Functional derivative of normal function

In summary: Please stay on topic. In summary, the conversation discusses the functional derivative of a function with respect to another function and its relationship to the derivative of the delta distribution. The conversation also touches on the use of different variables and the composition rule for derivatives.
  • #1
ChrisPhys
6
0
I can't convince myself whether the following functional derivative is trivial or not:

##\frac \delta {\delta \psi(x)} \big[ \partial_x \psi(x)\big],##

where ##\partial_x## is a standard derivative with respect to ##x##.

One could argue that

## \partial_x \psi(x) = \int dx' [\partial_{x'} \psi(x')] \delta (x - x') = - \int dx' \psi(x') \partial_{x'} \delta (x - x'),##

assuming there is no boundary term in integration by parts.

In this case, the functional derivative would give

##\frac \delta {\delta \psi(x)}\Big[ - \int dx' \psi(x') \partial_{x'} \delta (x - x') \Big] = - \partial_{x'} \delta (x - x')\Big|_{x'=x} = 0.##

Any thoughts? Is this rigorous?
 
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  • #2
You really should use different variables for the function you are differentiating and the function you are taking the derivative with respect to ...

What makes you think that the last expression evaluates to zero? In the distribution sense it is the derivative of the delta distribution.
 
  • #3
To address your last point:
Orodruin said:
What makes you think that the last expression evaluates to zero? In the distribution sense it is the derivative of the delta distribution.
I just viewed the ##\delta## function as the limit of a Gaussian, whose derivative at zero is zero. Is that an error?
 
  • #4
ChrisPhys said:
To address your last point:

I just viewed the ##\delta## function as the limit of a Gaussian, whose derivative at zero is zero. Is that an error?

This then falls back on your original problem with not using different variables for the function you are differentiating and the function you are differentiating with respect to. Your question should be: "What is ##\delta (\partial_\mu \psi(x))/\delta \psi(y)##?"
The result should be a distribution which picks out the derivative of a function, i.e., the derivative (in the distributional sense) of the delta distribution.
 
  • #5
@Orodruin
Thank you, that makes sense. I appreciate your help.
 
  • #6
Hi
To informally guess the derivative, I use the 'little o' notation and then check the details. However, for the details. I think it is easiest to use composition rule, ie, if f,g are (Fréchet) differentiable with appropriate domains/ranges, then Df∘g(x)=Df(g(x))Dg(x).
Thanks.
 
  • #7
Gracie thomas said:
Hi
To informally guess the derivative, I use the 'little o' notation and then check the details. However, for the details. I think it is easiest to use composition rule, ie, if f,g are (Fréchet) differentiable with appropriate domains/ranges, then Df∘g(x)=Df(g(x))Dg(x).
Thanks.
This thread is about functional derivatives, not derivatives of composite functions.
 

Related to Functional derivative of normal function

1. What is a functional derivative?

A functional derivative is a mathematical concept used in calculus that represents the change in a functional (a mapping between a set of input values and a set of output values) with respect to a small change in its input.

2. How is the functional derivative different from the ordinary derivative?

The functional derivative is used to find the rate of change of a functional, while the ordinary derivative is used to find the rate of change of a single variable function. In other words, the functional derivative takes into account the entire function, rather than just a single input variable.

3. Why is the functional derivative important in science?

The functional derivative is important in science because it allows us to analyze and optimize complex systems that involve multiple variables and functions. It is commonly used in fields such as physics, engineering, and economics.

4. What is the notation for the functional derivative?

The functional derivative is denoted by the symbol ∂/∂f, where f represents the functional and ∂ represents the change in the functional.

5. Are there any applications of the functional derivative in real-world problems?

Yes, the functional derivative has many applications in real-world problems, such as in the optimization of economic systems, the analysis of physical systems, and the study of optimal control in engineering systems.

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