Partial Derivative of Convolution

In summary, the conversation discusses the calculation of the partial derivative of a convolution, where one of the functions depends on the variable that the derivative is taken with respect to. The identity for taking the derivative of a convolution with both functions depending only on t is mentioned, and the differentiation of the convolution with respect to r is explored. The result is shown to be (x * partial derivative of y with respect to r)(t,r). Resources for further help are requested.
  • #1
jstop
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TL;DR Summary
Calculating the partial derivative of a convolution
Hello, I am trying to calculate the partial derivative of a convolution. This is the expression:
##\frac{\partial}{\partial r}(x(t) * y(t, r))##​

Only y in the convolution depends on r. I know this identity below for taking the derivative of a convolution with both of the functions only depending on t:
##\frac{d}{dt}(x(t)*y(t) = (\frac{dx(t)}{dt}*y(t)) = (x(t)*\frac{dy(t)}{dt})##​

I'm not sure how this changes when taking a partial derivative with only one of the functions depending on the variable that the partial derivative is being taken with respect to. Any help/resources would be appreciated! (This is my first post here, please let me know if I need to improve my post formatting/structure).
 
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  • #2
Your convolution is [itex](x * y)(t,r)=\int_{-\infty}^{\infty} x(\tau)y(t - \tau, r)\,d\tau[/itex], is it not? What happens if you differentiate that with respect to [itex]r[/itex]?
 
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  • #3
Ah thanks, I think I see what you mean:
##\frac{\partial}{\partial r} (x * y)(t,r) = \int_{-\infty}^{\infty} x(\tau)\frac{\partial}{\partial r}y(t - \tau, r)d\tau = (x*\frac{\partial y}{\partial r})(t,r)##​
 
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What is a partial derivative of convolution?

A partial derivative of convolution is a mathematical operation that calculates the rate of change of one variable in a convolution function while keeping all other variables constant. It is used to find the sensitivity of the output of a convolution function to changes in its input variables.

Why is the partial derivative of convolution important?

The partial derivative of convolution is important because it allows us to analyze and optimize convolution functions in various applications, such as signal processing, image processing, and machine learning. By understanding the rate of change of the output with respect to the input variables, we can make informed decisions on how to improve the performance of these functions.

How is the partial derivative of convolution calculated?

The partial derivative of convolution is calculated using the chain rule of calculus. It involves taking the derivative of the convolution function with respect to one variable, while treating the other variables as constants, and then multiplying it by the derivative of the variable with respect to the input of the convolution function.

Can the partial derivative of convolution be negative?

Yes, the partial derivative of convolution can be negative. This indicates that the output of the convolution function is decreasing with respect to the input variable. It is important to consider both positive and negative values of the partial derivative when analyzing the behavior of a convolution function.

What are some applications of the partial derivative of convolution?

The partial derivative of convolution has many applications in various fields. Some examples include edge detection in image processing, feature extraction in machine learning, and noise reduction in signal processing. It is also used in optimization algorithms to improve the performance of convolution functions.

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