# 27 Polynomials

A big part of the high-school algebra curriculum is about polynomials. In some ways, this is appropriate since polynomials played an outsized part in the historical development of mathematical theory. Indeed, the so-called “Fundamental theorem of algebra” is about polynomials.^{1}

For modelers, polynomials are a mixed bag. They are very widely used in modeling. Sometimes this is entirely appropriate, for instance the low-order polynomials that are the subject of Chapter Chapter 26. The problems come when high-order polynomials are selected for modeling purposes. Building a reliable model with high-order polynomials requires a deep knowledge of mathematics, and introduces serious potential pitfalls. Modern professional modelers learn the alternatives to high-order polynomials, but newcomers often draw on their experience in high-school and give unwarranted credence to polynomials. This chapter attempts to guide you to the ways you are likely to see polynomials in your future work and to help you avoid them when better alternatives are available.

## 27.1 Basics of polynomials with one input

A polynomial is a ** linear combination** of a particular class of functions: power-law functions with non-negative, integer exponents: 1, 2, 3, …. The individual functions are called

**, a word that echoes the construction of chemical polymers out of monomers; for instance, the material**

*monomials**polyester*is constructed by chaining together a basic chemical unit called an

*ester*

*.*

In one input, say \(x\), the monomials are \(x^1, x^2, x^3\), and so on. (There is also \(x^0\), but that is better thought of as the constant function.) An ** n-th order** polynomial has monomials up to exponent \(n\). For example, the form of a third-order polynomial is

\[a_0 + a_1 x^1 + a_2 x^2 + a_3 x^3\]

The ** domain** of polynomials, like the power-law functions they are assembled from, is the

**, that is, the entire number line \(-\infty < x < \infty\). But for the purposes of understanding the shape of high-order polynomials, it is helpful to divide the domain into three parts: a**

*real numbers***at the center and two**

*wriggly domain***to the right and left of the center.**

*tail domains*Figure 27.1 shows a 7th order polynomial—that is, the highest-order term is \(x^7\). In one of the tail domains the function value heads off to \(\infty\), in the other to \(-\infty\). This is a necessary feature of all odd-order polynomials: 1, 3, 5, 7, …

In contrast, for even-order polynomials (2, 4, 6, …) the function value in the two tail domains go in the same direction, either both to \(\infty\) (Hands up!) or both to \(-\infty\).

In the wriggly domain in Figure 27.1, there are six argmins or argmaxes.

**An \(n\)th-order polynomial can have up to \(n-1\) extrema.**

Note that the local polynomial approximations in Chapter Chapter 26 are at most 2nd order and so there is at most 1 wriggle: a unique argmax. If the approximation does not include the quadratic terms (\(x^2\) or \(y^2\)) then there is no argmax for the function.

## 27.2 Multiple inputs?

High-order polynomials are rarely used with multiple inputs. One reason is the proliferation of coefficients. For instance, here is the third-order polynomial in two inputs, \(x\), and \(y\).

\[\underbrace{b_0 + b_x x + b_y y}_\text{first-order terms} + \underbrace{b_{xy} x y + b_{xx} x^2 + b_{yy} y^2}_\text{second-order terms} + \underbrace{b_{xxy} x^2 y + b_{xyy} x y^2 + b_{xxx} x^3 + b_{yyy} y^3}_\text{third-order terms}\]

This has 10 coefficients. With so many coefficients it is hard to ascribe meaning to any of them individually. And, insofar as some feature of the function does carry meaning in terms of the modeling situation, that meaning is spread out and hard to quantify.

## 27.3 High-order approximations

The potential attraction of high-order polynomials is that, with their wriggly interior, they can take on a large number of appearances. This chameleon-like behavior has historically made them the tool of choice for understanding the behavior of approximations. That theory has motivated the use of polynomials for modeling patterns in data, but, paradoxically, has shown that high-order polynomials should **not** be the tool of choice for modeling data.^{2}

Polynomial functions lend themselves well to calculations, since the output from a polynomial function can be calculated using just the basic arithmetic functions: addition, subtraction, multiplication, and division. To illustrate, consider this polynomial: \[g(x) \equiv x - \frac{1}{6} x^3\] Since the highest-order term is \(x^3\) this is a third-order polynomial. (As you will see, we picked these particular coefficients, 0, 1, 0, -1/6, for a reason.) With such simple coefficients the polynomial is easy to handle by mental arithmetic. For instance, for \(g(x=1)\) is \(5/6\). Similarly, \(g(x=1/2) = 23/48\) and \(g(x=2) = 2/3\). A person of today’s generation would use an electronic calculator for more complicated inputs, but the mathematicians of Newton’s time were accomplished human calculators. It would have been well within their capabilities to calculate, using paper and pencil, \(g(\pi/4) = 0.7046527\).^{3}

Our example polynomial, \(g(x) \equiv x - \frac{1}{6}x^3\), graphed in color in Figure 27.2, does not look exactly like the sinusoid. If we increased the extent of the graphics domain, the disagreement would be even more striking, since the sinusoid’s output is always in \(-1 \leq \sin(x) \leq 1\), while the polynomial’s tails are heading off to \(\infty\) and \(-\infty\). But, for a small interval around \(x=0\), exactly aligns with the sinusoid.

It is clear from the graph that the approximation is excellent near \(x=0\) and gets worse as \(x\) gets larger. The approximation is poor for \(x \approx \pm 2\). We know enough about polynomials to say that the approximation will not get better for larger \(x\); the sine function has a range of \(-1\) to \(1\), while the left and right tails of the polynomial are heading off to \(\infty\) and \(-\infty\) respectively.

One way to measure the quality of the approximation is the ** error** \({\cal E}(x)\) which gives, as a function of \(x\), the difference between the actual sinusoid and the approximation: \[{\cal E}(x) \equiv |\strut\sin(x) - g(x)|\] The absolute value used in defining the error reflects our interest in how

**the approximation is from the actual function and not so much in whether the approximation is below or above the actual function. Figure 27.3 shows \({\cal E}(x)\) as a function of \(x\). Since the error is the same on both sides of \(x=0\), only the positive \(x\) domain is shown.**

*far*Figure 27.3 shows that for \(x < 0.3\), the error in the polynomial approximation to \(\sin(x)\) is in the 5th decimal place. For instance, \(\sin(0.3) = 0.2955202\) while \(g(0.3) = 0.2955000\).

That the graph of \({\cal E}(x)\) is a straight-line on log-log scales diagnoses \({\cal E}(x)\) as a power law. That is: \({\cal E}(x) = A x^p\). As always for power-law functions, we can estimate the exponent \(p\) from the slope of the graph. It is easy to see that the slope is positive, so \(p\) must also be positive.

The inevitable consequence of \({\cal E}(x)\) being a power-law function with positive \(p\) is that \(\lim_{x\rightarrow 0} {\cal E}(x) = 0\). That is, the polynomial approximation \(x - \frac{1}{6}x^3\) is *exact* as \(x \rightarrow 0\).

Throughout this book, we’ve been using straight-line approximations to functions around an input \(x_0\).

\[g(x) = f(x_0) + \partial_x f(x_0) [x-x_0]\]

One way to look at \(g(x)\) is as a straight-line function. Another way is as a first-order polynomial. This raises the question of what a second-order polynomial approximation should be. Rather than the polynomial matching just the slope of \(f(x)\) at \(x_0\), we can arrange things so that the second-order polynomial will also match the curvature of the \(f()\). Since the curvature involves only the first and second derivatives of a function, the polynomial constructed to match both the first and the second derivative will necessarily match the slope and curvature of \(f()\). This can be accomplished by setting the polynomial coefficients appropriately.

Start with a general, second-order polynomial centered around \(x_0\):

\[g(x) \equiv a_0 + a_1 [x-x_0] + a_2 [x - x_0]^2\]

The first- and second-derivatives, evaluated at \(x=x_0\) are:

\[\partial_x g(x)\left.{\Large\strut}\right|_{x=x_0} = a_1 + 2 a_2 [x - x_0] \left.{\Large\strut}\right|_{x=x_0} = a_1\]

\[\partial_{xx} g(x)\left.{\Large\strut}\right|_{x=x_0} = 2 a_2\]

Notice the 2 in the above expression. When we want to write the coefficient \(a_2\) in terms of the second derivative of \(g()\), we will end up with

\[a_2 = \frac{1}{2} \partial_{xx} g(x)\left.{\Large\strut}\right|_{x=x_0}\]

To make \(g(x)\) approximate \(f(x)\) at \(x=x_0\), we need merely set

\[a_1 = \partial_x f(x)\left.{\Large\strut}\right|_{x=x_0}\] and

\[a_2 = \frac{1}{2} \partial_{xx} f(x) \left.{\Large\strut}\right|_{x=x_0}\]

This logic can also be applied to higher-order polynomials. For instance, to match the third derivative of \(f(x)\) at \(x_0\), set

\[a_3 = \frac{1}{6} \partial_{xxx} f(x) \left.{\Large\strut}\right|_{x=x_0}\]

Remarkably, each coefficient in the approximating polynomial involves only the corresponding order of derivative. \(a_1\) involves only \(\partial_x f(x) \left.{\Large\strut}\right|_{x=x_0}\); the \(a_2\) coefficient involves only \(\partial_{xx} f(x) \left.{\Large\strut}\right|_{x=x_0}\); the \(a_3\) coefficient involves only \(\partial_{xx} f(x) \left.{\Large\strut}\right|_{x=x_0}\), and so on.

Now we can explain where the polynomial that started this section, \(x - \frac{1}{6} x^3\) came from and why those coefficients make the polynmomial approximate the sinusoid near \(x=0\).

Order | \(\sin(x)\) derivative | \(x - \frac{1}{6}x^3\) derivative |
---|---|---|

0 | \(\sin(x) \left.{\Large\strut}\right|_{x=0} = 0\) | \(\left( 1 - \frac{1}{6}x^3\right)\left.{\Large\strut}\right|_{x=0} = 0\) |

1 | \(\cos(x) \left.{\Large\strut}\right|_{x=0} = 1\) | \(\left(1 - \frac{3}{6} x^2\right) \left.{\Large\strut}\right|_{x=0}= 1\) |

2 | \(-\sin(x) \left.{\Large\strut}\right|_{x=0} = 0\) | \(\left(- \frac{6}{6} x\right) \left.{\Large\strut}\right|_{x=0} = 0\) |

3 | \(-\cos(x) \left.{\Large\strut}\right|_{x=0} = -1\) | \(- 1\left.{\Large\strut}\right|_{x=0} = -1\) |

4 | \(\sin(x) \left.{\Large\strut}\right|_{x=0} = 0\) | \(0\left.{\Large\strut}\right|_{x=0} = 0\) |

The first four derivatives of \(x - \frac{1}{6} x^3\) exactly match, at \(x=0\), the first four derivatives of \(\sin(x)\).

The polynomial constructed by matching successive derivatives of a function \(f(x)\) at some input \(x_0\) is called a ** Taylor polynomial**.

Let’s construct a 3rd-order Taylor polynomial approximation to \(f(x) = e^x\) around \(x=0\).

We know it will be a 3rd order polynomial:

\[g_{\exp}(x) \equiv a_0 + a_1 x + a_2 x^2 + a_3 x^3\]

The exponential function is particularly nice for examples because the function value and all its derivatives are identical: \(e^x\). So

\[f(x= 0) = 1\]

\[ \partial_x f(x=0) = 1\]

\[\partial_{xx} f(x=0) = 1\]

\[\partial_{xxx} f(x=0) = 1\] and so on.

The function value and derivatives of \(g_{\exp}(x)\) at \(x=0\) are:

\[g_{\exp}(x=0) = a_0\]

\[\partial_{x}g_{\exp}(x=0) = a_1\]

\[\partial_{xx}g_{\exp}(x=0) = 2 a_2\]

\[\partial_{xxx}g_{\exp}(x=0) = 2\cdot3\cdot a_3 = 6\, a_3\]

Matching these to the exponential evaluated at \(x=0\), we get

\[a_0 = 1\]

\[a_1 = 1\]

\[a_2 = \frac{1}{2}\]

\[a_3 = \frac{1}{2 \cdot 3} = \frac{1}{6}\]

Result: the 3rd-order Taylor polynomial approximation to the exponential at \(x=0\) is \[g_{\exp}(x) = 1 + x + \frac{1}{2} x^2 + \frac{1}{2\cdot 3} x^3 +\frac{1}{2\cdot 3\cdot 4} x^4\]

Figure 27.4 shows the exponential function \(e^x\) and its 3th-order Taylor polynomial approximation near \(x=0\):

The polynomial is exact at \(x=0\). The ** error** \({\cal E}(x)\) grows with increasing distance from \(x=0\):

The plot of \(\log_{10} {\cal E}(x)\) versus \(\log_{10} | x |\) in ?fig-taylor-exp-5 shows that the error grows from zero at \(x=0\) as a power-law function. Measuring the exponent of the power-law from the slope of the graph on log-log axes give \({\cal E}(x) = a |x-x_0|^5\). This is typical of Taylor polynomials: for a polynomial of degree \(n\), the error will grow as a power-law with exponent \(n+1\). This means that the higher is \(n\), the faster \(\lim_{x\rightarrow x_0}{\cal E}(x) \rightarrow 0\). On the other hand, since \({\cal E}_x\) is a power law function, as \(x\) gets further from \(x_0\) the error grows as \(\left(x-x_0\right)^{n+1}\).

## 27.4 Indeterminate forms

Let’s return to an issue that has bedeviled calculus students since Newton’s time. The example we will use is the function \[\text{sinc}(x) \equiv \frac{\sin(x)}{x}\]

The sinc() function (pronounced “sink”) is still important today, in part because of its role in converting discrete-time measurements (as in an mp3 recording of sound) into continuous signals.

What is the value of \(\text{sinc}(0)\)? One answer, favored by arithmetic teachers is that \(\text{sinc}(0)\) is meaningless, because it involves division by zero.

On the other hand, \(\sin(0) = 0\) as well, so the sinc function evaluated at zero involves 0/0. This quotient is called an ** indeterminant form**. The logic is this: Suppose we assume that \(0/0 = b\) for some number \(b\). then \(0 = 0 \times b = 0\). So any value of \(b\) would do; the value of \(0/0\) is “indeterminant.”

Still another answer is suggested by plotting out sinc(\(x\)) near \(x=0\) and reading the value off the graph: sinc(0) = 1.

`slice_plot(sin(x) / x ~ x, bounds(x=c(-10,10)), npts=500)`

The graph of sinc() looks smooth and the shape makes sense. Even if we zoom in very close to \(x=0\), the graph continues to look smooth. We call such functions ** well behaved**.

Compare the well-behaved sinc() to a very closely related function (which does not seem to be so important in applied work): \(\frac{\sin(x)}{x^3}\).

Both \(\sin(x)/x\) and \(\sin(x) / x^3\), evaluated at \(x=0\) involve a divide by zero. Both are indeterminate forms 0/0 at \(x=0\). But the graph of \(\sin(x) / x^3\) (see ?fig-sinc2) is not we will behaved. \(\sin(x) / x^3\) does not have any particular value at \(x=0\); instead, it has an asymptote.

```
slice_plot(sin(x) / x ~ x, bounds(x=c(-0.1, 0.1)), npts=500) %>%
gf_refine(scale_y_log10())
slice_plot(sin(x) / x^3 ~ x, bounds(x=c(-0.1, 0.1)), npts=500) %>%
gf_refine(scale_y_log10())
```

Since both \(\sin(x)/x\left.{\Large\strut}\right|_{x=0}\) and \(\sin(x)/x^3\left. {\Large\strut}\right|_{x=0}\) involve a divide-by-zero, the answer to the utterly different behavior of the two functions is not to be found at zero. Instead, it is to be found ** near** zero. For any non-zero value of \(x\), the arithmetic to evaluate the functions is straight-forward. Note that \(\sin(x) / x^3\) starts its mis-behavior away from zero. The slope of \(\sin(x) / x^3\) is very large near \(x=0\), while the slope of \(\sin(x) / x\) smoothly approaches zero.

Since we are interested in behavior **near** \(x=0\), a useful technique is to approximate the numerator and denominator of both functions by polynomial approximations.

- \(\sin(x) \approx x - \frac{1}{6} x^3\) near \(x=0\)
- \(x\) is already a polynomial.
- \(x^3\) is already a polynomial.

Remember, these approximations are **exact** as \(x\) goes to zero. So sufficiently close to zero,

\[\frac{\sin(x)}{x} = \frac{x - \frac{1}{6} x^3}{x} = 1 + \frac{1}{6} x^2\]

Even at \(x=0\), there is nothing indeterminant about \(1 + x^2/6\); it is simply 1.

Compare this to the polynomial approximation to \(\sin(x) / x^3\):

\[\frac{\sin(x)}{x^3} = \frac{x - \frac{1}{6} x^3}{x^3} = \frac{1}{x^2} - \frac{1}{6}\]

Evaluating this at \(x=0\) involves division by zero. No wonder it is badly behaved.

The procedure for checking whether a function involving division by zero behaves well or poorly is described in the first-ever calculus textbook, published in 1697. The title (in English) is: *The analysis into the infinitely small for the understanding of curved lines*. In honor of the author, the Marquis de l’Hospital, the procedure is called ** l’Hopital’s rule**.

^{4}

Conventionally, the relationship is written

\[\lim_{x\rightarrow x_0} \frac{u(x)}{v(x)} = \lim_{x\rightarrow x_0} \frac{\partial_x u(x)}{\partial_x v(x)}\]

Let’s try this out with our two example functions around \(x=0\):

\[\lim_{x\rightarrow 0} \frac{\sin(x)}{x} = \frac{\lim_{x\rightarrow 0} \cos(x)}{\lim_{x \rightarrow 0} 1} = \frac{1}{1} = 1\]

\[\lim_{x\rightarrow 0} \frac{\sin(x)}{x^3} = \frac{\lim_{x\rightarrow 0} \cos(x)}{\lim_{x \rightarrow 0} 3x^2} = \frac{1}{0} \ \ \text{indeterminate}!\]

## 27.5 Computing with indeterminate forms

In the early days of electronic computers, division by zero would cause a fault in the computer, often signaled by stopping the calculation and printing an error message to some display. This was inconvenient, since programmers did not always forsee division-by-zero situations and avoid them.

As you’ve seen, modern computers have adopted a convention that simplifies programming considerably. Instead of stopping the calculation, the computer just carries on normally, but produces as a result one of two indeterminant forms: `Inf`

and `NaN`

.

`Inf`

is the output for the simple case of dividing zero into a non-zero number, for instance:

```
17/0
## [1] Inf
```

`NaN`

, standing for “not a number,” is the output for more challenging cases: dividing zero into zero, or multiplying `Inf`

by zero, or dividing `Inf`

by `Inf`

.

```
0/0
## [1] NaN
0 * Inf
## [1] NaN
Inf / Inf
## [1] NaN
```

The brilliance of the idea is that any calculation that involves `NaN`

will return a value of `NaN`

. This might seem to get us nowhere. But most programs are built out of other programs, usually written by other people interested in other applications. You can use those programs (mostly) without worrying about the implications of a divide by zero. If it is important to respond in some particularly way, you can always check the result for being `NaN`

in your own programs. (Much the same is true for `Inf`

, although dividing a non-`Inf`

number by `Inf`

will return 0.)

Plotting software will often treat `NaN`

values as “don’t plot this.” that is why it is possible to make a sensible plot of \(\sin(x)/x\) even when the plotting domain includes zero.

## 27.6 Drill

**Part 1** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=-2\), what will be the sign of the coefficient on the first-order term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 2** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=1\), what will be the sign of the coefficient on the first-order term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 3** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=1\), what will be the sign of the coefficient on the second-order term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 4** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=-4\), what will be the sign of the coefficient on the first-order term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 5** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=4\), what will be the sign of the coefficient on the second-order term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 6** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=4\), what will be the sign of the coefficient on the constant (zeroth-order) term. Choose the best answer.

- positive
- zero
- negative
- no such coefficient exists in a Taylor polynomial

**Part 7** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=3\), what will be the sign of the coefficient on the second-order term. Choose the best answer.

- positive
- zero
- negative
- no such term exists in a Taylor polynomial

**Part 8** Here is a function \(f(x)\):

In the Taylor polynomial approximation to \(f(x)\) centered at \(x=0\), what will be the sign of the coefficient on the reciprocal term. Choose the best answer.

- positive
- zero
- negative
- no such term exists in a Taylor polynomial

**Part 9** Here is a function \(g(x)\):

Two Taylor polynomials, centered on the same \(x\) are shown. One is fifth-order, the other is third-order. Which is which?

- The third-order polynomial is brown.
- The third-order polynomial is magenta.

**Part 10** Here is a function \(g(x)\):

with a Taylor polynomial shown in magenta. What is the order of the polynomial?

zero one two three four

## 27.7 Exercises

#### Exercise 27.01

The Taylor polynomial for \(e^x\) has an especially lovely formula: \[p(x) = 1 + \frac{x}{1!} + \frac{x^2}{2!} + \frac{x^3}{3!} + \frac{x^4}{4!} + \cdots\]

**Part A** In the above formula, the center \(x_0\) does not appear. Why not?

- Having a center is not a requirement for a Taylor polynomial.
- There is a center, \(x_0 = 1\), but terms like \(x_0 x^2\) were simplified to \(x^2\).
- There is a center, \(x_0 = 0\), but the terms like \((x-x_0)^2\) were simplified to \(x^2\).

#### Exercise 27.02

Here is a Taylor polynomial:

\[p(x) = e + \frac{e}{1!} (x-1) + \frac{e}{2!} (x-1)^2 + \cdots\]

**Part A** Where is the center \(x_0\) of this polynomial?

\(x_0 = -2\) \(x_0 = -1\) \(x_0 = 0\) \(x_0=1\) \(x_0 = 2\)

**Part B** Your roommate suggests that \(p(x)\) is a Taylor expansion of \(e^x\) around \(x=1\). Is he right?

- No, a polynomial does not have functions like \(e\).
- Yes. The center is \(x_0 = 1\).
- Not really. The formula suggests that the center is \(x_0=1\) but the coefficients are wrong.

#### Exercise 27.03

Consider the function \(f(x) \equiv (x - 3)^2\).

**Part A** Using ordinary algebra, \(f(x)\) can be expanded as \((x^2 - 6 x -9)\). Consider \[p(x) = -9 - 6 x + x^2 .\] Is \(p()\) a Taylor polynomial expansion of \(f(x)\)?

- Yes, with a center at \(x_0 = 0\)
- Yes, with a center at \(x_0 = 3\)
- No, because there are no factorials involved

#### Exercise 27.04

Here’s the Taylor polynomial expansion of \(\sin(x)\) about a center \(x_0\):

\[p(x) = 1/2 - \frac{\sqrt{3}/{2}}{2!} (x - x_0)^2 + \frac{1/2}{4!} (x - x_0)^4 + \cdots\]

**Part A** Which of these is the numerical value of \(x_0\)? (Hint: Remember that the coefficients involve the function and its derivatives evaluated at \(x_0\) as described in Section @ref(high-order-approx).)

\(\pi/6\) \(\pi/3\) \(\pi/2\) \(\pi\)

Problem with Differentiation Exercises/camel-sing-futon

#### Exercise 27.06

Consider the function \[f(x)\equiv \sin(x-\frac{\pi}{6})\] and its 3rd order Taylor polynomial approximation, \(T(x)\). We will implement both in R/mosaic:

```
<- makeFun(sin(x-pi/6)~x)
f <- makeFun(-.5+x*sqrt(3)/2+.25*x^2-x^3/(4*sqrt(3))~x) T
```

In this exercise, we want to figure out the extent of the interval in which \(T(x)\) is within \(0.05\) of \(f(x)\). We can do this by plotting \(T(x)\) and also the two functions \(f(x)+0.05\) and \(f(x)-0.05\).

```
slice_plot(f(x)+.05~x,bounds(x=-1:1))%>%
slice_plot(f(x)-.05~x)%>%
slice_plot(T(x)~x,color="red")
```

**Part A** Find the full interval of the domain of \(x\) for which \(f(x) - 0.05 < T(x) < f(x) + 0.05\). (Hint: you may need to modify the plotting commands.)

\(x\in(-1.1,1,1)\) \(x\in(-1.1,1.5)\) \(x\in(-1.5,1.1)\) \(x\in(-1.5,1.5)\)

#### Exercise 27.07

With very high-order derivatives, it can be awkward to use a notation with repeated subscripts, like \(\partial_{xxxxx} f(x)\). Another notation often encountered is \(f^{(5)}\), where the integer in the superscript tells the order of the derivative. We will use that notation in this problem.

For a function \(f(x)\) and its derivatives \(f^{(1)}(x)\), \(f^{(2)}(x)\), … the Taylor polynomial \(p(x)\) centered on \(x_0\) is

\[p(x) \equiv f(x_0) + \frac{f^{(1)}(x_0)}{1!} (x - x_0)^1 + \frac{f^{(2)}(x_0)}{2!} (x - x_0)^2 + \cdots\]

A Taylor polynomial, like all polynomials, is a linear combination of basic functions.

**Part A** Which of these are the basic functions being linearly combined in a Taylor polynomial?

- \(f(x), f^{(1)}(x), f^{(2)}(x), \ldots\)
- \(f(x_0), f^{(1)}(x_0), f^{(2)}(x_0), \ldots\)
- \(f(x_0), \frac{f^{(1)}(x_0)}{1!}, \frac{f^{(2)}(x_0)}{2!}, \ldots\)
- \((x-x_0), (x - x_0)^2, \ldots\)

#### Exercise 27.08

It is worth remembering the first couple of terms in the Taylor polynomial approximation of the sine and cosine functions.

\[\begin{eqnarray} \sin(x) &\approx & x - \frac{1}{2\cdot 3} x^3\\ \cos(x) &\approx & 1 - \frac{1}{2} x^2$$ \end{eqnarray}\]

**Part A** What is the order of the approximation to \(\sin(x)\) given above?

First Second Third Fourth

**Part B** What is the order of the approximation to \(\cos(x)\) given above?

First Second Third Fourth

Here are several different quantities to calculate involving limits. You will carry out the calculation by substituting the Taylor Polynomials given above, cancelling out \(x\) terms as permitted, and examining if the resulting formula makes sense. If it does, calculate the numerical value of the limit.

Example:

\[\lim_{x \rightarrow 0} \frac{1 - \sin(x)}{x^2} \ \ \text{becomes}\ \ \lim_{x \rightarrow 0} \frac{1 - x + x^3/6}{x} = \lim_{x \rightarrow 0} \left(\strut\frac{1}{x} - 1 + \frac{x^2}{6}\right)\]

The \(-1\) and \(x^2/6\) terms are perfectly sensible in the limit as \(x \rightarrow 0\), but the \(1/x\) term implies division by zero. There is no numerical value for the limit.

\[\text{a.}\ \ \lim_{x\rightarrow 0}\frac{1 - \cos(x)}{x}\]

\[\text{b.}\ \ \lim_{x\rightarrow 0}\frac{1 - \cos(x)}{x^2}\]

\[\text{c.}\ \ \lim_{x\rightarrow 0}\frac{\sin(x)}{x}\]

#### Exercise 27.09

Each of the three functions graphed below is a simple power-law function that can be written \(\left[x-x_0\right]^n\). The three functions have different values for \(x_0\) and for \(n\).

**Part A** For the \(\color{blue}{ ext{blue}}\) function, what is \(x_0\)?

0 1 2 3 4 5

**Part B** For the \(\color{blue}{ ext{blue}}\) function, what is the order of the polynomial?

0 1 2 3 4 5

**Part C** For the \(\color{orange}{ ext{orange}}\) function, what is \(x_0\)?

0 1 2 3 4 5

**Part D** For the \(\color{orange}{ ext{orange}}\) function, what is the order of the polynomial?

0 1 2 3 4 5

**Part E** For the \(\color{magenta}{ ext{magenta}}\) function, what is \(x_0\)?

-2 -1 0 1 2 3

**Part F** For the \(\color{magenta}{ ext{magenta}}\) function, what is the order of the polynomial?

0 1 2 3 4 5

#### Exercise 27.10

At \(x=0\), the value of \(x \ln(x)\) is indeterminate, having the form \(0 \cdot \infty\).

Using a sandbox, plot out \(x \ln(x)\) over the domain \(0 < x < \frac{1}{2}\).

**Part A** From the graph, determine \(\lim_{x\rightarrow 0} x \ln(x()\). Choose the correct answer.

-0.2 0 0.1 0.5 not well behaved

Use l’Hopital’s rule to confirm that \(\lim_{x\rightarrow 0} x \ln(x()\) exists. Here, the rule has the form:

$$_{x} x (x) = [_{x} _x x]

[_{x} _x (x)]$$

**Part B** Does the application of l’Hopital’s rule confirm the result from graphing the function?

Yes No

#### Exercise 27.11

We would like to write a Taylor-series polynomial for \(\dnorm(x)\). To do this, we will need the numbers

\[\dnorm(x_0),\ \ \partial_x \dnorm(x_0),\ \ \partial_{xx} \dnorm(x_0),\ \ \partial_{xxx} \dnorm(x_0), ...\]

Here are the functions for the derivatives

Order | formula for derivative |
---|---|

0th | \(\dnorm(x)\) |

1st | \(\left[{\large\strut}-x\right]\dnorm(x)\) |

2nd | \(\left[{\large\strut}x^2 - 1\right]\dnorm(x)\) |

3rd | \(\left[{\large\strut}3x - x^3\right]\dnorm(x)\) |

4th | \(\left[{\large\strut}3\,x^2-x^4 \right]\dnorm(x)\) |

5th | \(\left[{\large\strut}3-6\,x^3 + x^5 \right]\dnorm(x)\) |

6th | \(\left[{\large\strut}-15\,x^2 + 10\,x^4-x^6 \right]\dnorm(x)\) |

7th | \(\left[{\large\strut}-15 + 45\,x^3-15\,x^5 + x^7 \right]\dnorm(x)\) |

8th | \(\left[{\large\strut}105\,x^2-105\,x^4 + 21\,x^6-x^8 \right]\dnorm(x)\) |

- Write a Taylor polynomial function for \(\dnorm(x)\) around \(x_0=0\)). To do this, you will have to evaluate each of the 9 formulas above at \(x=0\), producing a number. For example, the first five formulas will give 0.3989, 0, -0.3989, 0, 1.1968. This would expand into the following Taylor function:

```
= makeFun(0.3989 +
T_at_0 0*(x-x0) +
- 0.3989*(x-x0)^2/factorial(2) +
0*(x-x0)^3/factorial(3) +
1.1968*(x-x0)^4/factorial(4) ~ x,
x0=0)
```

You will need to add in the 6th, 7th, and 8th terms.

Plot

`T_at_0(x) ~ x`

over the domain \(0 \leq x \leq 2\), and pipe the result to a slice-plot of`dnorm(x) ~ x`

so that you can compare the two plots.Define another function

`T_at_1()`

which will be much like`T_at_zero()`

but with`x0=1`

and the coefficients changed to be the formulas evaluated at \(x=1\).Add a layer showing

`T_at_1()`

to your previous plot.

Say over what domain `T_at_0()`

is a good approximation to `dnorm()`

and over what domain `T_at_1()`

is a good approximation to `dnorm()`

. Do the two domains overlap?

- Write a piecewise function of this form:

```
<- makeFun(ifelse(abs(x) < 0.5,
T T_at_0(abs(x)),
T_at_1(abs(x))) ~ x)
```

Plot this out on top of `dnorm()`

to show whether `T()`

is a good approximation to `dnorm()`

.

You could continue on to define `T_at_2()`

and incorporate that into the piecewise `T()`

, and so on, to construct an approximation to `dnorm()`

that is accurate over a larger domain.

The fundamental theorem says that an order-n polynomial has n roots (including multiplicities).↩︎

The mathematical background needed for those better tools won’t be available to us until Block 5, when we explore linear algebra.↩︎

Unfortunately for these human calculators, pencils weren’t invented until 1795. Prior to the introduction of this advanced, graphite-based computing technology, mathematicians had to use quill and ink.↩︎

In many French words, the sequence “os” has been replaced by a single, accented letter, \(\hat{o}\).↩︎