How to approximate this relation?

Then you can solve for \Delta\lambda_k, and it looks like the answer is something like \lambda_k \frac{dn(\lambda_k)}{d\lambda_k}.In summary, the conversation discusses a relation involving wavelength (\lambda_k), mode (k), index of reflection (n), and a constant (L). The goal is to find the change in wavelength between two adjacent modes, and the answer is approximately given by \Delta\lambda_k \approx \frac{\lambda_k^2}{2n_gL}, where n_g is calculated using a first-order Taylor expansion of n around \lambda_k.
  • #1
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I got a relation as follow

[tex]\lambda_k = \frac{2 n(\lambda_k) L}{k}[/tex]

where [tex]\lambda_k[/tex] is a wavelength at mode k, k is integer, n is the index of reflection, L is a constant. I am trying to find the change of wavelength between two adjacent mode approximately, the answer will be

[tex]\Delta\lambda_k \approx \frac{\lambda_k^2}{2n_gL}[/tex]

where
[tex]n_g = n(\lambda_k) - \left.\lambda_k\frac{dn}{d\lambda}\right|_{\lambda_k}[/tex]

I have no idea how to achieve this. Please give me some hint. Thanks
 
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  • #2
I don't see exactly how to do it right away, but [itex]n_g[/itex] looks an awful lot like a first-order Taylor expansion of n around [itex]\lambda_k[/itex]. So maybe if you rewrite [tex]n(\lambda_k) = \frac{k \lambda_k}{2 L}[/tex]
and then do some expansion of the left hand side for
[tex]n(\lambda_{k + 1}) = n(\lambda_k) + \Delta\lambda_k \frac{dn(\lambda_k)}{d\lambda_k} + \text{ higher order}[/tex].
 

1. How do I determine the best approximation method to use?

The best approximation method depends on the type of relation you are trying to approximate. If the relation is linear, a regression analysis may be suitable. If the relation is more complex, a polynomial approximation may be necessary.

2. How do I choose the appropriate degree for a polynomial approximation?

The degree of a polynomial approximation should be chosen based on the complexity of the relation. A higher degree polynomial may fit the data better, but may also overfit and lead to inaccurate predictions. It is important to balance accuracy and simplicity when choosing the degree.

3. Can I use multiple approximation methods for the same relation?

Yes, it is possible to use multiple approximation methods for the same relation. This is known as ensemble modeling and can often result in more accurate predictions. However, it is important to carefully analyze and compare the results of each method to ensure they are not conflicting.

4. How do I know if my approximation is accurate?

The accuracy of an approximation can be measured by calculating the error between the predicted values and the actual values. This can be done using metrics such as mean squared error or root mean squared error. The lower the error, the more accurate the approximation is.

5. Is it better to have a higher accuracy or a simpler approximation?

It depends on the purpose of the approximation. If the goal is to make accurate predictions, a higher accuracy is desirable. However, if the goal is to understand the general trend of the data, a simpler approximation may be sufficient. It is important to consider the trade-off between accuracy and simplicity when choosing an approximation method.

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