Strange definition of regularization of Operators

In summary, while surfing the web and arxiv, the speaker came across the formula lnA= \frac{d^{n}}{ds^{n}} \frac{s^{n-1}}{n! A^{s}}, and they are curious about its origin and validity in terms of non-renormalizable or renormalizable theories. They provide a link to a book by E. Elizalde where the formula is previewed on pages 75 and 76, and mention the importance of the term -\lim_{s\to 0} out in front and taking a derivative with respect to A.
  • #1
mhill
189
1
surfing the web and arxiv i found the strange formula

[tex] lnA= \frac{d^{n}}{ds^{n}} \frac{s^{n-1}}{n! A^{s}} [/tex]

my question is .. where does this formula come from ??

here 'n' is supposed to be a finite parameter we must define to avoid the divergences, is it valid for non-renormalizable or renormalizable theories ??
 
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  • #2
mhill said:
surfing the web and arxiv i found the strange formula

Whenever you want to do something like this, you must make a full citation of the source to put this into a proper context. Or else this may not make any sense.

Zz.
 
  • #4
The [tex]-\lim_{s\to 0}[/tex] out in front is important.
 
  • #5
Formally taking a derivative wrt to [tex]A[/tex] on both sides should prove the identity to you.
 

FAQ: Strange definition of regularization of Operators

What is the purpose of regularization in operators?

Regularization is a mathematical technique used to handle ill-posed problems, where the solution may be unstable or non-existent due to noise or other factors. In the context of operators, regularization helps to stabilize and improve the accuracy of the solution.

How does regularization of operators differ from regularization in other fields?

The concept of regularization is similar across different fields, but the specific techniques and methods used may vary. In the context of operators, regularization involves modifying the operator itself, rather than the data, to improve the solution.

What are some common methods used for regularization of operators?

Some common methods for regularization of operators include Tikhonov regularization, which involves adding a penalty term to the operator to control the smoothness of the solution, and truncated singular value decomposition (TSVD), which involves approximating the operator using a truncated version of its singular value decomposition.

Can regularization of operators be applied to any type of operator?

Yes, regularization can be applied to a wide range of operators, including linear and non-linear operators. However, the effectiveness of different regularization methods may vary depending on the specific properties of the operator.

What are the benefits of using regularization in operators?

Regularization can help to improve the accuracy and stability of solutions for ill-posed problems, as well as reduce the effects of noise and other sources of error. It can also provide a way to control the smoothness or complexity of the solution, which can be useful in applications where simplicity or interpretability is important.

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