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The following content is a test post for the blog's markdown and \KaTeX
rendering capabilities. There's a few errors, and wikilinks aren't supported. This file was copy pasted directly out of my obsidian.md
notebook, so it contains some weird formatting.
The methods in 9.8 Power Series and 9.9 Representation of (Rational) Functions by Power Series allow us to find power series for rational functions, \ln
, and \arctan
. To get power series for other elementary functions, we need a more general method.
We can approximate some non-polynomial functions by constructing a polynomial with the same derivatives as the function. This is called a Taylor Polynomial.
Note
In general, if
c \neq 0
, it's called a Taylor Polynomial. Ifc = 0
, then it's a Maclaurin Polynomial.
Caution
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Warning
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Tip
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Important
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Consider f(x) = e^x
. Let's find the best cubic approximation P_3(x)
for f(x)
at c=0
.
$$
P_3(0) = f(0)
$$
f'(x) = e^x,,P_3'(0)=f'(0)
$$
f''(x) = e^x,,P_3''(0) = f''(0)
$$
f'''(x) = e^x,,P_3'''(0) = f'''(0)
$$
f(0) = f'(0) = f''(0) = f'''(0) = 1
We know that P_3(x)
will be of the form
$$
P_3(x) = a_0 + a_1 x + a_2 x^2 + a_3 x^3
We can differentiate both sides n
times to find the values of a_0
, a_1
, \dots
, a_n
.
$$
P_3(x) = 1 + x + \frac{1}{2} x^2 + \frac{1}{6} x^3
You can confirm that this polynomial has the same first, second, and third derivatives as e^x
.
Generalizing
Taking repeated derivatives like this leads to a common pattern in all Taylor polynomials.
Note
We use the notation
P_n(x)
to denote then^{th}
Taylor polynomial
Taylor polynomials take the form:
$$
P_n(x) = a_0 + a_1 (x-c) + a_2(x-c)^2 + a_3 (x-c)^3 + \dots + a_n(x-c)^n
where a_n
is the coefficient of the n^{th}
term (indexed from 0). It turns out that these coefficients are actually common across Taylor polynomials.
$$
P_n(c) = a_0 = f(c)
$$
P_n'(c) = a_1 = f'(c)
$$
P_n''(c) = 2 a_2 = 2!,a_2
$$
P_n'''(c) = 6a_3 = 3!,a_3 = f'''(c)
$$
P_n^{(n)}(c) = n!,a_n = f^{(n)}(c)
$$
\implies a_n = \frac{f^{(n)}(c)}{n!}
Tying up loose ends
We've seen Taylor and Maclaurin polynomials. We will eventually extend them to 9.10 Taylor and Maclaurin Series. Before this is mathematically valid (at least, valid enough), we need to do some stuff first.
We can use Maclaurin and Taylor polynomials (or series) to estimate the value of functions by hand.
Taylor Remainder
If you have f(x)
and P_n(x)
(centered at c
), then f(x) = P_n(x) + R_n(x)
, where R_n
is
$$
R_n(x) = \frac{f^{(n+1)}(z)}{(n+1)!},(x-c)^{n+1}
$$
z \in [c,,x]
Tip
This is a fancy way of saying that
z
is betweenc
andx
.
$$
\text{Error} = \left|R_n(x)\right|
[!NOTE] >
R_n
would be the "next term" inP_n(x)
except we putz
instead ofc
.
Applying to e^x
How many terms of the Maclaurin Polynomial for f(x) = e^x
do we need to guarantee that our estimate for f(1) = e
is within \displaystyle\frac{1}{1000000}
?
The error is
$$
|R_n(1)| = \frac{|f^{(n+1)}(z)|}{(n+1)!},\cancel{|1-0|^{n+1}}
for some z
between c=0
and $x=1$
So we want an n
such that
$$
\frac{|e^z|}{(n+1)!} \le \frac{1}{1000000}
The worst case scenario is z=1
.
If \displaystyle\frac{e}{(n+1)!} \le \frac{1}{1000000}
, we're all set.
Assume that e \le 3
, which implies that if \displaystyle\frac{3}{(n+1)!} \le \frac{1}{1000000}
, we're all set.
After trying terms, we find that n=9
works.
So P_9(1) = 1 + 1 + \frac{1}{2!} + \frac{1}{3!} + \dots + \frac{1}{9!}
is within \frac{1}{1000000}
of e
.