Why Is Fourier Transform Used in Diagonalizing Operators in Mean Field Theory?

In summary, the conversation involves a person seeking help with understanding a passage about mean field theory and calculating the inverse of a specific operator. The passage involves the use of discrete time Fourier transform and finding a function that satisfies a certain equation. The person also mentions similar questions in other texts and eventually solves the problem by understanding the concept of diagonalization and Fourier transforming.
  • #1
tirrel
50
0
Hi...

I hope somebody can help me...

Studying mean field theory in a passage it was necessary to calcolate the inverse of this operator defined on Z^2:

$A(I,K)=-J\sum_e \delta(I,K-e)+1/(\beta)*\delta(I,K)$

where I,K pass all ZxZ and the sum on $e$ is a sum on the for basis vectors e_1,e_2,...,e_4. $\delta(A,B)$ is the usual delta function. $J$ and $\beta$ are constants.

well my book tries to compute $A'(q,p)$ as the discrete time Fourier transform of $A(I,K)$... then finds a certain function $g$ which respects this equation $A'(q,p)*g(q,p)=\delta(q-p)$ and anti-transforms it, pretending thus to find an integral representation of the inverse matrix...

unluckily I don't see why this passage is true... does anybody can help me?
 
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  • #2
I apologize... but I don't know how to write better the formulas...

anyway, pheraps this topic had to be written on the section about field theory... sorry... I'm a newbye!
 
  • #3
Replace the $ signs with tex tags (or itex for inline typesetting).

Example (click to see code): [tex]A(I,K)=-J\sum_e \delta(I,K-e)+1/(\beta)*\delta(I,K)[/tex]

PS: Also, when asking questions about a specific text, it may be useful to cite the text and the page/chapter where the passage is found.
 
Last edited:
  • #4
thank you Gokul...

I found this passage in the pdf file of a teacher of an other university, but unluckily it is not written in english...

Anyway similar questions (more or less) arise here:

-Itzykson Drouffe... "Statysitical field theory", pag. 128. How can I pass from formula 59 to formula 60?

- G.Parisi... "Statystical field theory", chap.3 (mean field)... at the beginning of the chapter (haven't got the book with me right now!) there is written exactly the same operator I wrote in the first message;
 
  • #5
Ok... I guess I solved... thank u for the attention... if anyone is interested, I'll post what I've understood...
 
  • #6
The general story is as follows. You can think of your operator as a matrix where the real space coordinates are it's indices. To find the inverse of that matrix is in general difficult. But the inverse of a diagonal matrix is of course easy (simply 1/every component along the diagonal).

To take a general matrix and render it as a diagonal matrix is the process of diagonalization. But diagonalization is simply "picking a basis" in which the matrix is diagonal, i.e., finding it's eigenvectors and eigenvalues.

Fourier transforming is simply "picking a basis" and writing the object as a linear combination in that basis, and usually operators that are translation invariant in position space become diagonal in Fourier space.

I hope this has clarified things. I don't know the exact problem you are looking at, but this is a fairly general concept.
 

Related to Why Is Fourier Transform Used in Diagonalizing Operators in Mean Field Theory?

1. What is Mean Field Theory?

Mean Field Theory is a mathematical framework used to describe the collective behavior of a large number of interacting particles or agents. It assumes that each particle interacts with the average effect of all other particles, rather than individual interactions. This simplification allows for easier calculations and predictions of the system's behavior.

2. What are the applications of Mean Field Theory?

Mean Field Theory has applications in various fields, including physics, chemistry, biology, neuroscience, and economics. It can be used to understand the behavior of magnets, liquids, gases, and other complex systems.

3. How does Mean Field Theory differ from other statistical methods?

Mean Field Theory is a type of classical statistical mechanics, which assumes that the system is in equilibrium and follows well-defined rules. In contrast, other statistical methods, such as Monte Carlo simulations, do not make such assumptions and can be used to study non-equilibrium systems.

4. What are the limitations of Mean Field Theory?

Mean Field Theory is a simplified model and does not account for fluctuations or correlations between particles. It is most accurate for systems with a large number of particles, and its predictions may deviate from experimental results for small systems or in extreme conditions.

5. How is Mean Field Theory used in machine learning?

Mean Field Theory has been applied in machine learning algorithms, particularly in neural networks. It helps to simplify the computations and improve the efficiency of training and prediction processes. However, its use in this field is still an active area of research and development.

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