Contour Choosing Problem

  • Thread starter Mitra
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In summary, for the given integral, a keyhole contour is recommended for analytic evaluation using Cauchy's Residue Theorem, as it avoids the singularity at 0 and allows for the application of Cauchy's theorem.
  • #1
Mitra
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I would like to find this integral analytically by using “Cauchy Residue Theorem”, but my problem is what contour is suitable?

[tex]\int {\frac {\exp(-M\omega) \exp(iN\omega)} {\omega^5(\frac{\omega^4}{f^4} + \frac{(\omega^2)(4\zeta^2-2)}{f^2}+ 1)}d\omega[/tex]

From 0 to [tex]\infty[/tex]
Where, M, N,f and [tex]\zeta[/tex] are real and positive.

Since the function has a singularity at 0 on the Semi circle Contour, what do you suggest as a contour? A Keyhole Contour? or a semicircle contour with a small detour around 0?
 
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  • #2
For this integral, a keyhole contour would be the most suitable. The keyhole contour is a closed loop path in the complex plane that follows the real axis from 0 to some positive value M, encircles the origin in a small circle of radius r, and then returns to the real axis at M. This contour avoids the singularity at the origin and allows us to apply Cauchy’s theorem to evaluate the integral.
 
  • #3


The choice of contour is crucial in evaluating integrals using the Cauchy Residue Theorem. In this case, we have a rational function with a singularity at 0, which means we cannot use a simple closed contour such as a semicircle or a keyhole. We need to modify the contour to avoid the singularity at 0 while still enclosing all the poles of the function.

One possible approach is to use a semicircle contour with a small detour around 0, as you suggested. This detour can be chosen to be a small semicircle centered at 0, with radius r. This way, we can avoid the singularity at 0 while still enclosing all the poles of the function.

Another approach could be to use a keyhole contour, where the detour around 0 is a small circle centered at 0, with radius r. This contour would also avoid the singularity at 0 and enclose all the poles, but it may require more calculations to determine the residues at the poles.

Ultimately, the choice of contour will depend on the specific problem and the poles of the function. It may be helpful to graph the function and its poles to get a better understanding of the contour that would be suitable. Whichever contour you choose, make sure to carefully calculate the residues at the poles to accurately evaluate the integral using the Cauchy Residue Theorem.
 

Related to Contour Choosing Problem

What is the Contour Choosing Problem?

The Contour Choosing Problem is a mathematical problem that involves finding the optimal contour line for a given dataset. This contour line is the line that best represents the underlying pattern or trend in the data.

Why is the Contour Choosing Problem important?

The Contour Choosing Problem has many real-world applications, such as in weather forecasting, geology, and image processing. It allows us to accurately represent and interpret complex data, making it a crucial tool in various scientific fields.

What are some methods for solving the Contour Choosing Problem?

There are several algorithms and techniques for solving the Contour Choosing Problem, such as gradient descent, k-means clustering, and principal component analysis. Each method has its advantages and limitations, and the choice of method depends on the specific dataset and problem at hand.

What challenges are associated with the Contour Choosing Problem?

One of the main challenges of the Contour Choosing Problem is determining the appropriate number of contour lines to use. Too few contour lines can oversimplify the data, while too many can make it difficult to interpret. Additionally, the choice of contour line placement can be subjective and can vary depending on the individual performing the analysis.

How does the Contour Choosing Problem relate to other statistical methods?

The Contour Choosing Problem is closely related to other statistical methods, such as regression analysis and clustering. It shares some similarities with these methods, but it also has its unique characteristics and applications. It can also be used in combination with other methods to gain a more comprehensive understanding of the data.

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