Gradient of an inverse vector function?

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SUMMARY

The discussion focuses on finding the gradient of the function f(𝑥) = |𝑥|^r, where 𝑥 ∈ ℝ^n and r ∈ ℝ. The user correctly identifies that the gradient can be computed by taking partial derivatives of the function with respect to its components. Specifically, the first component of the gradient is derived as ∇f = (r/2)(|𝑥|^r)^(1/2 - 1)(2𝑥_1), leading to a general formula for the gradient across all components of the vector function.

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  • Understanding of vector calculus and gradients
  • Familiarity with Euclidean norms in ℝ^n
  • Knowledge of partial derivatives
  • Basic concepts of scalar and vector functions
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Students and professionals in mathematics, physics, and engineering who are working with vector calculus and need to compute gradients of scalar functions.

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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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.
 

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