Optimizing n for ε in a Square Root Expression

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In summary, the conversation is about finding a better way to find n such that for every n > n0 |sqrt(n^2+3)-sqrt(n^2-1)| < ε. The speaker has tried reformulating the expression and squaring both sides but only got the correct result for ε < 1. They are seeking advice on how to derive the correct answer, which is 2/ε. The conversation also discusses the possibility of ε being larger than 1 and how that may affect the solution.
  • #1
peripatein
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Hello,

Is there a better way to find n such that for every n>n0 |sqrt(n^2+3)-sqrt(n^2-1)|<ε?
I have tried reformulating the expression on the left side so that only one of the square roots remains, to not much avail. The only way I could solve it was by squaring both sides. I did get the correct expression for ε for every ε<1, but it does not work for larger ε's, hence, alas, it is wrong.
Any word of advice, please?
 
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  • #2
peripatein said:
Hello,

Is there a better way to find n such that for every n>n0 |sqrt(n^2+3)-sqrt(n^2-1)|<ε?
I have tried reformulating the expression on the left side so that only one of the square roots remains, to not much avail. The only way I could solve it was by squaring both sides. I did get the correct expression for ε for every ε<1, but it does not work for larger ε's, hence, alas, it is wrong.
Any word of advice, please?

I don't see why you would need to worry about ##\epsilon## ≥ 1. In fact, you should probably be able to assume that ##\epsilon## < 1 - the whole goal of this exercise is to show that you can choose n large enough so that the difference of the two square roots is arbitrarily small.
 
  • #3
Thank you very much for replying.
n0 >= sqrt(epsilon^4-4epsilon^2+16)*1/(2epsilon)
Which not only seems very messy but also yields wrong results for epsilon greater than 1. The question states that epsilon is positive, so I presume my expression must hold for any epsilon, even if greater than 1.
I know that the right answer should probably be 1/(2epsilon), but how may I derive it?
 
  • #4
My bad, I meant that the right answer should probably be 2/epsilon!
 
  • #5
peripatein said:
Thank you very much for replying.
n0 >= sqrt(epsilon^4-4epsilon^2+16)*1/(2epsilon)
If ##\epsilon## is suitably small, then ##\epsilon^4## and ##\epsilon^2## can be neglected. This makes the expression on the right side approximately equal to √16/##2\epsilon##, which gives you the (corrected) result below.
peripatein said:
Which not only seems very messy but also yields wrong results for epsilon greater than 1. The question states that epsilon is positive, so I presume my expression must hold for any epsilon, even if greater than 1.

I know that the right answer should probably be (edit: corrected from later post) [STRIKE]1/(2epsilon)[/STRIKE] 2/##\epsilon##, but how may I derive it?
 
  • #6
But what if epsilon were equal to 40? In that case epsilon^4 and epsilon^2 may not be neglected and 2/epsilon still yields the right value for n0, whereas sqrt(epsilon^4-4epsilon^2+16)*1/(2epsilon) doesn't!
 
  • #7
peripatein said:
But what if epsilon were equal to 40?
Why would anyone care? ##\epsilon## is almost always used to represent small (close to 0) numbers.
peripatein said:
In that case epsilon^4 and epsilon^2 may not be neglected and 2/epsilon still yields the right value for n0, whereas sqrt(epsilon^4-4epsilon^2+16)*1/(2epsilon) doesn't!
 
  • #8
peripatein said:
But what if epsilon were equal to 40? In that case epsilon^4 and epsilon^2 may not be neglected and 2/epsilon still yields the right value for n0, whereas sqrt(epsilon^4-4epsilon^2+16)*1/(2epsilon) doesn't!
Since that is a decreasing function of n, and is 2 when n= 1, as soon as [itex]\epsilon[/itex] is larger than 2, you can take [itex]n_0= 1[/itex].
 

FAQ: Optimizing n for ε in a Square Root Expression

What is the purpose of finding an expression for ε?

Finding an expression for ε helps to quantify the error or uncertainty in a mathematical model or measurement. It allows for a better understanding of the accuracy and reliability of the results obtained.

How is ε calculated?

The calculation of ε depends on the specific context in which it is being used. Generally, it involves comparing the measured or predicted value to the true or expected value and taking the difference between the two. This difference is then divided by the true value or a measure of the variability in the data.

Can ε be negative?

Yes, ε can be negative. A negative value for ε indicates that the predicted or measured value is lower than the true or expected value, while a positive value indicates the opposite.

How can ε be reduced?

There are several ways to reduce ε, depending on the specific situation. In general, increasing sample size, improving measurement techniques, and reducing sources of error can all help to decrease ε.

Can ε ever be equal to 0?

In theory, ε can be equal to 0, which would indicate a perfect fit between the predicted or measured value and the true or expected value. However, in practice, it is very rare to have an error or uncertainty of exactly 0 due to the complexity and variability of real-world data and measurements.

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