Series: Calculus Notes

Part 3: From A to o(h)

In Parts 1 and 2, we wrote formulas like

x(t+h)=x(t)+v(t)h+A(h2). x(t+h) = x(t) + v(t)h + A(h^2).

That was a good way to track the leftover error, but it was intentionally a little loose. Now we want the precise version of the same idea.

One reason the is useful is that you can calculate with it. For example,

sinx=A(1), \sin x = A(1),

because sine always stays between 1-1 and 11. You can also manipulate it algebraically.

(3.14+A(0.005))(1+A(0.01))=3.14+A(0.005)+A(0.0314)+A(0.00005)=3.14+A(0.03645)=3.14+A(0.04). \begin{aligned} \bigl(3.14 + A(0.005)\bigr)\bigl(1 + A(0.01)\bigr) &= 3.14 + A(0.005) + A(0.0314) + A(0.00005) \\ &= 3.14 + A(0.03645) \\ &= 3.14 + A(0.04). \end{aligned}

Another example is

(1+A(t))2=1+2A(t)+A(t)2=1+2A(t)+A(t2)=1+2A(t)+A(t)if t=A(1)=1+3A(t). \begin{aligned} \bigl(1 + A(t)\bigr)^2 &= 1 + 2A(t) + A(t)^2 \\ &= 1 + 2A(t) + A(t^2) \\ &= 1 + 2A(t) + A(t) \qquad \text{if } t = A(1) \\ &= 1 + 3A(t). \end{aligned}

These examples all do the same job. The AA notation lets us carry an unnamed error term through algebra while still keeping track of its size.

Let's go back to

x(t+h)=x(t)+v(t)h+error. x(t+h) = x(t) + v(t)h + \text{error}.

The precise way to say that the error is much smaller than the main linear term is to write the error as hε(h)h\varepsilon(h), where ε(h)0\varepsilon(h) \to 0 as h0h \to 0. We do not need to know exactly what ε(h)\varepsilon(h) is. We only need to know that it becomes small as hh becomes small. So the exact local statement of speed is

x(t+h)=x(t)+v(t)h+hε(h),ε(h)0. x(t+h) = x(t) + v(t)h + h\varepsilon(h), \qquad \varepsilon(h) \to 0.

We abbreviate hε(h)h\varepsilon(h) using o notation, so the same statement becomes

x(t+h)=x(t)+v(t)h+o(h). x(t+h) = x(t) + v(t)h + o(h).

This is the precise version of the informal A(h2)A(h^2) language from Part 1. The AA notation gives an absolute bound on the error. The o notation gives a relative bound: once you divide the error by hh, what is left goes to 0. In other words, the error is negligible compared with hh itself.

Definition

o notation

f(h)=o(h)f(h) = o(h) means f(h)=hε(h)f(h) = h\varepsilon(h) for some function ε(h)\varepsilon(h) with ε(h)0\varepsilon(h) \to 0 as h0h \to 0.

We do not know exactly what ε(h)\varepsilon(h) is, and we do not need to. We only need to know that it goes to 0.

Definition

Exact local speed statement

x(t+h)=x(t)+v(t)h+o(h)x(t+h) = x(t) + v(t)h + o(h) is the precise way to say that the position looks linear for small hh.

The main term is v(t)hv(t)h, and everything else is smaller than that in the h0h \to 0 limit.

Part 4 will use three simple rules for o notation over and over, so it is worth stating them clearly.

Rule 1

Sum rule

If f(h)=o(h)f(h) = o(h) and g(h)=o(h)g(h) = o(h), then f(h)+g(h)=o(h)f(h) + g(h) = o(h).

Write f(h)=hε(h)f(h) = h\varepsilon(h) and g(h)=hδ(h)g(h) = h\delta(h). Then f(h)+g(h)=h(ε(h)+δ(h))f(h) + g(h) = h(\varepsilon(h) + \delta(h)), and the bracket still goes to 0.

Rule 2

Constant multiple rule

If f(h)=o(h)f(h) = o(h) and cc is fixed, then cf(h)=o(h)cf(h) = o(h).

Multiplying the small error factor by a fixed constant does not stop it from going to 0.

Rule 3

Extra-factor rule

Multiplying by an extra factor of hh gives a term of size o(h)o(h). In particular, h2=o(h)h^2 = o(h), and if f(h)=o(h)f(h) = o(h), then hf(h)=o(h)hf(h) = o(h).

For h2h^2, just write h2=hhh^2 = h \cdot h. For hf(h)hf(h), write f(h)=hε(h)f(h) = h\varepsilon(h), so hf(h)=h(hε(h))hf(h) = h(h\varepsilon(h)).

So the old A(h2)A(h^2) language was a good way to bound errors, while o notation gives the exact local statement needed for proofs. In the next note, we will use that exact form to prove the power rule.

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