When these functions are evaluated at , all terms with a positive -power become zero:
This last formula is the basis for Taylor and Maclaurin series:
Power series: Derivative-Coefficient Identity
This identity holds for a power series function which has a nonzero radius of convergence.
We can apply the identity in both directions:
Know ? Calculate for any .
Know ? Calculate for any .
Many functions can be ‘expressed’ or ‘represented’ near (i.e. for small enough ) as convergent power series. (This is true for almost all the functions encountered in pre-calculus and calculus.)
Such a power series representation is called a Taylor series.
When , the Taylor series is also called the Maclaurin series.
One power series representation we have already studied:
Whenever a function has a power series (Taylor or Maclaurin), the Derivative-Coefficient Identity may be applied to calculate the coefficients of that series.
Conversely, sometimes a series can be interpreted as an evaluated power series coming from for some . If the closed form function format can be obtained for this power series, the total sum of the original series may be discovered by putting in the argument of the function.
04 Illustration
Example - Maclaurin series of
Maclaurin series of e to the x
What is the Maclaurin series of ?
Solution
Because , we find that for all .
So for all . Therefore for all by the Derivative-Coefficient identity.
Linear approximation is the technique of approximating a specific value of a function, say , at a point that is close to another point where we know the exact value . We write for , and , and . Then we write and use the fact that:
Computing a linear approximation
For example, to approximate the value of , set , set and , and set so .
Then compute:
So .
Finally:
Now recall the linearization of a function, which is itself another function:
Given a function , the linearization at the basepoint is:
The graph of this linearization is the tangent line to the curve at the point .
The linearization may be used as a replacement for for values of near . The closer is to , the more accurate the approximation is for .
Computing a linearization
We set , and we let .
We compute , and so .
Plug everything in to find :
Now approximate :
07 Theory
Taylor polynomials
The Taylor polynomials of a function are the partial sums of the Taylor series of :
These polynomials are generalizations of linearization.
Specifically, , and .
The Taylor series is a better approximation of than for any .
Facts about Taylor series
The series has the same derivatives as at the point . This fact can be verified by visual inspection of the series: apply the power rule and chain rule, then plug in and all factors left with will become zero.
The difference vanishes to order at :
The factor drives the whole function to zero with order as .
If we only considered orders up to , we might say that and are the same near .
08 Illustration
Taylor polynomial approximations
Taylor polynomial approximations
Let and let be the Taylor polynomials expanded around .
By considering the alternating series error bound, find the first for which must have error less than .
Solution
Write the Maclaurin series of because we are expanding around .
Alternating sign, odd function:
Notice this series is alternating, so AST error bound formula applies.
AST error bound formula is:
Here the series is and is the error.
Notice that is part of the terms in this formula.
Implement error bound to set up equation for .
Find such that , and therefore by the AST error bound formula:
Plug in .
From the series of we obtain for :
We seek the first time it happens that .
Solve for the first time .
Equations to solve:
Method: list the values:
The first time is below happens when .
Interpret result and state the answer.
When , the term at is less than .
Therefore the sum of prior terms is accurate to an error of less than .
The sum of prior terms equals .
Since because there is no term, the same sum is .
The final answer is .
It would be wrong to infer at the beginning that the answer is , or to solve to get .