Gradient vector property proofs

In summary, the operation of taking the gradient of a function has the given property. The quotient rule for taking the gradient of a quotient of two differentiable functions is given by Δ(u/v) = vΔu - uΔv / v^2. And for positive integer values of n, the power rule for taking the gradient of a function raised to a power is given by Δu^n = nu^(n-1)Δu. The proof for this is established by using induction on n and showing the base case
  • #1
fastXattack
3
0

Homework Statement


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


Homework Equations


Δ = gradient vector

1) Δ(u/v) = vΔu - uΔv / v^2

2) Δu^n = nu^(n-1)Δu


The Attempt at a Solution


I tried taking the partial derivative of (u/v) and separating the terms but I didn't get the ending result.

For 2, I don't even know where to begin because it is an exponent.
 
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  • #2
fastXattack said:

Homework Statement


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


Homework Equations


Δ = gradient vector

1) Δ(u/v) = vΔu - uΔv / v^2

2) Δu^n = nu^(n-1)Δu


The Attempt at a Solution


I tried taking the partial derivative of (u/v) and separating the terms but I didn't get the ending result.

For 2, I don't even know where to begin because it is an exponent.
Let's try a simpler problem:
[tex]\nabla(uv) = v\nabla{u} + u\nabla{v}[/tex]

[tex]\nabla(uv) = <\frac{\partial (uv)}{\partial x}, \frac{\partial (uv)}{\partial y}>[/tex]
[tex]= <u\frac{\partial v}{\partial x} + v\frac{\partial u}{\partial x}, u\frac{\partial v}{\partial y} + v\frac{\partial u}{\partial y}>[/tex]
[tex]= u< \frac{\partial v}{\partial x}, \frac{\partial v}{\partial y}> + v< \frac{\partial u}{\partial x}, \frac{\partial u}{\partial y} > = u\nabla{v} + v\nabla{u}[/tex]
 
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  • #3
I was given that problem for homework as well and I was able to do that one. I have an idea on how to do the quotient rule one, but no idea for the second question.
 
  • #4
If (2) is to be done for n a positive integer, use induction on n.
 
  • #5
This is my attempt at the quotient rule proof. Did I do the correct thing for this problem at least? I'm still unsure how to do the second question...
 

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  • #6
Yes, that looks fine although the second line is difficult to read.

For the other problem, use induction (the tacit assumption is that n is a positive integer). The base case is easy to establish.
[tex]\nabla u^1 = \nabla u = 1 \nabla u[/tex]

Now assume that for n = k,
[tex]\nabla u^k = k \nabla u^{k - 1}[/tex]

To complete the proof, show that for n = k + 1
[tex]\nabla u^{k + 1} = (k + 1) \nabla u^k[/tex]
 

Related to Gradient vector property proofs

1. What is the gradient vector property?

The gradient vector property states that the gradient vector of a function at a given point is perpendicular to the level curve of the function at that point. This means that the gradient vector points in the direction of the steepest increase of the function.

2. How is the gradient vector property used in proofs?

The gradient vector property is often used in proofs to show that a given function has a maximum or minimum value at a certain point. By showing that the gradient vector is zero at that point, it can be concluded that the function is either at a maximum or minimum value at that point.

3. What is the difference between a gradient vector and a normal vector?

A gradient vector is specific to a function, while a normal vector is a general concept that can be applied to any geometric shape. The gradient vector of a function gives information about the direction and magnitude of the function's change, while a normal vector is perpendicular to a surface or curve at a given point.

4. Can the gradient vector property be applied to functions with more than two variables?

Yes, the gradient vector property can be applied to functions with any number of variables. In these cases, the gradient vector is a vector of partial derivatives with respect to each variable. The property still holds true, with the gradient vector pointing in the direction of the steepest increase of the function.

5. Are there any limitations to using the gradient vector property in proofs?

The gradient vector property can only be used to prove the existence of a maximum or minimum value at a given point. It does not provide information on the actual value of the function at that point. Additionally, it can only be used for continuous functions, as discontinuous functions do not have a well-defined gradient vector.

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