Differentiation of the l1 norm of gradient

In summary, the conversation discusses the differentiation of the norm of the gradient of a function F(x,y,z). Specifically, the question is about the differentiation of |∇F|^{α}, where α is a constant. The answer is given as div(\frac{∇F}{|∇F|}), where div stands for divergence. The conversation also suggests looking into the total differential of the gradient and considering the euclidean norm in terms of squared-components.
  • #1
wilsonnl
1
0
Hi everyone, I need help with a derivation I'm working on, it is the differentiation of the norm of the gradient of function F(x,y,z):

[itex]\frac{∂}{∂F}[/itex](|∇F|[itex]^{α}[/itex])

The part of [itex]\frac{∂}{∂F}[/itex]([itex]\frac{∂F}{∂x}[/itex]) is bit confusing.

(The answer with α=1 is div([itex]\frac{∇F}{|∇F|}[/itex]), where div stands for divergence.)

Any ideas? many thanks!
 
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  • #2
Hey wilsonnl and welcome to the forums.

It looks like what you are trying to get is the total differential of the gradient. You can express the total differential in terms of dF = dF/dx*dx + dF/dy*dy + dF/dz*dz.

The thing is you are trying to differentiate with respect to the total differential which does make sense in the context of a norm (since this differential will correspond to the total change in each orthogonal component summed up).

I'd suggest you look into whether the total differential is what you want since it can be used as an analog to see how the total change of the length of something is changing with respect to all elements.

The norm, if it's euclidean will have things in terms of squared-components (i.e |x^2 + y^2 + z^2| for the 1-norm) so you can translate that into functions of (x,y,z) and then look at the total differential dF of this function.
 
  • #3
Since this has nothing to do with "differential equations", I am moving it to "Calculus and Analysis".
 

1. What is the "l1 norm" in the context of gradient differentiation?

The l1 norm is a mathematical measure of the size or magnitude of a vector. In the context of gradient differentiation, it is used to measure the change in a function with respect to its input variables.

2. How is the l1 norm of gradient used in scientific research?

The l1 norm of gradient is commonly used in scientific research to measure the smoothness of a function or to identify areas of high change or discontinuity. It is also used in optimization algorithms to minimize the error or cost function.

3. What is the difference between l1 norm and l2 norm of gradient?

The l1 norm of gradient is the sum of the absolute values of the gradient vector, while the l2 norm is the square root of the sum of the squared values. This means that the l1 norm is more sensitive to outliers and can result in sparser solutions compared to the l2 norm.

4. How do you calculate the l1 norm of gradient?

To calculate the l1 norm of gradient, you first need to compute the gradient vector of the function with respect to each input variable. Then, take the absolute value of each component of the gradient vector and sum them together. The resulting value is the l1 norm of gradient.

5. What are the applications of l1 norm of gradient in machine learning?

The l1 norm of gradient is commonly used in machine learning for feature selection, where it helps to identify the most important features for predicting the target variable. It is also used in regularization techniques to prevent overfitting and improve the generalization performance of models.

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