Maple plot of the curve -x*sin(x) and its derivative.
What we have done with average velocities for the displacement
function given above applies in much more general settings. We can
move in the direction of generality by abstracting what we did to a
more purely mathematical context. In the above context, the velocity
measures the rate of change of the displacement. There are
three concepts to keep in mind. Let f(t) be the
position function of a particle during a time interval
.
In our more abstract mathematical context, we will talk of functions,
and the three concepts above are abstracted as quantities which say
something about the growth of the value of the function as the
independent variable varies over its domain. Now, just let
f(t) be a function defined on some interval
.
net change = f(d) - f(c)
(where h stands in for d - c in the expressions
we were using
before - Verify this). Notice how this fits in with the Maple
expressions we used in Exercise 2.3 above.

Make use of Maple to review the concept of instantaneous
rate of change in Exercise
2-4.
Maple can compute instantaneous growth rates (also known as derivatives). There are two ways, depending on whether you want to compute the derivative of a function such as the f you have defined, or just of an expression, which is what you get when you type f(t). For functions, the statement D(f); (or D(f,t) when the definition of f contains other names you are using as parameters and Maple needs to be told which is the actual variable) will return the definition of a function which gives the instantaneous growth rate (i.e., the derivative) of f; for an expression, the statement diff(f(t),t) (here the ``,t'' is obligatory) does the same thing.

Be sure you understand the distinction between expressions
and functions in Maple, and the (subtle!) difference between
D and diff. Exercises
2-5 and
2-6 will help
clear up your understanding.
The preceding problem should indicate that computing average growth rates over small enough intervals is, for all practical purposes, just as good as computing derivatives. Furthermore, the definition we have given of derivative leaves a lot to be desired (it is essentially the same as the one Newton gave, and the loose ends he left over took more than 200 years to tie up), and seems much more complicated than computing average rates of change. So why work with derivatives at all?
As it turns out, there are pragmatic, theoretical and philosophical reasons for working with derivatives. To begin with, we have seen that average growth rates over small intervals are effectively as good as derivatives when the intervals are ``small enough''. But how small is that? It turns out not just to be a matter of taste, but to depend in an essential way on the function whose growth rate we are trying to measure - it can even depend over which values of the variable we are trying to measure it. For example, consider the function f(t) = sin(t² ).

Do exercise
2-7.
The second reason the derivative works better than average growth rates over small intervals is that, after all is said and done, derivatives are easier to compute. As you will learn (or have learned) in your regular calculus course, there are algorithmic rules for computing derivatives of practically any function that can be written down. Maple knows all these rules (that's how diff and D work), but they are still good to have memorized, because they provide some insight into the behavior of functions that arise in applications.
Another reason derivatives are better is that calculating average growth rates over extremely small intervals is fraught with serious numerical pitfalls. For instance, consider the problem of finding the growth rate of the function f(x) = cos(x) for x = 0.1 (radians). Maple reports that the values of cos(0.1) and cos(0.10001) differ only in the sixth decimal place (they are 0.995004 and 0.995003 respectively). So to get a good growth rate estimate, we have to be able to compute cosines to many significant figures. It's just not worth the effort when we know that the derivative of cos(x) is -sin(x) (verify this using Maple) and computing -sin(0.1) to a few significant figures is a breeze.
Finally (for the moment), derivatives provide a powerful theoretical tool for studying quantities that change. The following section is meant to provide some idea of this.

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