Multivariate Higher Order Derivatives

In summary: Anyway, you have ##h_{uv}=\partial_u\partial_v h=\partial_x\partial_y f##, so we can think of h as a linear combination of ##\partial_u## and ##\partial_v## and f as a linear combination of ##\partial_x## and ##\partial_y##. So, as we go around the loop, we can see that ##\partial_x\partial_y=\partial_u\partial_v=\partial_v\partial_u##. And we can see that ##\partial_x^2=\partial_u^2+\partial_v^2##, and ##\partial_y^2=\partial_u^2+\partial_v^2##.
  • #1
Yagoda
46
0

Homework Statement


Let [itex]h(u,v) = f(u+v, u-v)[/itex]. Show that [itex]f_{xx} - f_{yy} = h_{uv}[/itex] and [itex]f_{xx} + f_{yy} = \frac12(h_{uu}+h_{vv}) [/itex].


Homework Equations





The Attempt at a Solution


I'm always confused on how to tackle these types of questions because there isn't an actual function to differentiate.
So I am assuming here that x = u+v and y = u-v and going from there. So I need to find the first partial with respect to x, which might be something like [itex]f_x = \frac{\partial f}{\partial(u+v)}[/itex] since I need to get it in terms of u and v, but this doesn't seem right. What sort of approach do I need to use on these questions?

Edit: What I've done now is write [itex]h_{uv} = \frac{\partial}{\partial v}\frac{\partial h}{\partial u} = \frac{\partial}{\partial v} ( \frac{\partial f}{\partial x} \frac{\partial x}{\partial u} + \frac{\partial f}{\partial y} \frac{\partial y}{\partial u}) =\frac{\partial}{\partial v} (\frac{\partial f}{\partial x} + \frac{\partial f}{\partial y}) [/itex]. I'm having trouble converting from u's and v's to x's and y's.
 
Last edited:
Physics news on Phys.org
  • #2
[itex]u=\frac{x+y}{2} [/itex] and [itex] v=\frac{x-y}{2} [/itex] so [itex] f(x,y)=h(\frac{x+y}{2},\frac{x-y}{2}) [/itex]

So we have:

[itex]
f_x=h_u u_x+h_v v_x \Rightarrow f_x=\frac{1}{2}h_u+\frac{1}{2}h_v [/itex]

[itex]
f_{xx}=\frac{1}{2}(h_{uu}u_x+h_{uv}v_x+h_{vu}u_x+h_{vv}v_x)
[/itex]

I think you can continue yourself.
 
  • #3
Yagoda said:

Homework Statement


Let [itex]h(u,v) = f(u+v, u-v)[/itex]. Show that [itex]f_{xx} - f_{yy} = h_{uv}[/itex] and [itex]f_{xx} + f_{yy} = \frac12(h_{uu}+h_{vv}) [/itex].

Homework Equations


The Attempt at a Solution


I'm always confused on how to tackle these types of questions because there isn't an actual function to differentiate.
So I am assuming here that x = u+v and y = u-v and going from there. So I need to find the first partial with respect to x, which might be something like [itex]f_x = \frac{\partial f}{\partial(u+v)}[/itex] since I need to get it in terms of u and v, but this doesn't seem right. What sort of approach do I need to use on these questions?

Edit: What I've done now is write [itex]h_{uv} = \frac{\partial}{\partial v}\frac{\partial h}{\partial u} = \frac{\partial}{\partial v} ( \frac{\partial f}{\partial x} \frac{\partial x}{\partial u} + \frac{\partial f}{\partial y} \frac{\partial y}{\partial u}) =\frac{\partial}{\partial v} (\frac{\partial f}{\partial x} + \frac{\partial f}{\partial y}) [/itex]. I'm having trouble converting from u's and v's to x's and y's.

For thinking about these things it's sometimes good to think about the operators without worrying about the functions. Define, for example, ##\partial_u(F)=\frac{\partial F}{\partial u}##. Then wouldn't it be true that ##\partial_u=\partial_x+\partial_y## and ##\partial_v=\partial_x-\partial_y## from the chain rule? It can save you a lot of texing and even spare some confusion. It's kind of the same as your underscore notation for partial derivatives.
 
Last edited:

Related to Multivariate Higher Order Derivatives

1. What are multivariate higher order derivatives?

Multivariate higher order derivatives are mathematical tools used to describe the rate of change of a function with respect to multiple variables. They are extensions of the concept of derivatives, which describe the rate of change of a function with respect to a single variable.

2. How are multivariate higher order derivatives calculated?

Multivariate higher order derivatives can be calculated using a process called differentiation, which involves taking the limit of a function as the change in the input variables approaches zero. This process can be repeated multiple times to calculate higher order derivatives.

3. What are the applications of multivariate higher order derivatives?

Multivariate higher order derivatives have many applications in fields such as physics, economics, and engineering. They are used to model complex systems and describe the relationships between multiple variables.

4. How do multivariate higher order derivatives relate to partial derivatives?

Partial derivatives are a type of multivariate derivative that describe the rate of change of a function with respect to one variable while holding all other variables constant. Higher order partial derivatives can be calculated in a similar manner to multivariate higher order derivatives.

5. What is the difference between a multivariate higher order derivative and a multivariate partial derivative?

The main difference between these two types of derivatives is that multivariate higher order derivatives involve calculating the rate of change of a function with respect to multiple variables, while partial derivatives only involve one variable at a time. Additionally, higher order derivatives involve taking the derivative of a derivative, while partial derivatives only involve taking the first derivative.

Similar threads

Replies
4
Views
678
  • Calculus and Beyond Homework Help
Replies
10
Views
2K
  • Calculus and Beyond Homework Help
Replies
6
Views
590
  • Calculus and Beyond Homework Help
Replies
21
Views
1K
  • Calculus and Beyond Homework Help
Replies
4
Views
821
  • Calculus and Beyond Homework Help
Replies
11
Views
1K
  • Calculus and Beyond Homework Help
Replies
4
Views
592
  • Calculus and Beyond Homework Help
Replies
1
Views
980
Replies
2
Views
926
  • Calculus and Beyond Homework Help
Replies
4
Views
839
Back
Top