ‘c’. The function is equal to zero either between ‘a’ and ‘c’, or between ‘c’ and ‘b’. Pick the side that it’s on, and bisect that. Pick the half of that which the zero must be in. Bisect that half. And repeat until you get close enough to the answer for your needs. (The same reasoning applies to a lot of problems in which you divide the search range in two each time until the answer appears.)

    We can get some pretty heady results from the Intermediate Value Theorem, too, even if we don’t know where any of them are. An example you’ll see everywhere is that there must be spots on the opposite sides of the globe with the exact same temperature. Or humidity, or daily rainfall, or any other quantity like that. I had thought everyone was ripping that example off from Richard Courant and Herbert Robbins’s masterpiece What Is Mathematics?. But I can’t find this particular example in there. I wonder what we are all ripping it off from.

    Two blobby shapes, one of them larger and more complicated, the other looking kind of like the outline of a trefoil, both divided by a magenta line.

    Does this magenta line bisect both the red and the greyish blobs simultaneously? … Probably not, unless I’ve been way lucky. But there is some line that does.

    So here’s a neat example that is ripped off from them. Draw two blobs on the plane. Is there a straight line that bisects both of them at once? Bisecting here means there’s exactly as much of one blob on one side of the line as on the other. There certainly is. The trick is there are any number of lines that will bisect one blob, and then look at what that does to the other.

    A similar ripped-off result you can do with a single blob of any shape you like. Draw any line that bisects it. There are a lot of candidates. Can you draw a line perpendicular to that so that the blob gets quartered, divided into four spots of equal area? Yes. Try it.

    A generic blobby shape with two perpendicular magenta lines crossing over it.

    Does this pair of magenta lines split this blue blob into four pieces of exactly the same area? … Probably not, unless I’ve been lucky. But there is some pair of perpendicular lines that will do it. Also, is it me or does that blob look kind of like a butterfly?

    But surely the best use of the Intermediate Value Theorem is in the problem of wobbly tables. If the table has four legs, all the same length, and the problem is the floor isn’t level it’s all right. There is some way to adjust the table so it won’t wobble. (Well, the ground can’t be angled more than a bit over 35 degrees, but that’s all right. If the ground has a 35 degree angle you aren’t setting a table on it. You’re rolling down it.) Finally a mathematical proof can save us from despair!

    Except that the proof doesn’t work if the table legs are uneven which, alas, they often are. But we can’t get everything.

    Courant and Robbins put forth one more example that’s fantastic, although it doesn’t quite work. But it’s a train problem unlike those you’ve seen before. Let me give it to you as they set it out:

    Suppose a train travels from station A to station B along a straight section of track. The journey need not be of uniform speed or acceleration. The train may act in any manner, speeding up, slowing down, coming to a halt, or even backing up for a while, before reaching B. But the exact motion of the train is supposed to be known in advance; that is, the function s = f(t) is given, where s is the distance of the train from station A, and t is the time, measured from the instant of departure.

    On the floor of one of the cars a rod is pivoted so that it may move without friction either forward or backward until it touches the floor. If it does touch the floor, we assume that it remains on the floor henceforth; this wil be the case if the rod does not bounce.

    Is it possible to place the rod in such a position that, if it is released at the instant when the train starts and allowed to move solely under the influence of gravity and the motion of the train, it will not fall to the floor during the entire journey from A to B?

    They argue it is possible, and use the Intermediate Value Theorem to show it. They admit the range of angles it’s safe to start the rod from may be too small to be useful.

    But they’re not quite right. Ian Stewart, in the revision of What Is Mathematics?, includes an appendix about this. Stewart credits Tim Poston with pointing out, in 1976, the flaw. It’s possible to imagine a path which causes the rod, from one angle, to just graze tipping over, let’s say forward, and then get yanked back and fall over flat backwards. This would leave no room for any starting angles that avoid falling over entirely.

    It’s a subtle flaw. You might expect so. Nobody mentioned it between the book’s original publication in 1941, after which everyone liking mathematics read it, and 1976. And it is one that touches on the complications of spaces. This little Intermediate Value Theorem problem draws us close to chaos theory. It’s one of those ideas that weaves through all mathematics.

     
  • Joseph Nebus 3:00 pm on Wednesday, 13 April, 2016 Permalink | Reply
    Tags: , , , , , polynomials, ,   

    A Leap Day 2016 Mathematics A To Z: Transcendental Number 


    I’m down to the last seven letters in the Leap Day 2016 A To Z. It’s also the next-to-the-last of Gaurish’s requests. This was a fun one.

    Transcendental Number.

    Take a huge bag and stuff all the real numbers into it. Give the bag a good solid shaking. Stir up all the numbers until they’re thoroughly mixed. Reach in and grab just the one. There you go: you’ve got a transcendental number. Enjoy!

    OK, I detect some grumbling out there. The first is that you tried doing this in your head because you somehow don’t have a bag large enough to hold all the real numbers. And you imagined pulling out some number like “2” or “37” or maybe “one-half”. And you may not be exactly sure what a transcendental number is. But you’re confident the strangest number you extracted, “minus 8”, isn’t it. And you’re right. None of those are transcendental numbers.

    I regret saying this, but that’s your own fault. You’re lousy at picking random numbers from your head. So am I. We all are. Don’t believe me? Think of a positive whole number. I predict you probably picked something between 1 and 10. Almost surely something between 1 and 100. Surely something less than 10,000. You didn’t even consider picking something between 10,012,002,214,473,325,937,775 and 10,012,002,214,473,325,937,785. Challenged to pick a number, people will select nice and familiar ones. The nice familiar numbers happen not to be transcendental.

    I detect some secondary grumbling there. Somebody picked π. And someone else picked e. Very good. Those are transcendental numbers. They’re also nice familiar numbers, at least to people who like mathematics a lot. So they attract attention.

    Still haven’t said what they are. What they are traces back, of course, to polynomials. Take a polynomial that’s got one variable, which we call ‘x’ because we don’t want to be difficult. Suppose that all the coefficients of the polynomial, the constant numbers we presumably know or could find out, are integers. What are the roots of the polynomial? That is, for what values of x is the polynomial a complicated way of writing ‘zero’?

    For example, try the polynomial x2 – 6x + 5. If x = 1, then that polynomial is equal to zero. If x = 5, the polynomial’s equal to zero. Or how about the polynomial x2 + 4x + 4? That’s equal to zero if x is equal to -2. So a polynomial with integer coefficients can certainly have positive and negative integers as roots.

    How about the polynomial 2x – 3? Yes, that is so a polynomial. This is almost easy. That’s equal to zero if x = 3/2. How about the polynomial (2x – 3)(4x + 5)(6x – 7)? It’s my polynomial and I want to write it so it’s easy to find the roots. That polynomial will be zero if x = 3/2, or if x = -5/4, or if x = 7/6. So a polynomial with integer coefficients can have positive and negative rational numbers as roots.

    How about the polynomial x2 – 2? That’s equal to zero if x is the square root of 2, about 1.414. It’s also equal to zero if x is minus the square root of 2, about -1.414. And the square root of 2 is irrational. So we can certainly have irrational numbers as roots.

    So if we can have whole numbers, and rational numbers, and irrational numbers as roots, how can there be anything else? Yes, complex numbers, I see you raising your hand there. We’re not talking about complex numbers just now. Only real numbers.

    It isn’t hard to work out why we can get any whole number, positive or negative, from a polynomial with integer coefficients. Or why we can get any rational number. The irrationals, though … it turns out we can only get some of them this way. We can get square roots and cube roots and fourth roots and all that. We can get combinations of those. But we can’t get everything. There are irrational numbers that are there but that even polynomials can’t reach.

    It’s all right to be surprised. It’s a surprising result. Maybe even unsettling. Transcendental numbers have something peculiar about them. The 19th Century French mathematician Joseph Liouville first proved the things must exist, in 1844. (He used continued fractions to show there must be such things.) It would be seven years later that he gave an example of one in nice, easy-to-understand decimals. This is the number 0.110 001 000 000 000 000 000 001 000 000 (et cetera). This number is zero almost everywhere. But there’s a 1 in the n-th digit past the decimal if n is the factorial of some number. That is, 1! is 1, so the 1st digit past the decimal is a 1. 2! is 2, so the 2nd digit past the decimal is a 1. 3! is 6, so the 6th digit past the decimal is a 1. 4! is 24, so the 24th digit past the decimal is a 1. The next 1 will appear in spot number 5!, which is 120. After that, 6! is 720 so we wait for the 720th digit to be 1 again.

    And what is this Liouville number 0.110 001 000 000 000 000 000 001 000 000 (et cetera) used for, besides showing that a transcendental number exists? Not a thing. It’s of no other interest. And this plagued the transcendental numbers until 1873. The only examples anyone had of transcendental numbers were ones built to show that they existed. In 1873 Charles Hermite showed finally that e, the base of the natural logarithm, was transcendental. e is a much more interesting number; we have reasons to care about it. Every exponential growth or decay or oscillating process has e lurking in it somewhere. In 1882 Ferdinand von Lindemann showed that π was transcendental, and that’s an even more interesting number.

    That bit about π has interesting implications. One goes back to the ancient Greeks. Is it possible, using straightedge and compass, to create a square that’s exactly the same size as a given circle? This is equivalent to saying, if I give you a line segment, can you create another line segment that’s exactly the square root of π times as long? This geometric problem is equivalent to an algebraic one. That problem: can you create a polynomial, with integer coefficients, that has the square root of π as a root? (WARNING: I’m skipping some important points for the sake of clarity. DO NOT attempt to use this to pass your thesis defense without putting those points back in.) We want the square root of π because … well, what’s the area of a square whose sides are the square root of π long? That’s right. So we start with a line segment that’s equal to the radius of the circle and we can do that, surely. Once we have the radius, can’t we make a line that’s the square root of π times the radius, and from that make a square with area exactly π times the radius squared? Since π is transcendental, then, no. We can’t. Sorry. One of the great problems of ancient mathematics, and one that still has the power to attract the casual mathematician, got its final answer in 1882.

    Georg Cantor is a name even non-mathematicians might recognize. He showed there have to be some infinite sets bigger than others, and that there must be more real numbers than there are rational numbers. Four years after showing that, he proved there are as many transcendental numbers as there are real numbers.

    They’re everywhere. They permeate the real numbers so much that we can understand the real numbers as the transcendental numbers plus some dust. They’re almost the dark matter of mathematics. We don’t actually know all that many of them. Wolfram MathWorld has a table listing numbers proven to be transcendental, and the fact we can list that on a single web page is remarkable. Some of them are large sets of numbers, yes, like e^{\pi \sqrt{d}} for every positive whole number d. And we can infer many more from them; if π is transcendental then so is 2π, and so is 5π, and so is -20.38π, and so on. But the table of numbers proven to be irrational is still just 25 rows long.

    There are even mysteries about obvious numbers. π is transcendental. So is e. We know that at least one of π times e and π plus e is transcendental. Perhaps both are. We don’t know which one is, or if both are. We don’t know whether ππ is transcendental. We don’t know whether ee is, either. Don’t even ask if πe is.

    How, by the way, does this fit with my claim that everything in mathematics is polynomials? — Well, we found these numbers in the first place by looking at polynomials. The set is defined, even to this day, by how a particular kind of polynomial can’t reach them. Thinking about a particular kind of polynomial makes visible this interesting set.

     
    • howardat58 3:26 pm on Wednesday, 13 April, 2016 Permalink | Reply

      I like this stuff. I cut my mathematical teeth on Paul Halmos ” Naive Set Theory”.
      So I thought about “dense set”, “Canto’s middle third”, “countable”, and then realized that U is next. How about “Uncountable” ?

      Like

  • Joseph Nebus 3:00 pm on Tuesday, 12 April, 2016 Permalink | Reply
    Tags: Ancient Greeks, Chandler Wobble, , , polynomials,   

    Some More Stuff To Read 


    I’ve actually got enough comics for yet another Reading The Comics post. But rather than overload my Recent Posts display with those I’ll share some pointers to other stuff I think worth looking at.

    So remember how the other day I said polynomials were everything? And I tried to give some examples of things you might not expect had polynomials tied to them? Here’s one I forgot. Howard Phillips, of the HowardAt58 blog, wrote recently about discrete signal processing, the struggle to separate real patterns from random noise. It’s a hard problem. If you do very little filtering, then meaningless flutterings can look like growing trends. If you do a lot of filtering, then you miss rare yet significant events and you take a long time to detect changes. Either can be mistakes. The study of a filter’s characteristics … well, you’ll see polynomials. A lot.

    For something else to read, and one that doesn’t get into polynomials, here’s a post from Stephen Cavadino of the CavMaths blog, abut the areas of lunes. Lunes are … well, they’re kind of moon-shaped figures. Cavadino particularly writes about the Theorem of Not That Hippocrates. Start with a half circle. Draw a symmetric right triangle inside the circle. Draw half-circles off the two equal legs of that right triangle. The area between the original half-circle and the newly-drawn half circles is … how much? The answer may surprise you.

    Cavadino doesn’t get into this, but: it’s possible to make a square that has the same area as these strange crescent shapes using only straightedge and compass. Not That Hippocrates knew this. It’s impossible to make a square with the exact same area as a circle using only straightedge and compass. But these figures, with edges that are defined by circles of just the right relative shapes, they’re fine. Isn’t that wondrous?

    And this isn’t mathematics but what the heck. Have you been worried about the Chandler Wobble? Apparently there’s been a bit of a breakthrough in understanding it. Turns out water melting can change the Earth’s rotation enough to be noticed. And to have been noticed since the 1890s.

     
  • Joseph Nebus 3:00 pm on Monday, 4 April, 2016 Permalink | Reply
    Tags: , , , , polynomials   

    A Leap Day 2016 Mathematics A To Z: Polynomials 


    I have another request for today’s Leap Day Mathematics A To Z term. Gaurish asked for something exciting. This should be less challenging than Dedekind Domains. I hope.

    Polynomials.

    Polynomials are everything. Everything in mathematics, anyway. If humans study it, it’s a polynomial. If we know anything about a mathematical construct, it’s because we ran across it while trying to understand polynomials.

    I exaggerate. A tiny bit. Maybe by three percent. But polynomials are big.

    They’re easy to recognize. We can get them in pre-algebra. We make them out of a set of numbers called coefficients and one or more variables. The coefficients are usually either real numbers or complex-valued numbers. The variables we usually allow to be either real or complex-valued numbers. We take each coefficient and multiply it by some power of each variable. And we add all that up. So, polynomials are things that look like these things:

    x^2 - 2x + 1
    12 x^4 + 2\pi x^2 y^3 - 4x^3 y - \sqrt{6}
    \ln(2) + \frac{1}{2}\left(x - 2\right) - \frac{1}{2 \cdot 2^2}\left(x - 2\right)^2 + \frac{1}{2 \cdot 2^3}\left(x - 2\right)^3 - \frac{1}{2 \cdot 2^4}\left(x - 2\right)^4  + \cdots
    a_n x^n + a_{n - 1}x^{n - 1} + a_{n - 2}x^{n - 2} + \cdots + a_2 x^2 + a_1 x^1 + a_0

    The first polynomial maybe looks nice and comfortable. The second may look a little threatening, what with it having two variables and a square root in it, but it’s not too weird. The third is an infinitely long polynomial; you’re supposed to keep going on in that pattern, adding even more terms. The last is a generic representation of a polynomial. Each number a0, a1, a2, et cetera is some coefficient that we in principle know. It’s a good way of representing a polynomial when we want to work with it but don’t want to tie ourselves down to a particular example. The highest power we raise a variable to we call the degree of the polynomial. A second-degree polynomial, for example, has an x2 in it, but not an x3 or x4 or x18 or anything like that. A third-degree polynomial has an x3, but not x to any higher powers. Degree is a useful way of saying roughly how long a polynomial is, so it appears all over discussions of polynomials.

    But why do we like polynomials? Why like them so much that MathWorld lists 1,163 pages that mention polynomials?

    It’s because they’re great. They do everything we’d ever want to do and they’re great at it. We can add them together as easily as we add regular old numbers. We can subtract them as well. We can multiply and divide them. There’s even prime polynomials, just like there are prime numbers. They take longer to work out, but they’re not harder.

    And they do great stuff in advanced mathematics too. In calculus we want to take derivatives of functions. Polynomials, we always can. We get another polynomial out of that. So we can keep taking derivatives, as many as we need. (We might need a lot of them.) We can integrate too. The integration produces another polynomial. So we can keep doing that as long as we need too. (We need to do this a lot, too.) This lets us solve so many problems in calculus, which is about how functions work. It also lets us solve so many problems in differential equations, which is about systems whose change depends on the current state of things.

    That’s great for analyzing polynomials, but what about things that aren’t polynomials?

    Well, if a function is continuous, then it might as well be a polynomial. To be a little more exact, we can set a margin of error. And we can always find polynomials that are less than that margin of error away from the original function. The original function might be annoying to deal with. The polynomial that’s as close to it as we want, though, isn’t.

    Not every function is continuous. Most of them aren’t. But most of the functions we want to do work with are, or at least are continuous in stretches. Polynomials let us understand the functions that describe most real stuff.

    Nice for mathematicians, all right, but how about for real uses? How about for calculations?

    Oh, polynomials are just magnificent. You know why? Because you can evaluate any polynomial as soon as you can add and multiply. (Also subtract, but we think of that as addition.) Remember, x4 just means “x times x times x times x”, four of those x’s in the product. All these polynomials are easy to evaluate.

    Even better, we don’t have to evaluate them. We can automate away the evaluation. It’s easy to set a calculator doing this work, and it will do it without complaint and with few unforeseeable mistakes.

    Now remember that thing where we can make a polynomial close enough to any continuous function? And we can always set a calculator to evaluate a polynomial? Guess that this means about continuous functions. We have a tool that lets us calculate stuff we would want to know. Things like arccosines and logarithms and Bessel functions and all that. And we get nice easy to understand numbers out of them. For example, that third polynomial I gave you above? That’s not just infinitely long. It’s also a polynomial that approximates the natural logarithm. Pick a positive number x that’s between 0 and 4 and put it in that polynomial. Calculate terms and add them up. You’ll get closer and closer to the natural logarithm of that number. You’ll get there faster if you pick a number near 2, but you’ll eventually get there for whatever number you pick. (Calculus will tell us why x has to be between 0 and 4. Don’t worry about it for now.)

    So through polynomials we can understand functions, analytically and numerically.

    And they keep revealing things to us. We discovered complex-valued numbers because we wanted to find roots, values of x that make a polynomial of x equal to zero. Some formulas worked well for third- and fourth-degree polynomials. (They look like the quadratic formula, which solves second-degree polynomials. The big difference is nobody remembers what they are without looking them up.) But the formulas sometimes called for things that looked like square roots of negative numbers. Absurd! But if you carried on as if these square roots of negative numbers meant something, you got meaningful answers. And correct answers.

    We wanted formulas to solve fifth- and higher-degree polynomials exactly. We can do this with second and third and fourth-degree polynomials, after all. It turns out we can’t. Oh, we can solve some of them exactly. The attempt to understand why, though, helped us create and shape group theory, the study of things that look like but aren’t numbers.

    Polynomials go on, sneaking into everything. We can look at a square matrix and discover its characteristic polynomial. This allows us to find beautifully-named things like eigenvalues and eigenvectors. These reveal secrets of the matrix’s structure. We can find polynomials in the formulas that describe how many ways to split up a group of things into a smaller number of sets. We can find polynomials that describe how networks of things are connected. We can find polynomials that describe how a knot is tied. We can even find polynomials that distinguish between a knot and the knot’s reflection in the mirror.

    Polynomials are everything.

     
    • gaurish 3:40 pm on Monday, 4 April, 2016 Permalink | Reply

      Beautiful post!
      Recently I studied Taylor’s Theorem & Weierstrass approximation theorem. These theorems illustrate your ideas :)

      Like

      • Joseph Nebus 6:38 pm on Monday, 4 April, 2016 Permalink | Reply

        Thank you kindly. And yeah, the Taylor Theorem and Weierstrauss Approximation Theorem are the ideas I was sneaking around without trying to get too technical. (Maybe I should start including a postscript of technical talk to these essays.)

        Like

  • Joseph Nebus 3:00 pm on Monday, 7 March, 2016 Permalink | Reply
    Tags: , , Dedekind rings, , , , , polynomials,   

    A Leap Day 2016 Mathematics A To Z: Dedekind Domain 


    When I tossed this season’s A To Z open to requests I figured I’d get some surprising ones. So I did. This one’s particularly challenging. It comes fro Gaurish Korpal, author of the Gaurish4Math blog.

    Dedekind Domain

    A major field of mathematics is Algebra. By this mathematicians don’t mean algebra. They mean studying collections of things on which you can do stuff that looks like arithmetic. There’s good reasons why this field has that confusing name. Nobody knows what they are.

    We’ve seen before the creation of things that look a bit like arithmetic. Rings are a collection of things for which we can do something that works like addition and something that works like multiplication. There are a lot of different kinds of rings. When a mathematics popularizer tries to talk about rings, she’ll talk a lot about the whole numbers. We can usually count on the audience to know what they are. If that won’t do for the particular topic, she’ll try the whole numbers modulo something. If she needs another example then she talks about the ways you can rotate or reflect a triangle, or square, or hexagon and get the original shape back. Maybe she calls on the sets of polynomials you can describe. Then she has to give up on words and make do with pictures of beautifully complicated things. And after that she has to give up because the structures get too abstract to describe without losing the audience.

    Dedekind Domains are a kind of ring that meets a bunch of extra criteria. There’s no point my listing them all. It would take several hundred words and you would lose motivation to continue before I was done. If you need them anyway Eric W Weisstein’s MathWorld dictionary gives the exact criteria. It also has explanations for all the words in those criteria.

    Dedekind Domains, also called Dedekind Rings, are aptly named for Richard Dedekind. He was a 19th century mathematician, the last doctoral student of Gauss, and one of the people who defined what we think of as algebra. He also gave us a rigorous foundation for what irrational numbers are.

    Among the problems that fascinated Dedekind was Fermat’s Last Theorem. This can’t surprise you. Every person who would be a mathematician is fascinated by it. We take our innings fiddling with cases and ways to show an + bn can’t equal cn for interesting whole numbers a, b, c, and n. We usually go about this by saying, “Suppose we have the smallest a, b, and c for which this is true and for which n is bigger than 2”. Then we do a lot of scribbling that shows this implies something contradictory, like an even number equals an odd, or that there’s some set of smaller numbers making this true. This proves the original supposition was false. Mathematicians first learn that trick as a way to show the square root of two can’t be a rational number. We stick with it because it’s nice and familiar and looks relevant. Most of us get maybe as far as proving there aren’t any solutions for n = 3 or maybe n = 4 and go on to other work. Dedekind didn’t prove the theorem. But he did find new ways to look at numbers.

    One problem with proving Fermat’s Last Theorem is that it’s all about integers. Integers are hard to prove things about. Real numbers are easier. Complex-valued numbers are easier still. This is weird but it’s so. So we have this promising approach: if we could prove something like Fermat’s Last Theorem for complex-valued numbers, we’d get it up for integers. Or at least we’d be a lot of the way there. The one flaw is that Fermat’s Last Theorem isn’t true for complex-valued numbers. It would be ridiculous if it were true.

    But we can patch things up. We can construct something called Gaussian Integers. These are complex-valued numbers which we can match up to integers in a compelling way. We could use the tools that work on complex-valued numbers to squeeze out a result about integers.

    You know that this didn’t work. If it had, we wouldn’t have had to wait for the 1990s for the proof of Fermat’s Last Theorem. And that proof would have anything to do with this stuff. It hasn’t. One of the problems keeping this kind of proof from working is factoring. Whole numbers are either prime numbers or the product of prime numbers. Or they’re 1, ruled out of the universe of prime numbers for reasons I get to after the next paragraph. Prime numbers are those like 2, 5, 13, 37 and many others. They haven’t got any factors besides themselves and 1. The other whole numbers are the products of prime numbers. 12 is equal to 2 times 2 times 3. 35 is equal to 5 times 7. 165 is equal to 3 times 5 times 11.

    If we stick to whole numbers, then, these all have unique prime factorizations. 24 is equal to 2 times 2 times 2 times 3. And there are no other combinations of prime numbers that multiply together to give us 24. We could rearrange the numbers — 2 times 3 times 2 times 2 works. But it will always be a combination of three 2’s and a single 3 that we multiply together to get 24.

    (This is a reason we don’t consider 1 a prime number. If we did consider a prime number, then “three 2’s and a single 3” would be a prime factorization of 24, but so would “three 2’s, a single 3, and two 1’s”. Also “three 2’s, a single 3, and fifteen 1’s”. Also “three 2’s, a single 3, and one 1”. We have a lot of theorems that depend on whole numbers having a unique prime factorization. We could add the phrase “except for the count of 1’s in the factorization” to every occurrence of the phrase “prime factorization”. Or we could say that 1 isn’t a prime number. It’s a lot less work to say 1 isn’t a prime number.)

    The trouble is that if we work with Gaussian integers we don’t have that unique prime factorization anymore. There are still prime numbers. But it’s possible to get some numbers as a product of different sets of prime numbers. And this point breaks a lot of otherwise promising attempts to prove Fermat’s Last Theorem. And there’s no getting around that, not for Fermat’s Last Theorem.

    Dedekind saw a good concept lurking under this, though. The concept is called an ideal. It’s a subset of a ring that itself satisfies the rules for being a ring. And if you take something from the original ring and multiply it by something in the ideal, you get something that’s still in the ideal. You might already have one in mind. Start with the ring of integers. The even numbers are an ideal of that. Add any two even numbers together and you get an even number. Multiply any two even numbers together and you get an even number. Take any integer, even or not, and multiply it by an even number. You get an even number.

    (If you were wondering: I mean the ideal would be a “ring without identity”. It’s not required to have something that acts like 1 for the purpose of multiplication. If we insisted on looking at the even numbers and the number 1, then we couldn’t be sure that adding two things from the ideal would stay in the ideal. After all, 2 is in the ideal, and if 1 also is, then 2 + 1 is a peculiar thing to consider an even number.)

    It’s not just even numbers that do this. The multiples of 3 make an ideal in the integers too. Add two multiples of 3 together and you get a multiple of 3. Multiply two multiples of 3 together and you get another multiple of 3. Multiply any integer by a multiple of 3 and you get a multiple of 3.

    The multiples of 4 also make an ideal, as do the multiples of 5, or the multiples of 82, or of any whole number you like.

    Odd numbers don’t make an ideal, though. Add two odd numbers together and you don’t get an odd number. Multiply an integer by an odd number and you might get an odd number, you might not.

    And not every ring has an ideal lurking within it. For example, take the integers modulo 3. In this case there are only three numbers: 0, 1, and 2. 1 + 1 is 2, uncontroversially. But 1 + 2 is 0. 2 + 2 is 1. 2 times 1 is 2, but 2 times 2 is 1 again. This is self-consistent. But it hasn’t got an ideal within it. There isn’t a smaller set that has addition work.

    The multiples of 4 make an interesting ideal in the integers. They’re not just an ideal of the integers. They’re also an ideal of the even numbers. Well, the even numbers make a ring. They couldn’t be an ideal of the integers if they couldn’t be a ring in their own right. And the multiples of 4 — well, multiply any even number by a multiple of 4. You get a multiple of 4 again. This keeps on going. The multiples of 8 are an ideal for the multiples of 4, the multiples of 2, and the integers. Multiples of 16 and 32 make for even deeper nestings of ideals.

    The multiples of 6, now … that’s an ideal of the integers, for all the reasons the multiples of 2 and 3 and 4 were. But it’s also an ideal of the multiples of 2. And of the multiples of 3. We can see the collection of “things that are multiples of 6” as a product of “things that are multiples of 2” and “things that are multiples of 3”. Dedekind saw this before us.

    You might want to pause a moment while considering the idea of multiplying whole sets of numbers together. It’s a heady concept. Trying to do proofs with the concept feels at first like being tasked with alphabetizing a cloud. But we’re not planning to prove anything so you can move on if you like with an unalphabetized cloud.

    A Dedekind Domain is a ring that has ideals like this. And the ideals come in two categories. Some are “prime ideals”, which act like prime numbers do. The non-prime ideals are the products of prime ideals. And while we might not have unique prime factorizations of numbers, we do have unique prime factorizations of ideals. That is, if an ideal is a product of some set of prime ideals, then it can’t also be the product of some other set of prime ideals. We get back something like unique factors.

    This may sound abstract. But you know a Dedekind Domain. The integers are one. That wasn’t a given. Yes, we start algebra by looking for things that work like regular arithmetic do. But that doesn’t promise that regular old numbers will still satisfy us. We can, for instance, study things where the order matters in multiplication. Then multiplying one thing by a second gives us a different answer to multiplying the second thing by the first. Still, regular old integers are Dedekind domains and it’s hard to think of being more familiar than that.

    Another example is the set of polynomials. You might want to pause for a moment here. Mathematics majors need a pause to start thinking of polynomials as being something kind of like regular old numbers. But you can certainly add one polynomial to another, and you get a polynomial out of it. You can multiply one polynomial by another, and you get a polynomial out of that. Try it. After that the only surprise would be that there are prime polynomials. But if you try to think of two polynomials that multiply together to give you “x + 1” you realize there have to be.

    Other examples start getting more exotic. They’re things like the Gaussian integers I mentioned before. Gaussian integers are themselves an example of a structure called algebraic integers. Algebraic integers are — well, think of all the polynomials you can out of integer coefficients, and with a leading coefficient of 1. So, polynomials that look like “x3 – 4 x2 + 15 x + 6” or the like. All of the roots of those, the values of x which make that expression equal to zero, are algebraic integers. Yes, almost none of them are integers. We know. But the algebraic integers are also a Dedekind Domain.

    I’d like to describe some more Dedekind Domains. I am foiled. I can find some more, but explaining them outside the dialect of mathematics is hard. It would take me more words than I am confident readers will give me.

    I hope you are satisfied to know a bit of what a Dedekind Domain is. It is a kind of thing which works much like integers do. But a Dedekind Domain can be just different enough that we can’t count on factoring working like we are used to. We don’t lose factoring altogether, though. We are able to keep an attenuated version. It does take quite a few words to explain exactly how to set this up, however.

     
    • gaurish 3:33 pm on Monday, 7 March, 2016 Permalink | Reply

      Wow! I just couldn’t believe my eyes while reading this post. It’s beautiful. Thanks for satisfying my curiosity. :)

      Like

      • Joseph Nebus 7:54 am on Wednesday, 9 March, 2016 Permalink | Reply

        I’m glad to be of service, and I hope that you were satisfied. I’d tried a couple times to find a way to describe all the properties of a Dedekind domain in conversational English, and gave up with reluctance. I just got too many thousands of words in without being near the ending and had to try a different goal.

        Like

  • Joseph Nebus 6:00 pm on Monday, 28 September, 2015 Permalink | Reply
    Tags: , currency, polynomials, power series   

    Making Lots Of Change 


    John D Cook’s Algebra Fact of the Day points to a pair of algorithms about making change. Specifically these are about how many ways there are to provide a certain amount of change using United States coins. By that he, and the algorithms, mean 1, 5, 10, 25, and 50 cent pieces. I’m not sure if 50 cent coins really count, since they don’t circulate any more than dollar coins do. Anyway, if you want to include or rule out particular coins it’s clear enough how to adapt things.

    What surprised me was a simple algorithm, taken from Ronald L Graham, Donald E Knuth, and Oren Patashnik’s Concrete Mathematics: A Foundation For Computer Science to count the number of ways to make a certain amount of change. You start with the power series that’s equivalent to this fraction:

    \frac{1}{\left(1 - z\right)\cdot\left(1 - z^{5}\right)\cdot\left(1 - z^{10}\right)\cdot\left(1 - z^{25}\right)\cdot\left(1 - z^{50}\right)}

    A power series is a polynomial. The power series for \frac{1}{1 - z} , for example, is 1 + z + z^2 + z^3 + z^4 + \cdots ... and carries on forever like that. But if you choose a number between minus one and positive one, and put that in for z in either \frac{1}{1 - z} or in that series 1 + z + z^2 + z^3 + z^4 + \cdots ... you’ll get the same number. (If z is not between minus one and positive one, it doesn’t. Don’t worry about it. For what we’re doing we will never need any z.)

    The power series for that big fraction with all the kinds of change in it is more tedious to work out. You’d need the power series for \frac{1}{1 - z} and \frac{1}{1 - z^5} and \frac{1}{1 - z^{10}} and so on, and to multiply all those series together. And yes, that’s multiplying infinitely long polynomials together, which you might reasonably expect will take some time.

    You don’t need to, though. All you really want is a single term in this series. To tell how many ways there are to make n cents of change, look at the coefficient, the number, in front of the zn term. That’s the number of ways. So while this may be a lot of work, it’s not going to be hard work, and it’s going to be finite. You only have to work out the products that give you a zn power. That will take planning and preparation to do correctly, but that’s all.

     
  • Joseph Nebus 3:00 pm on Wednesday, 22 July, 2015 Permalink | Reply
    Tags: , , polynomials, , , signals,   

    A Summer 2015 Mathematics A To Z: z-transform 


    z-transform.

    The z-transform comes to us from signal processing. The signal we take to be a sequence of numbers, all representing something sampled at uniformly spaced times. The temperature at noon. The power being used, second-by-second. The number of customers in the store, once a month. Anything. The sequence of numbers we take to stretch back into the infinitely great past, and to stretch forward into the infinitely distant future. If it doesn’t, then we pad the sequence with zeroes, or some other safe number that we know means “nothing”. (That’s another classic mathematician’s trick.)

    It’s convenient to have a name for this sequence. “a” is a good one. The different sampled values are denoted by an index. a0 represents whatever value we have at the “start” of the sample. That might represent the present. That might represent where sampling began. That might represent just some convenient reference point. It’s the equivalent of mileage maker zero; we have to have something be the start.

    a1, a2, a3, and so on are the first, second, third, and so on samples after the reference start. a-1, a-2, a-3, and so on are the first, second, third, and so on samples from before the reference start. That might be the last couple of values before the present.

    So for example, suppose the temperatures the last several days were 77, 81, 84, 82, 78. Then we would probably represent this as a-4 = 77, a-3 = 81, a-2 = 84, a-1 = 82, a0 = 78. We’ll hope this is Fahrenheit or that we are remotely sensing a temperature.

    The z-transform of a sequence of numbers is something that looks a lot like a polynomial, based on these numbers. For this five-day temperature sequence the z-transform would be the polynomial 77 z^4 + 81 z^3 + 84 z^2 + 81 z^1 + 78 z^0 . (z1 is the same as z. z0 is the same as the number “1”. I wrote it this way to make the pattern more clear.)

    I would not be surprised if you protested that this doesn’t merely look like a polynomial but actually is one. You’re right, of course, for this set, where all our samples are from negative (and zero) indices. If we had positive indices then we’d lose the right to call the transform a polynomial. Suppose we trust our weather forecaster completely, and add in a1 = 83 and a2 = 76. Then the z-transform for this set of data would be 77 z^4 + 81 z^3 + 84 z^2 + 81 z^1 + 78 z^0 + 83 \left(\frac{1}{z}\right)^1 + 76 \left(\frac{1}{z}\right)^2 . You’d probably agree that’s not a polynomial, although it looks a lot like one.

    The use of z for these polynomials is basically arbitrary. The main reason to use z instead of x is that we can learn interesting things if we imagine letting z be a complex-valued number. And z carries connotations of “a possibly complex-valued number”, especially if it’s used in ways that suggest we aren’t looking at coordinates in space. It’s not that there’s anything in the symbol x that refuses the possibility of it being complex-valued. It’s just that z appears so often in the study of complex-valued numbers that it reminds a mathematician to think of them.

    A sound question you might have is: why do this? And there’s not much advantage in going from a list of temperatures “77, 81, 84, 81, 78, 83, 76” over to a polynomial-like expression 77 z^4 + 81 z^3 + 84 z^2 + 81 z^1 + 78 z^0 + 83 \left(\frac{1}{z}\right)^1 + 76 \left(\frac{1}{z}\right)^2 .

    Where this starts to get useful is when we have an infinitely long sequence of numbers to work with. Yes, it does too. It will often turn out that an interesting sequence transforms into a polynomial that itself is equivalent to some easy-to-work-with function. My little temperature example there won’t do it, no. But consider the sequence that’s zero for all negative indices, and 1 for the zero index and all positive indices. This gives us the polynomial-like structure \cdots + 0z^2 + 0z^1 + 1 + 1\left(\frac{1}{z}\right)^1 + 1\left(\frac{1}{z}\right)^2 + 1\left(\frac{1}{z}\right)^3 + 1\left(\frac{1}{z}\right)^4 + \cdots . And that turns out to be the same as 1 \div \left(1 - \left(\frac{1}{z}\right)\right) . That’s much shorter to write down, at least.

    Probably you’ll grant that, but still wonder what the point of doing that is. Remember that we started by thinking of signal processing. A processed signal is a matter of transforming your initial signal. By this we mean multiplying your original signal by something, or adding something to it. For example, suppose we want a five-day running average temperature. This we can find by taking one-fifth today’s temperature, a0, and adding to that one-fifth of yesterday’s temperature, a-1, and one-fifth of the day before’s temperature a-2, and one-fifth a-3, and one-fifth a-4.

    The effect of processing a signal is equivalent to manipulating its z-transform. By studying properties of the z-transform, such as where its values are zero or where they are imaginary or where they are undefined, we learn things about what the processing is like. We can tell whether the processing is stable — does it keep a small error in the original signal small, or does it magnify it? Does it serve to amplify parts of the signal and not others? Does it dampen unwanted parts of the signal while keeping the main intact?

    We can understand how data will be changed by understanding the z-transform of the way we manipulate it. That z-transform turns a signal-processing idea into a complex-valued function. And we have a lot of tools for studying complex-valued functions. So we become able to say a lot about the processing. And that is what the z-transform gets us.

     
    • sheldonk2014 4:45 pm on Wednesday, 22 July, 2015 Permalink | Reply

      Do you go to that pinball place in New Jersey

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      • Joseph Nebus 1:46 am on Thursday, 23 July, 2015 Permalink | Reply

        When I’m able to, yes! Fortunately work gives me occasional chances to revisit my ancestral homeland and from there it’s a quite reasonable drive to Asbury Park and the Silverball Museum. It’s a great spot and I recommend it highly.

        There’s apparently also a retro arcade in Redbank, with a dozen or so pinball machines and a fair number of old video games. I’ve not been there yet, though.

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    • howardat58 2:18 am on Thursday, 23 July, 2015 Permalink | Reply

      Here is a bit more.

      z is used in dealing with recurrence relations and their active form, with input as well, in the form of “z transfer function:
      a(n) is the input at time n, u(n) is the output at time n, these can be viewed as sequences
      u(n+1) = u(n) + a(n+1) represents the integral/accumulation/sum of series for the input process
      z is considered as an operator which moves the whole sequence back one step,
      Applied to the sequence equation shown you get u(n+1) = zu(n),
      and the equation becomes
      zu(n) = u(n) + za(n)
      Now since everything has (n) we don’t need it, and get
      zu = u + za
      Solving for u gives
      u = z/(z-1)a
      which describes the behaviour of the output for a given sequence of inputs
      z/(z-1) is called the transfer function of the input/output system
      and in this case of summation or integration the expression z/(z-1) represents the process of adding up the terms of the sequence.
      One nice thing is that if you do all of this for the successive differences process
      u(n+1) = a(n+1) – a(n)
      you get the transfer function (z-1)/z, the discrete differentiation process.

      Liked by 1 person

      • Joseph Nebus 2:11 pm on Saturday, 25 July, 2015 Permalink | Reply

        That’s a solid example of using these ideas. May I bump it up to a main post in the next couple days so that (hopefully) more people catch it?

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  • Joseph Nebus 5:40 pm on Saturday, 27 June, 2015 Permalink | Reply
    Tags: polynomials, vertex   

    Vertex of a parabola – language in math again 


    I don’t want folks thinking I’m claiming a monopoly on the mathematics-glossary front. HowardAt58 is happy to explain words too. Here he talks about one of the definitions of “vertex”, in this case the one that relates to parabolas and other polynomial curves. As a bonus, there’s osculating circles.

    Like

    Saving school math

    Here are some definitions of the vertex of a parabola.

    One is complete garbage, one is correct  though put rather chattily.

    The rest are not definitions, though very popular (this is just a selection). But they are true statements

    Mathwarehouse: The vertex of a parabola is the highest or lowest point, also known as the maximum or minimum of a
    parabola.
    Mathopenref: A parabola is the shape defined by a quadratic equation. The vertex is the peak in the curve as shown on
    the right. The peak will be pointing either downwards or upwards depending on the sign of the x2 term.
    Virtualnerd: Each quadratic equation has either a maximum or minimum, but did you that this point has a special name?
    In a quadratic equation, this point is called the vertex!
    Mathwords: Vertex of a Parabola: The point at which a parabola makes its sharpest turn.
    Purplemath: The…

    View original post 419 more words

     
    • mathtuition88 5:59 am on Sunday, 28 June, 2015 Permalink | Reply

      Interesting use of the word vertex!

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      • Joseph Nebus 6:39 pm on Saturday, 4 July, 2015 Permalink | Reply

        Is it unfamiliar to you? I’d thought it was a common term, but I might be misjudging its universality. There are dialects in mathematics as in all things.

        Liked by 1 person

        • mathtuition88 1:20 am on Sunday, 5 July, 2015 Permalink | Reply

          In Singapore we usually call it the minimum point / maximum point or turning point

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          • Joseph Nebus 6:33 pm on Sunday, 5 July, 2015 Permalink | Reply

            Oh, I have seen “turning point” used occasionally. And it is a minimum or maximum point, of course, unless the parabola’s been rotated. I remember being tasked with working out rotations of curves in high school, although not since then.

            Liked by 1 person

  • Joseph Nebus 2:49 pm on Wednesday, 17 June, 2015 Permalink | Reply
    Tags: , biology, , , , polynomials, trefoils,   

    A Summer 2015 Mathematics A To Z: knot 


    Knot.

    It’s a common joke that mathematicians shun things that have anything to do with the real world. You can see where the impression comes from, though. Even common mathematical constructs, such as “functions”, are otherworldly abstractions once a mathematician is done defining them precisely. It can look like mathematicians find real stuff to be too dull to study.

    Knot theory goes against the stereotype. A mathematician’s knot is just about what you would imagine: threads of something that get folded and twisted back around themselves. Every now and then a knot theorist will get a bit of human-interest news going for the department by announcing a new way to tie a tie, or to tie a shoelace, or maybe something about why the Christmas tree lights get so tangled up. These are really parts of the field, and applications that almost leap off the page as one studies. It’s a bit silly, admittedly. The only way anybody needs to tie a tie is go see my father and have him do it for you, and then just loosen and tighten the knot for the two or three times you’ll need it. And there’s at most two ways of tying a shoelace anybody needs. Christmas tree lights are a bigger problem but nobody can really help with getting them untangled. But studying the field encourages a lot of sketches of knots, and they almost cry out to be done out of some real material.

    One amazing thing about knots is that they can be described as mathematical expressions. There are multiple ways to encode a description for how a knot looks as a polynomial. An expression like t + t^3 - t^4 contains enough information to draw one knot as opposed to all the others that might exist. (In this case it’s a very simple knot, one known as the right-hand trefoil knot. A trefoil knot is a knot with a trefoil-like pattern.) Indeed, it’s possible to describe knots with polynomials that let you distinguish between a knot and its mirror-image reflection.

    Biology, life, is knots. The DNA molecules that carry and transmit genes tangle up on themselves, creating knots. The molecules that DNA encodes, proteins and enzymes and all the other basic tools of cells, can be represented as knots. Since at this level the field is about how molecules interact you probably would expect that much of chemistry can be seen as the ways knots interact. Statistical mechanics, the study of unspeakably large number of particles, do as well. A field you can be introduced to by studying your sneaker runs through the most useful arteries of science.

    That said, mathematicians do make their knots of unreal stuff. The mathematical knot is, normally, a one-dimensional thread rather than a cylinder of stuff like a string or rope or shoelace. No matter; just imagine you’ve got a very thin string. And we assume that it’s frictionless; the knot doesn’t get stuck on itself. As a result a mathematician just learning knot theory would snootily point out that however tightly wound up your extension cord is, it’s not actually knotted. You could in principle push one of the ends of the cord all the way through the knot and so loosen it into an untangled string, if you could push the cord from one end and if the cord didn’t get stuck on itself. So, yes, real-world knots are mathematically not knots. After all, something that just falls apart with a little push hardly seems worth the name “knot”.

    My point is that mathematically a knot has to be a closed loop. And it’s got to wrap around itself in some sufficiently complicated way. A simple circle of string is not a knot. If “not a knot” sounds a bit childish you might use instead the Lewis Carrollian term “unknot”.

    We can fix that, though, using a surprisingly common mathematical trick. Take the shoelace or rope or extension cord you want to study. And extend it: draw lines from either end of the cord out to the edge of your paper. (This is a great field for doodlers.) And then pretend that the lines go out and loop around, touching each other somewhere off the sheet of paper, as simply as possible. What had been an unknot is now not an unknot. Study wisely.

     
    • Lily Lau 6:09 pm on Wednesday, 17 June, 2015 Permalink | Reply

      Knots, I see! I should have studied sciences, they always sound fascinating.

      Like

      • Joseph Nebus 7:08 pm on Thursday, 18 June, 2015 Permalink | Reply

        Oh, they’re better than fascinating. They’re fun. This is a field of mathematics you actually study by imagining the cutting and splicing of threads. You can bring arts and crafts to your thesis defense and it’ll belong. I ended up in numerical mathematics and statistical mechanics; all I could bring was color transparencies of simulation results.

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    • Ken Dowell 9:19 pm on Wednesday, 17 June, 2015 Permalink | Reply

      That’s a lot more knot than I had every given much thought to. But your post did make me think about knotting ties and made me wonder why we all tie our ties the same way rather than using any of dozens of different kinds of knots that would create a different look.

      Like

      • Joseph Nebus 7:22 pm on Thursday, 18 June, 2015 Permalink | Reply

        I would imagine that most people settle on one or two ways of tying their ties because there’s not much point to picking up something more exotic. It takes effort to learn and do, and the payoff is almost secret; you might get a bit “Oh, that’s neat”, but not other recognition. We just don’t see tie-knotting as an artistic endeavor worth comment.

        It’s a bit of an open question how many different ways there are to tie a tie. It depends heavily on how you you define “different ways”, and so that makes ties an interesting application of knot theory. Last year Dan Hirsch, Ingemar Markström, Meredith L Patterson, Anders Sandberg, and Mikael Vejdemo-Johansson got a bit of human-interest coverage by declaring there were at most 177,147 different ways to tie a tie, if you make certain assumptions about what makes a legitimate tying. They’ve since revised the estimate to 266,682 kinds of knots that seem achievable.

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    • sunesiss 12:23 am on Thursday, 18 June, 2015 Permalink | Reply

      Hey Joseph thank you for stopping by my blog i really appreciate it, that was awesome of you. I nominated you for the first post challenge. dont know if you do them or have already done it, but heres the link. https://sunesiss.wordpress.com/2015/06/18/your-first-post-challenge/ i really hope you stop by!

      Like

  • Joseph Nebus 2:09 am on Saturday, 8 September, 2012 Permalink | Reply
    Tags: , binomials, , polynomials, , ,   

    Everything I Learned In Eighth-Grade Math 


    My title is an exaggeration. In eighth grade Prealgebra I learned many things, but I confess that I didn’t learn well from that particular teacher that particular year. What I most clearly remember learning I picked up from a substitute who filled in a few weeks. It’s a method for factoring quadratic expressions into binomial expressions, and I must admit, it’s not very good. It’s cumbersome and totally useless once one knows the quadratic equation. But it’s fun to do, and I liked it a lot, and I’ve never seen it described as a way to factor quadratic expressions. So let me put it on the web and do what I can to preserve its legacy, and get hundreds of people telling me what it actually is and how everybody but the people I know went through a phase of using it.

    It’s a method which looks at first like it’s going to be a magic square, but it’s not, and I’m at a loss what to call it. I don’t remember the substitute teacher’s name, so I can’t use that. I do remember the regular teacher’s name, but it wasn’t, as far as I know, part of his lesson plan, and it’d not be fair to him to let his legacy be defined by one student who just didn’t get him.

    (More …)

     
    • educationrealist 5:07 am on Saturday, 8 September, 2012 Permalink | Reply

      Is there any reason why you wouldn’t use the generic rectangle method instead? I find this method far more convoluted, and it covers the same material.

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      • Joseph Nebus 5:11 pm on Saturday, 8 September, 2012 Permalink | Reply

        Well, I don’t know the rectangle method, at least not by that name.

        I can’t defend this as a factoring method on any grounds, really, except that it’s how I first learned to do factoring systematically (or semi-systematically), and I hadn’t wanted it to be completely lost to time.

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        • educationrealist 12:43 am on Sunday, 9 September, 2012 Permalink | Reply

          No need to defend it. I think a method is essential for lower ability kids, and wasn’t criticizing you at all. I know teachers who use this method in preference to the rectangle (also known as box and diamond) and have just never been sure why, as it seems to have a bit more complexity with no added benefit. I thought maybe you could shed some insight.

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          • Joseph Nebus 11:32 pm on Monday, 10 September, 2012 Permalink | Reply

            Oh, I didn’t feel criticized, and please don’t worry about that.

            I hadn’t encountered the box-and-diamond method, as best as I know, before, but now that I know what to search for it does look like a more straightforward method. Why that wasn’t what we got back in middle school I couldn’t guess; maybe it was just how my substitute was first taught factoring.

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            • educationrealist 3:10 am on Tuesday, 11 September, 2012 Permalink | Reply

              If you’re interested, I have a long and short doc that takes students through the procedure. It’s a step by step document you might find helpful.

              Like

    • Blinky the Wonder Wombat 6:21 pm on Monday, 10 September, 2012 Permalink | Reply

      After determining the two x terms, I’ve found it easier to just insert them into the original equation thus:

      10x^2 -15x+8x-12

      Look for a common factor in the first term and one of the two middle terms, in this case, 5x:

      5x(2x-3) + 8x-12

      Now find a common factor in the other two terms, in this case, 4:

      5x(2x-3) +4(2x-3)

      Hey look, (2x-3) is common to both sides! Factor it out and get:

      (2x-3) * (5x+4)

      I found that this method seemed a little more logical to my children.

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      • Joseph Nebus 11:34 pm on Monday, 10 September, 2012 Permalink | Reply

        Mm, yes, looking for the common factor between the first term and either of the intermediate ones does work out. That might be easier for children to learn. It does avoid another two rounds of guess-the-factoring for the horizontal rows.

        ‘Course, it does leave the rest of the little boxes un-filled, and that seems like a shame or an invitation to tic-tac-toe. But there’s always something lost in adapting methods, isn’t there?

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    • educationrealist 11:31 am on Friday, 14 September, 2012 Permalink | Reply

      I wrote up the whole method here: https://educationrealist.wordpress.com/2012/09/14/binomial-multiplication-and-factoring-trinomials-with-the-rectangle/

      but the handout I was referring to, which is procedural only, is the last link on the page. I’ve been meaning to write up the approach for a while, so thanks for the impetus.

      Like

  • Joseph Nebus 7:39 pm on Sunday, 20 May, 2012 Permalink | Reply
    Tags: , , Bo Nanas, Borel, Bradley Trevor Greive, , Citizen Dog, , , , , denominator, Ed Allison, , , fraction, , Guy Endor-Kaiser, , Jef Mallet, John Kovaleski, Jorge Luis Borges, Kid City, , Latin, , , Mark O'Hare, , numerator, , polynomials, , , Rudy Park, Steve McGarry, The Lost Bear, Theron Heir, Unstrange Phenomena   

    Reading The Comics, May 20, 2012 


    Since I suspect that the comics roundup posts are the most popular ones I post, I’m very glad to see there was a bumper crop of strips among the ones I read regularly (from King Features Syndicate and from gocomics.com) this past week. Some of those were from cancelled strips in perpetual reruns, but that’s fine, I think: there aren’t any particular limits on how big an electronic comics page one can have, after all, and while it’s possible to read a short-lived strip long enough that you see all its entries, it takes a couple go-rounds to actually have them all memorized.

    The first entry, and one from one of these cancelled strips, comes from Mark O’Hare’s Citizen Dog, a charmer of a comic set in a world-plus-talking-animals strip. In this case Fergus has taken the place of Maggie, a girl who’s not quite ready to come back from summer vacation. It’s also the sort of series of questions that it feels like come at the start of any class where a homework assignment’s due.

    (More …)

     
  • Joseph Nebus 1:35 am on Saturday, 5 May, 2012 Permalink | Reply
    Tags: algebraic manipulations, careless mistakes, , , date, , , month, polynomials, , , translation,   

    What We Mean By x 


    [ Oh, wow. Yesterday’s entry had way fewer hits than average. I also put an equation out right up front where everyone could see it. I wonder if this might be a test of Stephen Hawking’s dictum about equations and sales. Or maybe I was just boring yesterday. I’d ask, but apparently, nobody found me interesting enough yesterday to know for comparison. ]

    It shouldn’t be too hard to translate the the idea “I want to know the population of Charlotte at some particular time” into a polynomial. The polynomial ought to look something like y equals some pile of numbers times x’s raised to powers, and x somehow has to do with the particular time, and y has something to do with the population. And it’s not hard to do that translating, but I want to talk about some deeper issues. It’s probably better explaining them on the simple problem, where we know what we want things to mean, than it would be explaining them for a complicated problem.

    (More …)

     
  • Joseph Nebus 1:04 am on Wednesday, 2 May, 2012 Permalink | Reply
    Tags: continuous, , , polynomials, , trigonometric polynomials   

    Why Do We Like Polynomials? 


    Polynomials turn up all over the place. There are multiple good reasons for this. For one, suppose we have any continuous function that we want to study. (“Continuous” has a technical definition, although if you imagine what we might mean by that in ordinary English — that we could draw it without having to lift pen from paper — you’ve got it, apart from freak cases designed to confuse students taking real analysis by making continuous functions that don’t look anything like something you could ever draw, which is jolly good fun until the grades are returned.) If we’re willing to accept a certain margin of error around that function, though, we can always find a polynomial that’s within that margin of error of the function we really want to study. I have read, albeit in secondary sources, that for a while in the 18th century it was thought that a mathematician could just as well define a function as “something that a polynomial can approximate”.

    (More …)

     
  • Joseph Nebus 2:25 am on Thursday, 6 October, 2011 Permalink | Reply
    Tags: binomial, , , polynomials   

    In Defense Of FOIL 


    I do sometimes read online forums of educators, particularly math educators, since it’s fun to have somewhere to talk shop, and the topics of conversation are constant enough you don’t have to spend much time getting the flavor of a particular group before participating. If you suppose the students are lazy, the administrators meddling, the community unsupportive, and the public irrationally terrified of mathematics you’ve covered most forum threads. I had no luck holding forth my view on one particular topic, though, so I’ll try fighting again here where I can easily squelch the opposition.

    The argument, a subset of students-are-lazy (as they don’t wish to understand mathematics), was about a mnemonic technique called FOIL. It’s a tool to help people multiply binomials. Binomials are the sum (or difference) of two quantities, for example, (a + 2) or (b + 5). Here a and b are numbers whose value I don’t care about; I don’t care about the 2 or 5 either, but by picking specific values I avoid having too much abstraction in my paragraph. The product of (a + 2) with (b + 5) is the sum of all the pairs made by multiplying one term in the first binomial by one term in the second. There are four such pairs: a times b, and a times 5, and 2 times b, and 2 times 5. And therefore the product (a + 2) * (b + 5) will be a*b + a*5 + 2*b + 2*5. That would usually be cleaned up by writing 5*a instead of a*5, and by writing 10 instead of 2*5, so the sum would become a*b + 5*a + 2*b + 10.

    FOIL is a way of making sure one has covered all the pairs. The letters stand for First, Outer, Inner, Last, and they mean: take the product of the First terms in each binomial, a and b; and those of the Outer terms, a and 5; and those of the Inner terms, 2 and b; and those of the Last terms, 2 and 5.

    Here is my distinguished colleague’s objection to FOIL: Nobody needs it. This is true.

    (More …)

     
    • Geoffrey Brent 2:53 am on Thursday, 6 October, 2011 Permalink | Reply

      I still use FOIL occasionally; as you say, while it doesn’t apply directly to trinomials etc, it’s a useful habit for binomials. Which then leaves me with more time to get the trinomials right.

      Even when multiplying longer expressions together, I do it as sigma-over-i(sigma-over-j(i*j)), and FOIL is a special case of that approach, so it’s not like I’m really changing styles. Just generalising the approach.

      Like

      • nebusresearch 4:57 am on Friday, 7 October, 2011 Permalink | Reply

        You know, this has reminded me: one of my mathematics teachers did point out how to use FOIL for trinomials (and higher terms), by rewriting a product of trinomials (a + (c + 2)) * (b + (d + 5)), and using a parenthetical expression and so recursion for the second part there. Of course nothing was said about “recursion”, as that would have been terrifying, but we did come out with the process being tediously long but never hard.

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    • David Brodbeck 3:17 am on Thursday, 6 October, 2011 Permalink | Reply

      I agree. I’m reminded of an electronics teacher who taught me a mnemonic for calculating power from current and resistance: “Twinkle, twinkle, little star / power equals I squared R.”

      Now, it’s fairly easy to derive P = I^2 * R from Ohm’s law, but this is a particularly common case and it’s nice to have a way to remember it specifically. FOIL strikes me as a similar sort of mental tool.

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      • nebusresearch 4:54 am on Friday, 7 October, 2011 Permalink | Reply

        It seems along those lines to me. Ohm’s Law reminds me of a mnemonic tool that literally uses pie charts, too: you drawa circle divided as in a medieval T-O map, with “E” in the upper half, “I” in the lower left quadrant, and “R” in the lower right quadrant. Take out the quantity you want; what’s left is the operation to get that … so “E” is “I R”; “I” is “E / R”, and “R” is “E / I”. And, of course, you get the same with P-I-E.

        Back in middle school I learned a weird magic square-based scheme for factoring polynomials that I’ve never seen anyone else talk about ever. I should make that an entry someday, so it’s not lost to the ages.

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    • BunnyHugger 5:50 pm on Thursday, 6 October, 2011 Permalink | Reply

      “If you suppose the students are lazy, the administrators meddling, the community unsupportive, and the public irrationally terrified of mathematics you’ve covered most forum threads.”

      Mutatis mutandis, this is what philosophy profession forums look like. And there’s a reason: all these things are substantially true.

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      • nebusresearch 4:50 am on Friday, 7 October, 2011 Permalink | Reply

        Sssh! I’m just starting my first class in years next week. I need to hold on to my wild optimism about the experience.

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    • MJ Howard (@random_bunny) 1:21 am on Friday, 7 October, 2011 Permalink | Reply

      So, is the objection that FOIL is bad because it doesn’t solve the general case of polynomial multiplication? Isn’t that the same as saying that the Pythagorean Theorem is bad because it doesn’t solve the unknown side length problem for all triangles? Similarly that the Power Rule is bad because it won’t solve all problems of derivation?

      In the interest of disclosure, I am not a real Mathematician since I really only have a Bachelor’s in Applied.

      Thinking about it, I’m not sure which I’d rather not do by hand: figure out the value of cosine for an angle other than the ones you memorized the answer for or compute the square root of a Real number. Both to some arbitrary level of precision.

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      • nebusresearch 4:48 am on Friday, 7 October, 2011 Permalink | Reply

        The objection as I understand it is more that since the distributive law is a more general and powerful approach, that it’s a waste of limited class time and student attention to focus on that when the teacher could go to the distributive law instead.

        I feel this assumes the general case is easier to learn than I believe it to be, but now I’m sorry I didn’t think of the Pythagorean Theorem/Law of Cosines example to use in the first past.

        I’d rather do the square root by hand, but that’s because I actually learned how to do that. I’ve seen the rules about working out cosines for arbitrary angles and done a couple of cases, but none enough to say I actually know how to do it. I was enchanted by the use of cosine angle addition formulas as a way of simplifying multiplication, though, and it shows how desperate the need for calculators was that that was ever seen as a good idea.

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