Approximation with small parameter

AI Thread Summary
To approximate expressions like √(k² - ε²), one can utilize the binomial series expansion by rewriting it as k√(1 - (ε/k)²). This approach allows for the application of the binomial approximation, where the term k is factored out. The approximation can then be derived by expanding (1 - (ε/k)²)^(1/2) using the binomial series. This method provides a clear pathway to derive the approximation k - (ε²/(2k)). Understanding the choice of parameter and point of expansion is crucial for accurate approximations.
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For some small parameter ##\epsilon##, how would one go about making an approximation such as ##\sqrt{k^2-\epsilon^2}\approx k-\frac{\epsilon^2}{2k}##? I was thinking that these types of approximations came from truncating Taylor series expansions, but I can't see how it would be obvious which parameter one would differentiate with respect to, a priori, or what value to expand about. Could someone please explain the general method for making such approximations? Thanks!
 
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avikarto said:
For some small parameter ##\epsilon##, how would one go about making an approximation such as ##\sqrt{k^2-\epsilon^2}\approx k-\frac{\epsilon^2}{2k}##? I was thinking that these types of approximations came from truncating Taylor series expansions
More specifically, a binomial series expansion, writing ##\sqrt{k^2-\epsilon^2}## as ##(k^2 - \epsilon^2)^{1/2}##. That's where I would start.
avikarto said:
, but I can't see how it would be obvious which parameter one would differentiate with respect to, a priori, or what value to expand about. Could someone please explain the general method for making such approximations? Thanks!
 
You can use the binomial approximation by first pulling the k term out.

√(k^2 - e^2) = k⋅√(1 - (e/k)^2) = k⋅(1 - (e/k)^2)^(1/2)

Now look at the binomial approximation.
 
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