## 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:

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 a_{0}, a_{1}, a_{2}, 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 x^{2} in it, but not an x^{3} or x^{4} or x^{18} or anything like that. A third-degree polynomial has an x^{3}, 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, x^{4} 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

onMonday, 4 April, 2016 Permalink |Beautiful post!

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

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## Joseph Nebus 6:38 pm

onMonday, 4 April, 2016 Permalink |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.)

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## Some More Stuff To Read | nebusresearch 3:01 pm

onTuesday, 12 April, 2016 Permalink |[…] 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. […]

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onWednesday, 13 April, 2016 Permalink |[…] A Leap Day 2016 Mathematics A To Z: Polynomials […]

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## What Second Derivatives Are And What They Can Do For You | nebusresearch 4:10 pm

onWednesday, 7 June, 2017 Permalink |[…] 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 […]

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onThursday, 29 June, 2017 Permalink |[…] Polynomials […]

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## The Summer 2017 Mathematics A To Z: Arithmetic | nebusresearch 4:01 pm

onMonday, 31 July, 2017 Permalink |[…] Arithmetic is about the calculations we do to evaluate or solve polynomials. And polynomials are everything that humans find interesting. Arithmetic is adding and subtracting, of multiplication and division, of taking powers and taking […]

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## The Summer 2017 Mathematics A To Z: Diophantine Equations | nebusresearch 6:00 pm

onMonday, 7 August, 2017 Permalink |[…] 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 […]

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## The Summer 2017 Mathematics A To Z: Young Tableau | nebusresearch 6:00 pm

onMonday, 25 September, 2017 Permalink |[…] Young Tableaus turn up over and over in group theory. They match up with polynomials, because yeah, everything is polynomials. But they turn out to describe polynomial representations of some of the superstar groups out […]

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