My previous post Deriving the Maclaurin Series is a prerequisite for this post.

We’ve just learned how to approximate a function using the Maclaurin Series. It’s a pretty sweet hammer, so let’s start hitting some nails. How about ?

If you care about the region around 0, you’re golden. This is a great approximation. But what if you care about ? It’s… not great. One option is to add more terms. We used the first 5 terms of the Maclaurin Series. We could use 100. That would definitely work. However, if you know beforehand that you care about a particular region that is not the region around 0, there’s a better way.

When might you know beforehand which x-region you care about in real life? Actually, quite often. Let’s say the function you’re approximating is the percentage humidity in the air as a function of temperature. Are you particularly interested in a temperature of 0? Probably not. You’re probably most interested in temperatures that are close to your current temperature. If you come up with real-life examples, there’s often a natural “default” x value and you probably care most about the x-region around that default value.

So what’s the better way? It’s called the Taylor Series, and it’s just two small steps away from the Maclaurin Series.

Step 1

We want to polynomial that best approximates a function around the point . However, as a first step, we’re first going to solve for a polynomial that is almost the polynomial we want. It’s going to be exactly the Maclaurin Series, except in place of , , etc., we’re going to use , , etc. I.e.

Because we’re now using the function value (and derivatives) around , the resulting polynomial will look very similar to around . The only problem is that it’s still centered around the origin.

A picture is going to be much easier to understand:

Notice how the red line, has the same value and shape around as does around ?

Step 2

This is the easy step, we need to shift our function to the right by . You may just remember this back from algebra, but if not, we want .

And, not that it will surprise you, but here’s the final picture (re-centered around ):

Notice that is a much worse approximation of around , similar to how the Maclaurin Series was a terrible approximation around . There’s no magic going on here. We traded off accuracy around for accuracy around . If you need more accuracy everywhere, you need more terms.