Gradient of an inverse vector function?

In summary, the conversation discusses the vector function f(\vec{x}) = |\vec{x}|^r and the task of finding the gradient of this function. It is mentioned that the output of this function is a scalar and that the gradient can be found by taking partial derivatives. An example of computing the gradient is provided using the first component of the function.
  • #1
CAF123
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Homework Statement


Consider [tex] f(\vec{x}) = |\vec{x}|^r, [/tex] where [itex] \vec{x} \in ℝ^n [/itex] and [itex] r \in ℝ[/itex].
Find [itex] \vec{∇}f [/itex]

The Attempt at a Solution


I know a vector function maps real numbers to a set of vectors, but here I believe we have the opposite. (inverse of a vector function, assuming inverse exists?)
I am unsure of where to go next.
 
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  • #2
Assuming the usual Euclidean norm on ℝ^n, the output is just a scalar. Computing the gradient, then, is a matter of taking partial derivatives. For example, the first component of the gradient is the partial of f with respect to x_1. If f= (sqrt(x_1^2 + ... + x_n^2))^r = (x_1^2 + ... + x_n^2) ^ (r/2) then the partial with respect to x_1 is r/2(x_1^2 + ... + x_n^2)^(r/2-1)*(2x_1). Continuing in this fashion gives the gradient.
 

Related to Gradient of an inverse vector function?

1. What is the gradient of an inverse vector function?

The gradient of an inverse vector function is a vector that represents the rate of change of the function in multiple directions. It is also known as the Jacobian matrix and is used to find the direction and magnitude of the steepest ascent or descent of the function.

2. How is the gradient of an inverse vector function calculated?

The gradient of an inverse vector function is calculated by taking the partial derivatives of the function with respect to each variable and arranging them in a matrix. This matrix is also known as the Jacobian matrix and is a representation of the gradient of the function.

3. What is the significance of the gradient of an inverse vector function?

The gradient of an inverse vector function is an essential tool in vector calculus and optimization problems. It helps in finding the direction and magnitude of the steepest ascent or descent of a function, which is crucial in many scientific and engineering applications.

4. Can the gradient of an inverse vector function be negative?

Yes, the gradient of an inverse vector function can be negative. The sign of the gradient depends on the direction of the steepest descent of the function. If the gradient is negative, it means that the function is decreasing in that direction, and if it is positive, the function is increasing.

5. How is the gradient of an inverse vector function used in machine learning?

The gradient of an inverse vector function is used in machine learning algorithms, such as gradient descent, to optimize the parameters of a model. It helps in finding the direction and magnitude of the steepest descent, which is used to update the parameters and minimize the error in the model.

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