What is the proof for the property of taking the gradient of a function?

In summary, the given operation of taking the gradient of a function has the property that (∇(u))n = n*un-1*∇u. This can be proven using the chain rule for partial derivatives.
  • #1
ohlala191785
18
0

Homework Statement


Show that the operation of taking the gradient of a function has the given property. Assume u and v are differentiable functions of x and y and that a and b are constants.

Operation: (∇(u))n = n*un-1*∇u

Homework Equations



The gradient vector of f is <∂f/∂x,∂f/∂y>, where f is a function of x and y (in other words f(x,y)).

The Attempt at a Solution



I tried proof by induction, but I have a lot of gaps.

Base, n=1: ∇u = n*u0∇u. But what is u0 if u is a function?

Assume n = k: ∇uk = n*uk-1∇u

For k=k+1, ∇uk+1 = n*uk∇u.

Isn't proof by induction for sums though? I can't seem to identify the sum here.Any help would be greatly appreciated!
 
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  • #2
ohlala191785 said:

Homework Statement


Show that the operation of taking the gradient of a function has the given property. Assume u and v are differentiable functions of x and y and that a and b are constants.

Operation: (∇(u))n = n*un-1*∇u

Homework Equations



The gradient vector of f is <∂f/∂x,∂f/∂y>, where f is a function of x and y (in other words f(x,y)).

The Attempt at a Solution



I tried proof by induction, but I have a lot of gaps.

Base, n=1: ∇u = n*u0∇u. But what is u0 if u is a function?

Assume n = k: ∇uk = n*uk-1∇u

For k=k+1, ∇uk+1 = n*uk∇u.

Isn't proof by induction for sums though? I can't seem to identify the sum here.


Any help would be greatly appreciated!

You have got to mean ## \nabla (u^n)=n u^{n-1} \nabla u ##, ## (\nabla(u))^n ## doesn't mean anything. Just use the chain rule for partial derivatives.
 
  • #3
Oh I see. Thanks!
 

FAQ: What is the proof for the property of taking the gradient of a function?

1. What is the gradient operation?

The gradient operation is a mathematical concept used in multivariate calculus to calculate the rate of change of a function in multiple dimensions. It is represented by the symbol ∇ and is also known as the del operator.

2. How is the gradient operation used in proofs?

The gradient operation is used in proofs to show the relationship between a function and its derivatives in multiple dimensions. It is a fundamental concept in vector calculus and plays a key role in the study of optimization and vector fields.

3. What is the proof of the gradient operation?

The proof of the gradient operation involves using the chain rule and partial derivatives to show that the gradient of a function is equal to a vector containing the partial derivatives of the function with respect to each variable. It is a straightforward but fundamental proof in multivariate calculus.

4. Why is the gradient operation important in science?

The gradient operation is important in science because it allows us to understand how a function changes in multiple dimensions and how this affects other variables. It is used in many fields such as physics, engineering, and economics to model and optimize complex systems.

5. Are there any real-world applications of the gradient operation?

Yes, the gradient operation has many real-world applications. For example, it is used in gradient descent algorithms to optimize machine learning models, in fluid dynamics to model and predict fluid flow, and in economics to determine optimal production levels. It is a crucial tool in many scientific and engineering disciplines.

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