Evaluating this contour integral

In summary: L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=
  • #1
Maybe_Memorie
353
0
I was reading a paper which featured the following horrendous integral
##\displaystyle\prod_{n=1}^L\oint_{C_n}\frac{dx_n}{2\pi i}\prod_{k<l}^L(x_k-x_l)\prod_{m=1}^L\frac{Q_w(x_m)}{Q^+_\theta(x_m)Q^-_\theta(x_m)}##

where ##Q^\pm_\theta(x)=\prod_{k=1}^L(u-\theta_k\pm \frac{i}{2})## and ##Q_w(x)=\prod_{r=1}^M(x-w_r)##.

From the paper, ##C_n## denotes the integration contour which encloses ##\theta_n \pm \frac{i}{2}## counterclockwise.

Obviously I can just use the residue theorem and sum over the poles to evaluate it but I'm having trouble doing that since the thing is so complicated. How do I write this as a residue? Can I just focus on the denominators and ignore the other parts?
 
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  • #2


Hello,

Thank you for bringing this integral to my attention. I understand your frustration with its complexity, but fortunately, there are some strategies we can use to simplify it and make it more manageable.

Firstly, let's break down the integral into smaller parts and focus on each one individually. The first part is the product of the integration contours ##C_n##, which enclose the poles ##\theta_n \pm \frac{i}{2}##. This is essentially a product of residues, so we can use the residue theorem to evaluate it. We can also use the fact that the residues of ##\frac{1}{z-a}## at ##z=a\pm \frac{i}{2}## are equal to ##\pm \frac{i}{2}##. This means that the integral reduces to ##\prod_{n=1}^L \frac{i}{2} = \left(\frac{i}{2}\right)^L##.

Next, let's focus on the second part, which is the product of differences of ##x_k## and ##x_l##. This can be rewritten as ##\prod_{k<l}^L (x_k-x_l) = \det(\mathbf{x})##, where ##\mathbf{x}## is a matrix with entries ##x_k## in the diagonal and zeros everywhere else. This is known as the Vandermonde determinant and has a closed form expression of ##\prod_{k<l}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=1}^L (x_k-x_l) = \prod_{k=1}^L \prod_{l=
 

1. What is a contour integral?

A contour integral is a type of integral that is evaluated along a specific path, or contour, in the complex plane. It is used in complex analysis to find values of functions or to solve differential equations.

2. How do you evaluate a contour integral?

To evaluate a contour integral, you must first parameterize the contour and then use the appropriate integration techniques, such as the fundamental theorem of calculus or Cauchy's integral formula. It is important to choose the correct contour and use the appropriate integration techniques for the specific problem at hand.

3. What are some common types of contours used in contour integrals?

Some common types of contours used in contour integrals include circles, rectangles, and curves that follow the shape of a function. These contours are typically chosen based on their simplicity and the ease of evaluating the integral along them.

4. What is the relationship between a contour integral and a regular integral?

A contour integral is a generalization of a regular integral in the complex plane. While a regular integral is evaluated along a line in the real plane, a contour integral is evaluated along a path in the complex plane. However, both types of integrals follow similar rules and techniques for evaluation.

5. How are contour integrals used in scientific research?

Contour integrals are used in many areas of scientific research, particularly in complex analysis, physics, and engineering. They are used to solve differential equations, find values of functions, and model physical phenomena. In addition, contour integrals are used to evaluate complex probabilities in statistics and to analyze data in signal processing and image analysis.

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