Taylor Series

Learning Objectives

• Approximate a function using a Taylor series
• Approximate function derivatives using a Taylor series
• Quantify the error in a Taylor series approximation

Degree $n$ Polynomial

A polynomial in a variable $x$ can always be written (or rewritten) in the form

where $a_i$ ($0\le i\le n$) are constants.

Using the summation notation, we can express the polynomial concisely by

If $a_n\ne 0$, the polynomial is called an $n$-th degree polynomial.

Degree $n$ Polynomial as a Linear Combination of Monomials

A monomial in a variable $x$ is a power of $x$ where the exponent is a nonnegative integer (i.e. $x^n$ where $n$ is a nonnegative integer). You might see another definition of monomial which allows a nonzero constant as a coefficient in the monomial (i.e. $a{x}^n$ where $a$ is nonzero and $n$ is a nonnegative integer). Then an $n$-th degree polynomial

can be seen as a linear combination of monomials $x^i|0\le i\le n$.

Taylor Series Expansion, Infinite

A Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function’s derivatives at a single point. The Taylor series expansion about $x=x_0$ of a function $f(x)$ that is infinitely differentiable at $x_0$ is the power series

Using the summation notation, we can express the Taylor series concisely by

(Recall that $0!=1$)

Taylor Series Expansion, Finite

In practice, however, we often cannot compute the (infinite) Taylor series of the function, or the function is not infinitely differentiable at some points. Therefore, we often have to truncate the Taylor series (use a finite number of terms) to approximate the function.

Taylor Series Approximation of Degree $n$

If we use the first $n+1$ terms of the Taylor series, we will get

which is called a Taylor polynomial of degree $n$.

Error Bound when Truncating a Taylor Series

Suppose that $f(x)$ is an $n+1$ times differentiable function of $x$, and $T_n(x)$ is the Taylor polynomial of degree $n$ for $f(x)$ centered at $x_0$. Then when $h=|x-x_0|\to 0$, we obtain the truncation error bound by $$|f(x)-T_n(x)|\le C\cdot h^{n+1}=O(h^{n+1})$$

We will see the exact expression of $C$ in the next section: Taylor Remainder Theorem.

Taylor Remainder Theorem

Suppose that $f(x)$ is an $n+1$ times differentiable function of $x$. Let $R_n(x)$ denote the difference between $f(x)$ and the Taylor polynomial of degree $n$ for $f(x)$ centered at $x_0$. Then

for some $\xi$ between $x$ and $x_0$. Thus, the constant $C$ mentioned above is

.

Example of a Taylor Series Expansion

Suppose we want to expand $f(x)=\cos x$ about the point $x_0=0$. Following the formula

we need to compute the derivatives of $f(x)=\cos x$ at $x=x_0$.

Then

Example of Using a Truncated Taylor Series to Approximate a Function

Suppose we want to approximate $f(x)=\sin x$ at $x=2$ using a degree-4 Taylor polynomial about (centered at) the point $x_0=0$. Following the formula

we need to compute the first $4$ derivatives of $f(x)=\sin x$ at $x=x_0$.

Then

Using this truncated Taylor series centered at $x_0=0$, we can approximate $f(x)=\sin (x)$ at $x=2$. To do so, we simply plug $x=2$ into the above formula for the degree 4 Taylor polynomial giving

Example of an Error Bound

Suppose we want to approximate $f(x)=\sin x$ using a degree-4 Taylor polynomial expanded about the point $x_0=0$. We want to compute the error bound for this approximation. Following Taylor Remainder Theorem,

for some $\xi$ between $x_0$ and $x$.

If we want to find the upper bound for the absolute error, we are looking for an upper bound for $|f^{(5)}(\xi )|$.

Since $f^{(5)}(x)=\cos x$, we have $|f^{(5)}(\xi )|\le 1$. Then $$|R_4(x)|=|\frac{f^{(5)}(\xi )}{5!}(x-x_0{)}^5|=\frac{|f^{(5)}(\xi )|}{5!}|x{|}^5\le \frac{1}{120}|x{|}^5$$

Finite Difference Approximation

For a differentiable function $f:\mathbb{R}\to \mathbb{R}$, the derivative is defined as

Let’s consider the finite difference approximation to the first derivative as

where $h$ is often called a “perturbation”, i.e., a “small” change to the variable $x$ (small when compared to the magnitude of $x$). By the Taylor’s theorem, we can write

for some $\xi \in [x,x+h]$. Rearranging the above we get

Therefore, the truncation error of the finite difference approximation is bounded by $Mh/2$, where $M$ is a bound on $|f^{″}(\xi )|$ for $\xi$ near $x$.

Reference text: “Scientific Computing: an introductory survey” by Michael Heath

Review Questions

• See this review link

ChangeLog

• 2020-02-10 Peter Sentz sentz2@illinois.edu: Correct some small mistakes and update some notation
• 2019-01-29 John Doherty jjdoher2@illinois.edu: Added Finite Difference section from F18 activity
• 2018-01-14 Erin Carrier ecarrie2@illinois.edu: removes demo links
• 2017-11-02 Erin Carrier ecarrie2@illinois.edu: adds changelog
• 2017-10-27 Erin Carrier ecarrie2@illinois.edu: adds review questions, minor fixes throughout
• 2017-10-27 Yu Meng yumeng5@illinois.edu: first complete draft
• 2017-10-17 Luke Olson lukeo@illinois.edu: outline