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  • Joseph Nebus 6:00 pm on Friday, 14 April, 2017 Permalink | Reply
    Tags: complex numbers, , , ,   

    What Is The Logarithm of a Negative Number? 


    Learning of imaginary numbers, things created to be the square roots of negative numbers, inspired me. It probably inspires anyone who’s the sort of person who’d become a mathematician. The trick was great. I wondered could I do it? Could I find some other useful expansion of the number system?

    The square root of a complex-valued number sounded like the obvious way to go, until a little later that week when I learned that’s just some other complex-valued numbers. The next thing I hit on: how about the logarithm of a negative number? Couldn’t that be a useful expansion of numbers?

    No. It turns out you can make a sensible logarithm of negative, and complex-valued, numbers using complex-valued numbers. Same with trigonometric and inverse trig functions, tangents and arccosines and all that. There isn’t anything we can do with the normal mathematical operations that needs something bigger than the complex-valued numbers to play with. It’s possible to expand on the complex-valued numbers. We can make quaternions and some more elaborate constructs there. They don’t solve any particular shortcoming in complex-valued numbers, but they’ve got their uses. I never got anywhere near reinventing them. I don’t regret the time spent on that. There’s something useful in trying to invent something even if it fails.

    One problem with mathematics — with all intellectual fields, really — is that it’s easy, when teaching, to give the impression that this stuff is the Word of God, built into the nature of the universe and inarguable. It’s so not. The stuff we find interesting and how we describe those things are the results of human thought, attempts to say what is interesting about a thing and what is useful. And what best approximates our ideas of what we would like to know. So I was happy to see this come across my Twitter feed:

    The links to a 12-page paper by Deepak Bal, Leibniz, Bernoulli, and the Logarithms of Negative Numbers. It’s a review of how the idea of a logarithm of a negative number got developed over the course of the 18th century. And what great minds, like Gottfried Leibniz and John (I) Bernoulli argued about as they find problems with the implications of what they were doing. (There were a lot of Bernoullis doing great mathematics, and even multiple John Bernoullis. The (I) is among the ways we keep them sorted out.) It’s worth a read, I think, even if you’re not all that versed in how to calculate logarithms. (but if you’d like to be better-versed, here’s the tail end of some thoughts about that.) The process of how a good idea like this comes to be is worth knowing.

    Also: it turns out there’s not “the” logarithm of a complex-valued number. There’s infinitely many logarithms. But they’re a family, all strikingly similar, so we can pick one that’s convenient and just use that. Ask if you’re really interested.

     
  • Joseph Nebus 6:00 pm on Tuesday, 27 December, 2016 Permalink | Reply
    Tags: , complex numbers, , Riemann hypothesis,   

    The End 2016 Mathematics A To Z: Xi Function 


    I have today another request from gaurish, who’s also been good enough to give me requests for ‘Y’ and ‘Z’. I apologize for coming to this a day late. But it was Christmas and many things demanded my attention.

    Xi Function.

    We start with complex-valued numbers. People discovered them because they were useful tools to solve polynomials. They turned out to be more than useful fictions, if numbers are anything more than useful fictions. We can add and subtract them easily. Multiply and divide them less easily. We can even raise them to powers, or raise numbers to them.

    If you become a mathematics major then somewhere in Intro to Complex Analysis you’re introduced to an exotic, infinitely large sum. It’s spoken of reverently as the Riemann Zeta Function, and it connects to something named the Riemann Hypothesis. Then you remember that you’ve heard of this, because if you’re willing to become a mathematics major you’ve read mathematics popularizations. And you know the Riemann Hypothesis is an unsolved problem. It proposes something that might be true or might be false. Either way has astounding implications for the way numbers fit together.

    Riemann here is Bernard Riemann, who’s turned up often in these A To Z sequences. We saw him in spheres and in sums, leading to integrals. We’ll see him again. Riemann just covered so much of 19th century mathematics; we can’t talk about calculus without him. Zeta, Xi, and later on, Gamma are the famous Greek letters. Mathematicians fall back on them because the Roman alphabet just hasn’t got enough letters for our needs. I’m writing them out as English words instead because if you aren’t familiar with them they look like an indistinct set of squiggles. Even if you are familiar, sometimes. I got confused in researching this some because I did slip between a lowercase-xi and a lowercase-zeta in my mind. All I can plead is it’s been a hard week.

    Riemann’s Zeta function is famous. It’s easy to approach. You can write it as a sum. An infinite sum, but still, those are easy to understand. Pick a complex-valued number. I’ll call it ‘s’ because that’s the standard. Next take each of the counting numbers: 1, 2, 3, and so on. Raise each of them to the power ‘s’. And take the reciprocal, one divided by those numbers. Add all that together. You’ll get something. Might be real. Might be complex-valued. Might be zero. We know many values of ‘s’ what would give us a zero. The Riemann Hypothesis is about characterizing all the possible values of ‘s’ that give us a zero. We know some of them, so boring we call them trivial: -2, -4, -6, -8, and so on. (This looks crazy. There’s another way of writing the Riemann Zeta function which makes it obvious instead.) The Riemann Hypothesis is about whether all the proper, that is, non-boring values of ‘s’ that give us a zero are 1/2 plus some imaginary number.

    It’s a rare thing mathematicians have only one way of writing. If something’s been known and studied for a long time there are usually variations. We find different ways to write the problem. Or we find different problems which, if solved, would solve the original problem. The Riemann Xi function is an example of this.

    I’m going to spare you the formula for it. That’s in self-defense. I haven’t found an expression of the Xi function that isn’t a mess. The normal ways to write it themselves call on the Zeta function, as well as the Gamma function. The Gamma function looks like factorials, for the counting numbers. It does its own thing for other complex-valued numbers.

    That said, I’m not sure what the advantages are in looking at the Xi function. The one that people talk about is its symmetry. Its value at a particular complex-valued number ‘s’ is the same as its value at the number ‘1 – s’. This may not seem like much. But it gives us this way of rewriting the Riemann Hypothesis. Imagine all the complex-valued numbers with the same imaginary part. That is, all the numbers that we could write as, say, ‘x + 4i’, where ‘x’ is some real number. If the size of the value of Xi, evaluated at ‘x + 4i’, always increases as ‘x’ starts out equal to 1/2 and increases, then the Riemann hypothesis is true. (This has to be true not just for ‘x + 4i’, but for all possible imaginary numbers. So, ‘x + 5i’, and ‘x + 6i’, and even ‘x + 4.1 i’ and so on. But it’s easier to start with a single example.)

    Or another way to write it. Suppose the size of the value of Xi, evaluated at ‘x + 4i’ (or whatever), always gets smaller as ‘x’ starts out at a negative infinitely large number and keeps increasing all the way to 1/2. If that’s true, and true for every imaginary number, including ‘x – i’, then the Riemann hypothesis is true.

    And it turns out if the Riemann hypothesis is true we can prove the two cases above. We’d write the theorem about this in our papers with the start ‘The Following Are Equivalent’. In our notes we’d write ‘TFAE’, which is just as good. Then we’d take which ever of them seemed easiest to prove and find out it isn’t that easy after all. But if we do get through we declare ourselves fortunate, sit back feeling triumphant, and consider going out somewhere to celebrate. But we haven’t got any of these alternatives solved yet. None of the equivalent ways to write it has helped so far.

    We know some some things. For example, we know there are infinitely many roots for the Xi function with a real part that’s 1/2. This is what we’d need for the Riemann hypothesis to be true. But we don’t know that all of them are.

    The Xi function isn’t entirely about what it can tell us for the Zeta function. The Xi function has its own exotic and wonderful properties. In a 2009 paper on arxiv.org, for example, Drs Yang-Hui He, Vishnu Jejjala, and Djordje Minic describe how if the zeroes of the Xi function are all exactly where we expect them to be then we learn something about a particular kind of string theory. I admit not knowing just what to say about a genus-one free energy of the topological string past what I have read in this paper. In another paper they write of how the zeroes of the Xi function correspond to the description of the behavior for a quantum-mechanical operator that I just can’t find a way to describe clearly in under three thousand words.

    But mathematicians often speak of the strangeness that mathematical constructs can match reality so well. And here is surely a powerful one. We learned of the Riemann Hypothesis originally by studying how many prime numbers there are compared to the counting numbers. If it’s true, then the physics of the universe may be set up one particular way. Is that not astounding?

     
    • gaurish 5:34 am on Wednesday, 28 December, 2016 Permalink | Reply

      Yes it’s astounding. You have a very nice talent of talking about mathematical quantities without showing formulas :)

      Liked by 1 person

      • Joseph Nebus 5:15 am on Thursday, 5 January, 2017 Permalink | Reply

        You’re most kind, thank you. I’ve probably gone overboard in avoiding formulas lately though.

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  • Joseph Nebus 3:00 pm on Wednesday, 6 April, 2016 Permalink | Reply
    Tags: , complex numbers, Dublin, , , , , rotations,   

    A Leap Day 2016 Mathematics A To Z: Quaternion 


    I’ve got another request from Gaurish today. And it’s a word I had been thinking to do anyway. When one looks for mathematical terms starting with ‘q’ this is one that stands out. I’m a little surprised I didn’t do it for last summer’s A To Z. But here it is at last:

    Quaternion.

    I remember the seizing of my imagination the summer I learned imaginary numbers. If we could define a number i, so that i-squared equalled negative 1, and work out arithmetic which made sense out of that, why not do it again? Complex-valued numbers are great. Why not something more? Maybe we could also have some other non-real number. I reached deep into my imagination and picked j as its name. It could be something else. Maybe the logarithm of -1. Maybe the square root of i. Maybe something else. And maybe we could build arithmetic with a whole second other non-real number.

    My hopes of this brilliant idea petered out over the summer. It’s easy to imagine a super-complex number, something that’s “1 + 2i + 3j”. And it’s easy to work out adding two super-complex numbers like this together. But multiplying them together? What should i times j be? I couldn’t solve the problem. Also I learned that we didn’t need another number to be the logarithm of -1. It would be π times i. (Or some other numbers. There’s some surprising stuff in logarithms of negative or of complex-valued numbers.) We also don’t need something special to be the square root of i, either. \frac{1}{2}\sqrt{2} + \frac{1}{2}\sqrt{2}\imath will do. (So will another number.) So I shelved the project.

    Even if I hadn’t given up, I wouldn’t have invented something. Not along those lines. Finer minds had done the same work and had found a way to do it. The most famous of these is the quaternions. It has a famous discovery. Sir William Rowan Hamilton — the namesake of “Hamiltonian mechanics”, so you already know what a fantastic mind he was — had a flash of insight that’s come down in the folklore and romance of mathematical history. He had the idea on the 16th of October, 1843, while walking with his wife along the Royal Canal, in Dublin, Ireland. While walking across the bridge he saw what was missing. It seems he lacked pencil and paper. He carved it into the bridge:

    i^2 = j^2 = k^2 = ijk = -1

    The bridge now has a plaque commemorating the moment. You can’t make a sensible system with two non-real numbers. But three? Three works.

    And they are a mysterious three! i, j, and k are somehow not the same number. But each of them, multiplied by themselves, gives us -1. And the product of the three is -1. They are even more mysterious. To work sensibly, i times j can’t be the same thing as j times i. Instead, i times j equals minus j times i. And j times k equals minus k times j. And k times i equals minus i times k. We must give up commutivity, the idea that the order in which we multiply things doesn’t matter.

    But if we’re willing to accept that the order matters, then quaternions are well-behaved things. We can add and subtract them just as we would think to do if we didn’t know they were strange constructs. If we keep the funny rules about the products of i and j and k straight, then we can multiply them as easily as we multiply polynomials together. We can even divide them. We can do all the things we do with real numbers, only with these odd sets of four real numbers.

    The way they look, that pattern of 1 + 2i + 3j + 4k, makes them look a lot like vectors. And we can use them like vectors pointing to stuff in three-dimensional space. It’s not quite a comfortable fit, though. That plain old real number at the start of things seems like it ought to signify something, but it doesn’t. In practice, it doesn’t give us anything that regular old vectors don’t. And vectors allow us to ponder not just three- or maybe four-dimensional spaces, but as many as we need. You might wonder why we need more than four dimensions, even allowing for time. It’s because if we want to track a lot of interacting things, it’s surprisingly useful to put them all into one big vector in a very high-dimension space. It’s hard to draw, but the mathematics is nice. Hamiltonian mechanics, particularly, almost beg for it.

    That’s not to call them useless, or even a niche interest. They do some things fantastically well. One of them is rotations. We can represent rotating a point around an arbitrary axis by an arbitrary angle as the multiplication of quaternions. There are many ways to calculate rotations. But if we need to do three-dimensional rotations this is a great one because it’s easy to understand and easier to program. And as you’d imagine, being able to calculate what rotations do is useful in all sorts of applications.

    They’ve got good uses in number theory too, as they correspond well to the different ways to solve problems, often polynomials. They’re also popular in group theory. They might be the simplest rings that work like arithmetic but that don’t commute. So they can serve as ways to learn properties of more exotic ring structures.

    Knowing of these marvelous exotic creatures of the deep mathematics your imagination might be fired. Can we do this again? Can we make something with, say, four unreal numbers? No, no we can’t. Four won’t work. Nor will five. If we keep going, though, we do hit upon success with seven unreal numbers.

    This is a set called the octonions. Hamilton had barely worked out the scheme for quaternions when John T Graves, a friend of his at least up through the 16th of December, 1843, wrote of this new scheme. (Graves didn’t publish before Arthur Cayley did. Cayley’s one of those unspeakably prolific 19th century mathematicians. He has at least 967 papers to his credit. And he was a lawyer doing mathematics on the side for about 250 of those papers. This depresses every mathematician who ponders it these days.)

    But where quaternions are peculiar, octonions are really peculiar. Let me call a couple quaternions p, q, and r. p times q might not be the same thing as q times r. But p times the product of q and r will be the same thing as the product of p and q itself times r. This we call associativity. Octonions don’t have that. Let me call a couple quaternions s, t, and u. s times the product of t times u may be either positive or negative the product of s and t times u. (It depends.)

    Octonions have some neat mathematical properties. But I don’t know of any general uses for them that are as catchy as understanding rotations. Not rotations in the three-dimensional world, anyway.

    Yes, yes, we can go farther still. There’s a construct called “sedenions”, which have fifteen non-real numbers on them. That’s 16 terms in each number. Where octonions are peculiar, sedenions are really peculiar. They work even less like regular old numbers than octonions do. With octonions, at least, when you multiply s by the product of s and t, you get the same number as you would multiplying s by s and then multiplying that by t. Sedenions don’t even offer that shred of normality. Besides being a way to learn about abstract algebra structures I don’t know what they’re used for.

    I also don’t know of further exotic terms along this line. It would seem to fit a pattern if there’s some 32-term construct that we can define something like multiplication for. But it would presumably be even less like regular multiplication than sedenion multiplication is. If you want to fiddle about with that please do enjoy yourself. I’d be interested to hear if you turn up anything, but I don’t expect it’ll revolutionize the way I look at numbers. Sorry. But the discovery might be the fun part anyway.

     
    • elkement (Elke Stangl) 7:04 am on Sunday, 10 April, 2016 Permalink | Reply

      I wonder if quaternions would be useful in physics – as so often describing the same physics using different math leads to new insights. I vaguely remember some articles proposed by people who wanted to ‘revive’ quaternions for physics (sometimes this was close to … uhm … ‘outsider physics’, so I was reminded of people willing to apply Lord Kelvin’s theory of smoke rings to atomic physics…), but I have not encountered them in theoretical physics courses.

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      • elkement (Elke Stangl) 7:41 am on Sunday, 10 April, 2016 Permalink | Reply

        I should post an update – before somebody points out my ignorance of history of science and tells me to check out Wikipedia :-) https://en.wikipedia.org/wiki/Quaternion
        This quote explains it:
        “From the mid-1880s, quaternions began to be displaced by vector analysis, which had been developed by Josiah Willard Gibbs, Oliver Heaviside, and Hermann von Helmholtz. Vector analysis described the same phenomena as quaternions, so it borrowed some ideas and terminology liberally from the literature of quaternions. However, vector analysis was conceptually simpler and notationally cleaner, and eventually quaternions were relegated to a minor role in mathematics and physics.”

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        • Joseph Nebus 2:57 am on Friday, 15 April, 2016 Permalink | Reply

          I was going to say, but did figure you’d get to it soon enough. And it isn’t like quaternions are wrong. If you’ve got a programming language construct for quaternions, such as because you’re using Fortran, they’ll be fine for an array of three- or four-dimensional vectors as long as you’re careful about multiplications. It’s just that if you’ve turned your system into a 3N-dimensional vector, you might as well use a vector with 3N spots, instead of an array of N quaternions.

          Liked by 1 person

  • Joseph Nebus 3:00 pm on Friday, 6 November, 2015 Permalink | Reply
    Tags: complex numbers, , , , , ,   

    The Set Tour, Part 7: Matrices 


    I feel a bit odd about this week’s guest in the Set Tour. I’ve been mostly concentrating on sets that get used as the domains or ranges for functions a lot. The ones I want to talk about here don’t tend to serve the role of domain or range. But they are used a great deal in some interesting functions. So I loosen my rule about what to talk about.

    Rm x n and Cm x n

    Rm x n might explain itself by this point. If it doesn’t, then this may help: the “x” here is the multiplication symbol. “m” and “n” are positive whole numbers. They might be the same number; they might be different. So, are we done here?

    Maybe not quite. I was fibbing a little when I said “x” was the multiplication symbol. R2 x 3 is not a longer way of saying R6, an ordered collection of six real-valued numbers. The x does represent a kind of product, though. What we mean by R2 x 3 is an ordered collection, two rows by three columns, of real-valued numbers. Say the “x” here aloud as “by” and you’re pronouncing it correctly.

    What we get is called a “matrix”. If we put into it only real-valued numbers, it’s a “real matrix”, or a “matrix of reals”. Sometimes mathematical terminology isn’t so hard to follow. Just as with vectors, Rn, it matters just how the numbers are organized. R2 x 3 means something completely different from what R3 x 2 means. And swapping which positions the numbers in the matrix occupy changes what matrix we have, as you might expect.

    You can add together matrices, exactly as you can add together vectors. The same rules even apply. You can only add together two matrices of the same size. They have to have the same number of rows and the same number of columns. You add them by adding together the numbers in the corresponding slots. It’s exactly what you would do if you went in without preconceptions.

    You can also multiply a matrix by a single number. We called this scalar multiplication back when we were working with vectors. With matrices, we call this scalar multiplication. If it strikes you that we could see vectors as a kind of matrix, yes, we can. Sometimes that’s wise. We can see a vector as a matrix in the set R1 x n or as one in the set Rn x 1, depending on just what we mean to do.

    It’s trickier to multiply two matrices together. As with vectors multiplying the numbers in corresponding positions together doesn’t give us anything. What we do instead is a time-consuming but not actually hard process. But according to its rules, something in Rm x n we can multiply by something in Rn x k. “k” is another whole number. The second thing has to have exactly as many rows as the first thing has columns. What we get is a matrix in Rm x k.

    I grant you maybe didn’t see that coming. Also a potential complication: if you can multiply something in Rm x n by something in Rn x k, can you multiply the thing in Rn x k by the thing in Rm x n? … No, not unless k and m are the same number. Even if they are, you can’t count on getting the same product. Matrices are weird things this way. They’re also gateways to weirder things. But it is a productive weirdness, and I’ll explain why in a few paragraphs.

    A matrix is a way of organizing terms. Those terms can be anything. Real matrices are surely the most common kind of matrix, at least in mathematical usage. Next in common use would be complex-valued matrices, much like how we get complex-valued vectors. These are written Cm x n. A complex-valued matrix is different from a real-valued matrix. The terms inside the matrix can be complex-valued numbers, instead of real-valued numbers. Again, sometimes, these mathematical terms aren’t so tricky.

    I’ve heard occasionally of people organizing matrices of other sets. The notation is similar. If you’re building a matrix of “m” rows and “n” columns out of the things you find inside a set we’ll call H, then you write that as Hm x n. I’m not saying you should do this, just that if you need to, that’s how to tell people what you’re doing.

    Now. We don’t really have a lot of functions that use matrices as domains, and I can think of fewer that use matrices as ranges. There are a couple of valuable ones, ones so valuable they get special names like “eigenvalue” and “eigenvector”. (Don’t worry about what those are.) They take in Rm x n or Cm x n and return a set of real- or complex-valued numbers, or real- or complex-valued vectors. Not even those, actually. Eigenvectors and eigenfunctions are only meaningful if there are exactly as many rows as columns. That is, for Rm x m and Cm x m. These are known as “square” matrices, just as you might guess if you were shaken awake and ordered to say what you guessed a “square matrix” might be.

    They’re important functions. There are some other important functions, with names like “rank” and “condition number” and the like. But they’re not many. I believe they’re not even thought of as functions, any more than we think of “the length of a vector” as primarily a function. They’re just properties of these matrices, that’s all.

    So why are they worth knowing? Besides the joy that comes of knowing something, I mean?

    Here’s one answer, and the one that I find most compelling. There is cultural bias in this: I come from an applications-heavy mathematical heritage. We like differential equations, which study how stuff changes in time and in space. It’s very easy to go from differential equations to ordered sets of equations. The first equation may describe how the position of particle 1 changes in time. It might describe how the velocity of the fluid moving past point 1 changes in time. It might describe how the temperature measured by sensor 1 changes as it moves. It doesn’t matter. We get a set of these equations together and we have a majestic set of differential equations.

    Now, the dirty little secret of differential equations: we can’t solve them. Most interesting physical phenomena are nonlinear. Linear stuff is easy. Small change 1 has effect A; small change 2 has effect B. If we make small change 1 and small change 2 together, this has effect A plus B. Nonlinear stuff, though … it just doesn’t work. Small change 1 has effect A; small change 2 has effect B. Small change 1 and small change 2 together has effect … A plus B plus some weird A times B thing plus some effect C that nobody saw coming and then C does something with A and B and now maybe we’d best hide.

    There are some nonlinear differential equations we can solve. Those are the result of heroic work and brilliant insights. Compared to all the things we would like to solve there’s not many of them. Methods to solve nonlinear differential equations are as precious as ways to slay krakens.

    But here’s what we can do. What we usually like to know about in systems are equilibriums. Those are the conditions in which the system stops changing. Those are interesting. We can usually find those points by boring but not conceptually challenging calculations. If we can’t, we can declare x0 represents the equilibrium. If we still care, we leave calculating its actual values to the interested reader or hungry grad student.

    But what’s really interesting is: what happens if we’re near but not exactly at the equilibrium? Sometimes, we stay near it. Think of pushing a swing. However good a push you give, it’s going to settle back to the boring old equilibrium of dangling straight down. Sometimes, we go racing away from it. Think of trying to balance a pencil on its tip; if we did this perfectly it would stay balanced. It never does. We’re never perfect, or there’s some wind or somebody walks by and the perfect balance is foiled. It falls down and doesn’t bounce back up. Sometimes, whether it it stays near or goes away depends on what way it’s away from the equilibrium.

    And now we finally get back to matrices. Suppose we are starting out near an equilibrium. We can, usually, approximate the differential equations that describe what will happen. The approximation may only be good if we’re just a tiny bit away from the equilibrium, but that might be all we really want to know. That approximation will be some linear differential equations. (If they’re not, then we’re just wasting our time.) And that system of linear differential equations we can describe using matrices.

    If we can write what we are interested in as a set of linear differential equations, then we have won. We can use the many powerful tools of matrix arithmetic — linear algebra, specifically — to tell us everything we want to know about the system. We can say whether a small push away from the equilibrium stays small, or whether it grows, or whether it depends. We can say how fast the small push shrinks, or grows (for a while). We can say how the system will change, approximately.

    This is what I love in matrices. It’s not everything there is to them. But it’s enough to make matrices important to me.

     
  • Joseph Nebus 2:00 pm on Thursday, 15 October, 2015 Permalink | Reply
    Tags: , complex numbers, ,   

    The Set Tour, Part 4: Complex Numbers 


    C

    The square root of negative one. Everybody knows it doesn’t exist; there’s no real number you can multiply by itself and get negative one out. But then sometime in algebra, deep in a section about polynomials, suddenly we come out and declare there is such a thing. It’s an “imaginary number” that we call “i”. It’s hard to blame students for feeling betrayed by this. To make it worse, we throw real and imaginary numbers together and call the result “complex numbers”. It’s as if we’re out to tease them for feeling confused.

    It’s an important set of things, though. It turns up as the domain, or the range, of functions so often that one of the major fields of analysis is called, “Complex Analysis”. If the course listing allows for more words, it’s called “Analysis of Functions of a Complex Variable” or something like that. Despite the connotations of the word “complex”, though, the field is a delight. It’s considerably easier to understand than Real Analysis, the study of functions of mere real numbers. When there is a theorem that has a version in Real Analysis and a version in Complex Analysis, the Complex Analysis side is usually easier to prove and easier to understand. It’s uncanny.

    The set of all complex numbers is denoted C, in parallel to the set of real numbers, R. To make it clear that we mean this set, and not some piddling little common set that might happen to share the name C, add a vertical stroke to the left of the letter. This is just as we add a vertical stroke to R to emphasize we mean the Real Numbers. We should approach the set with respect, removing our hats, thinking seriously about great things. It would look silly to add a second curve to C though, so we just add a straight vertical stroke on the left side of the letter C. This makes it look a bit like it’s an Old English typeface (the kind you call Gothic until you learn that means “sans serif”) pared down to its minimum.

    Why do we teach people there’s no such thing as a square root of minus one, and then one day, teach them there is? Part of it is that whether there is a square root depends on your context. If you are interested only in the real numbers, there’s nothing that, squared, gives you minus one. This is exactly the way that it’s not possible to equally divide five objects between two people if you aren’t allowed to cut the objects in half. But if you are willing to allow half-objects to be things, then you can do what was previously forbidden. What you can do depends on what the rules you set out are.

    And there’s surely some echo of the historical discovery of imaginary and complex numbers at work here. They were noticed when working out the roots of third- and fourth-degree polynomials. These can be done by way of formulas that nobody ever remembers because there are so many better things to remember. These formulas would sometimes require one to calculate a square root of a negative number, a thing that obviously didn’t exist. Except that if you pretended it did, you could get out correct answers, just as if these were ordinary numbers. You can see why this may be dubbed an “imaginary” number. The name hints at the suspicion with which it’s viewed. It’s much as “negative” numbers look like some trap to people who’re just getting comfortable with fractions.

    It goes against the stereotype of mathematicians to suppose they’d accept working with something they don’t understand because the results are all right, afterwards. But, actually, mathematicians are willing to accept getting answers by any crazy method. If you have a plausible answer, you can test whether it’s right, and if all you really need this minute is the right answer, good.

    But we do like having methods; they’re more useful than mere answers. And we can imagine this set called the complex numbers. They contain … well, all the possible roots, the solutions, of all polynomials. (The polynomials might have coefficients — the numbers in front of the variable — of integers, or rational numbers, or irrational numbers. If we already accept the idea of complex numbers, the coefficients can be complex numbers too.)

    It’s exceedingly common to think of the complex numbers by starting off with a new number called “i”. This is a number about which we know nothing except that i times i equals minus one. Then we tend to think of complex numbers as “a real number plus i times another real number”. The first real number gets called “the real component”, and is usually denoted as either “a” or “x”. The second real number gets called “the imaginary component”, and is usually denoted as either “b” or “y”. Then the complex number is written “a + i*b” or “x + i*y”. Sometimes it’s written “a + b*i” or “x + y*i”; that’s a mere matter of house style. Don’t let it throw you.

    Writing a complex number this way has advantages. Particularly, it makes it easy to see how one would add together (or subtract) complex numbers: “a + b*i + x + y*i” almost suggests that the sum should be “(a + x) + (b + y)*i”. What we know from ordinary arithmetic gives us guidance. And if we’re comfortable with binomials, then we know how to multiply complex numbers. Start with “(a + b*i) * (x + y*i)” and follow the distributive law. We get, first, “a*x + a*y*i + b*i*x + b*y*i*i”. But “i*i” equals minus one, so this is the same as “a*x + a*y*i + b*i*x – b*y”. Move the real components together, and move the imaginary components together, and we have “(a*x – b*y) + (a*y + b*x)*i”.

    That’s the most common way of writing out complex numbers. It’s so common that Eric W Weisstein’s Mathworld encyclopedia even says that’s what complex numbers are. But it isn’t the only way to construct, or look at, complex numbers. A common alternate way to look at complex numbers is to match a complex number to a point on the plane, or if you prefer, a point in the set R2.

    It’s surprisingly natural to think of the real component as how far to the right or left of an origin your complex number is, and to think of the imaginary component as how far above or below the origin it is. Much complex-number work makes sense if you think of complex numbers as points in space, or directions in space. The language of vectors trips us up only a little bit here. We speak of a complex number as corresponding to a point on the “complex plane”, just as we might speak of a real number as a point on the “(real) number line”.

    But there are other descriptions yet. We can represent complex numbers as a pair of numbers with a scheme that looks like polar coordinates. Pick a point on the complex plane. We can say where that is by two points of information. The first is the amplitude, or magnitude: how far the point is from the origin. The second is the phase, or angle: draw the line segment connecting the origin and your point. What angle does that make with the positive horizontal axis?

    This representation is called the “phasor” representation. It’s tolerably popular in physics and I hear tell of engineers liking it. We represent numbers then not as “x + i*y” but instead as “r * e”, with r the magnitude and θ the angle. “e” is the base of the natural logarithm, which you get very comfortable with if you do much mathematics or physics. And “i” is just what we’ve been talking about here. This is a pretty natural way to write about complex numbers that represent stuff that oscillates, such as alternating current or the probability function in quantum mechanics. A lot of stuff oscillates, if you study it through the right lens. So numbers that look like this keep creeping in, and into unexpected places. It’s quite easy to multiply numbers in phasor form — just multiply the magnitude parts, and add the angle parts — although addition and subtraction become a pain.

    Mathematicians generally use the letter “z” to represent a complex-valued number whose identity is not known. As best I can tell, this is because we do think so much of a complex number as the sum “x + y*i”. So if we used familiar old “x” for an unknown number, it would carry the connotations of “the real component of our complex-valued number” and mislead the unwary mathematician. The connection is so common that a mathematician might carelessly switch between “z” and the real and imaginary components “x” and “y” without specifying that “z” is another way of writing “x + y*i”. A good copy editor or an alert student should catch this.

    Complex numbers work very much like real numbers do. They add and multiply in natural-looking ways, and you can do subtraction and division just as well. You can take exponentials, and can define all the common arithmetic functions — sines and cosines, square roots and logarithms, integrals and differentials — on them just as well as you can with real numbers. And you can embed the real numbers within the complex numbers: if you have a real number x, you can match that perfectly with the complex number “x + 0*i”.

    But that doesn’t mean complex numbers are exactly like the real numbers. For example, it’s possible to order the real numbers. You can say that the number “a” is less than the number “b”, and have that mean something. That’s not possible to do with complex numbers. You can’t say that “a + b*i” is less than, or greater than, “x + y*i” in a logically consistent way. You can say the magnitude of one complex-valued number is greater than the magnitude of another. But the magnitudes are real numbers. For all that complex numbers give us there are things they’re not good for.

     
    • howardat58 3:11 pm on Thursday, 15 October, 2015 Permalink | Reply

      Hi Joseph
      When I first encountered complex numbers the square root of -1 was wild but one could suspend belief.
      What I found weird was “r * eiθ”. The sudden appearance of e was a shock! More attention to the polar form and deMoivre’s Theorem first would have helped.

      Liked by 1 person

      • Joseph Nebus 3:16 pm on Saturday, 17 October, 2015 Permalink | Reply

        The peculiar thing, to me, is that I don’t remember feeling shock at either actually. I believe my pre-algebra or algebra teacher introduced ‘i’ by explaining something along the lines of, “we’re going to introduce a new kind of number with this property. If we treat it in this way, we get something useful”, which makes it a lot easier to approach.

        And then given that the r*e thing … well, it’s hardly inevitable. But it at least didn’t feel like something so wild we couldn’t possibly accept it. I don’t remember how we were eased into it. If we had infinite series then working out a couple of sample cases by a Taylor series for the exponential would be convincing. But I’m sure we didn’t have Taylor series until after we got the polar form. So I’m left, again, with a bit of a mystery about just how I learned mathematics.

        Liked by 2 people

    • mathtuition88 5:12 am on Saturday, 24 October, 2015 Permalink | Reply

      Yeah complex analysis is much nicer than real analysis

      Like

      • howardat58 11:15 am on Saturday, 24 October, 2015 Permalink | Reply

        It was with some disappointment that I realised that in a rather loose sense there were not very many analytic functions.

        Liked by 1 person

        • mathtuition88 11:50 am on Saturday, 24 October, 2015 Permalink | Reply

          Yes, real analysis has too many exceptions, the entire theory is full of surprising counter examples..

          Liked by 1 person

          • Joseph Nebus 5:26 am on Monday, 26 October, 2015 Permalink | Reply

            Yeah, it’s kind of strange. Real analysis also feels like it’s dependent on a bundle of neat tricks, for proving that the difference between what you have and what you want will be arbitrarily small. Those I don’t remember being taught as a coherent set of tools; we just had to pick them up from seeing them over and over.

            Liked by 1 person

        • Joseph Nebus 5:27 am on Monday, 26 October, 2015 Permalink | Reply

          Oh, now, complex analytic functions — entire functions — disappointed me when I was learning about them. At least, the ones that are supposed to be entire on the entire complex plane felt like such a sadly limited group.

          Like

      • Joseph Nebus 5:32 am on Monday, 26 October, 2015 Permalink | Reply

        Complex analysis is just a dream, compared to real analysis. Whatever you want to prove “draw a contour integral. It’s zero, plus 2 * pi * i times the function at any singularities inside! Done! Next question!”

        Liked by 1 person

        • mathtuition88 5:36 am on Monday, 26 October, 2015 Permalink | Reply

          Yeah, complex analysis is so much nicer and neater. Real analysis is full of nasty traps for the student

          Like

  • Joseph Nebus 4:00 pm on Monday, 2 July, 2012 Permalink | Reply
    Tags: , , , complex numbers, , Dave Whamond, , , Herman, Home and Away, , In The Sticks, Jim Unger, , , , Lincoln Pierce, , Nathan Cooper, , , , percentage, , , , , , space shuttle, Steve Sicula, wedding,   

    Reading the Comics, July 1, 2012 


    This will be a hastily-written installment since I married just this weekend and have other things occupying me. But there’s still comics mentioning math subjects so let me summarize them for you. The first since my last collection of these, on the 13th of June, came on the 15th, with Dave Whamond’s Reality Check, which goes into one of the minor linguistic quirks that bothers me: the claim that one can’t give “110 percent,” since 100 percent is all there is. I don’t object to phrases like “110 percent”, though, since it seems to me the baseline, the 100 percent, must be to some standard reference performance. For example, the Space Shuttle Main Engines routinely operated at around 104 percent, not because they were exceeding their theoretical limits, but because the original design thrust was found to be not quite enough, and the engines were redesigned to deliver more thrust, and it would have been far too confusing to rewrite all the documentation so that the new design thrust was the new 100 percent. Instead 100 percent was the design capacity of an engine which never flew but which existed in paper form. So I’m forgiving of “110 percent” constructions, is the important thing to me.

    (More …)

     
    • bug 3:41 am on Tuesday, 3 July, 2012 Permalink | Reply

      Oh man, I should read this more !

      While it would be simple enough to justify negative numbers through nuclear physics (i.e. every particle having an antiparticle), it’s also not that hard to consider them as deficits (“Tim lacks 3 apples”) rather than “anti-assets”. That way, they don’t actually represent anything physical, but instead a difference (ha) from one’s expectation of a physical state. This also makes a lot more sense considering their use in accounting.

      Also, I’ve never heard that engineers dislike complex numbers. They’re practically essential…

      Like

      • Joseph Nebus 10:09 pm on Thursday, 5 July, 2012 Permalink | Reply

        Treating negative numbers as positive numbers in the other direction was historically the intermediate step between just working with negative numbers. Accountants seem to have been there first, with geometers following close behind. Descartes’ original construction of the coordinate system divided the plane into the four quadrants we still have, with positive numbers in each of them, representing “right and up” in the first quadrant, “left and up” in the second, “left and down” in the third, and “right and down” in the fourth. But this ends up being a nuisance and making do with a negative sign rather than a separate tally gets to be easier fast.

        I can’t speak about the truth of electrical engineers disliking complex numbers, but it is certainly a part of mathematics folklore that if any students are going to have trouble with complex numbers, or reject them altogether, it’s more likely to be the electrical engineers. I note also the lore of the Salem Hypothesis, about the apparent predilection of engineers, particularly electrical engineers, to nutty viewpoints. (Petr Beckmann is probably the poster child for this, as he spent considerable time telling everyone Relativity was a Fraud, and he was indeed an electrical engineer.)

        Like

  • Joseph Nebus 7:39 pm on Sunday, 20 May, 2012 Permalink | Reply
    Tags: , , Bo Nanas, Borel, Bradley Trevor Greive, , Citizen Dog, , complex numbers, , , denominator, Ed Allison, , , fraction, , Guy Endor-Kaiser, , Jef Mallet, John Kovaleski, Jorge Luis Borges, Kid City, , Latin, , , Mark O'Hare, , numerator, , , , , 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 …)

     
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