Understanding Functional Determinants in Mathematics

In summary, the conversation discusses the concept of a functional determinant, particularly in relation to operators with a continuous spectrum. There is a disagreement about its definition and its appearance in evaluating Gaussian integrals. The conversation also references a paper that discusses the concept in more detail, using the zeta trace of a function to calculate its determinant.
  • #1
Klaus_Hoffmann
86
1
I say what is a functional determinant ??

for example [tex] Det( \partial ^{2} + m) [/tex]

is this some kind of Functional determinant?

then i also believe (althouhg it diverges ) that [tex] Det( \partial ^{2} + m)= \lambda_{1}\lambda_{2}\lambda_{3}\lambda_{4}...[/tex]

(the determinant of a Matrix is the product of its eigenvalues)
 
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  • #2
We're talking about an operator with a continuous spectrum, right?
 
  • #3
My gut reaction is in agreement with Gokul's. Your assumption is risky, but interesting. Can you expand upon it? I disagree with your closing remark, but may have taken in out of context.
 
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  • #4
Wouldn't call it 'risky' is just a direct generalization of what a Determinant should be on oo-dimensional spaces.

In fact an operator (integral or differential) is nothing but a oo-dimensional matrix with Trace and determinant, i believe these 'FUnctional determinants' appear when evaluating Gaussian integrals

[tex] \int \mathcal D[x]e^{ixAx^{T}}=B/ Det(A) [/tex]

if 'A' is an operator Det(A) would be some kind of Functional determinant, which is just the multiplication (after regularization) of its Eigenvalues.
 
  • #5
Klaus_Hoffmann said:
Wouldn't call it 'risky' is just a direct generalization of what a Determinant should be on oo-dimensional spaces.

In fact an operator (integral or differential) is nothing but a oo-dimensional matrix with Trace and determinant, i believe these 'FUnctional determinants' appear when evaluating Gaussian integrals

[tex] \int \mathcal D[x]e^{ixAx^{T}}=B/ Det(A) [/tex]

if 'A' is an operator Det(A) would be some kind of Functional determinant, which is just the multiplication (after regularization) of its Eigenvalues.
I noticed your comment that from the point of view of operator theory, an integral or derivation is nothing by an oo-dimensional matrix. I can intuitively come close to that conclusion, but I would love to see a more rigorous mathematical treatment, can you post one or recommend a good introductory book giving such a treatment?

Thanks
 
  • #6
Here's the paper I learned something like this from:

On the concept of determinant for the differential operators of Quantum Physics http://arxiv.org/abs/hep-th/9906229"

It requires using the zeta trace of the function (i.e. [tex]\zeta(s)=\sum_{n}\lambda_{n}^{-s}[/tex]) when you take it's derivative and then set s=0, you get the sum of the logarithms of the eigenvalues of the operator. You exponentiate this, and you get your determinant.
 
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1. What are functional determinants in mathematics?

Functional determinants are mathematical expressions that describe the relationship between inputs and outputs in a mathematical function. They are used to understand how changes in the input values affect the output values of a function.

2. What is the importance of understanding functional determinants?

Understanding functional determinants is crucial in many areas of mathematics, such as differential equations, calculus, and linear algebra. It allows us to analyze and predict the behavior of mathematical functions, and make informed decisions based on this understanding.

3. How can functional determinants be calculated?

Functional determinants can be calculated using various methods, depending on the type of function. In general, they can be calculated by taking the product of the eigenvalues of the matrix representation of the function, or by using special formulas for specific types of functions, such as polynomials or trigonometric functions.

4. Can functional determinants be negative?

Yes, functional determinants can be negative. This typically means that the function has a reflection or rotation component, which can change the sign of the determinant. However, in some cases, a negative functional determinant may also indicate an error in calculation.

5. How are functional determinants used in real-world applications?

Functional determinants are used in many real-world applications, such as physics, engineering, and economics. They are used to model and analyze systems, make predictions, and solve problems in various fields, such as fluid dynamics, electrical circuits, and financial markets.

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