MHB Cant quite see this grad relation

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The discussion centers on the relationship between the gradients of functions and their parallelism when the gradient of a function f(u, v) equals zero. Participants question the validity of replacing differential forms with gradients and how this leads to the conclusion that the gradients of u and v are parallel. It is clarified that if the gradient of f is zero, the only way for the sum of two vectors (the gradients) to equal zero is if they are equal and opposite, thus confirming their parallelism. An example function is proposed to illustrate these concepts further, emphasizing the importance of understanding the underlying principles of multivariable calculus. Overall, the discussion highlights the complexities of gradient relationships in higher dimensions.
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Book states: $\nabla f\left(u, v\right) =0 = \frac{\partial f}{\partial u}\nabla u + \frac{\partial f}{\partial v} \nabla v, \therefore \nabla u$ and $ \nabla v $ are parallel

1. I know $d f\left(u, v\right) = \frac{\partial f}{\partial u}du + \frac{\partial f}{\partial v} dv$, but how can we just replace the d<...> by $\nabla <... >$ like that? seems a little circular?

2. Can't anyway see how that makes them parallel?
I get $ =\frac{\partial f}{\partial u}\left( \pd{u}{x}, \pd{u}{y},\d{u}{z}\right) + \frac{\partial f}{\partial v}\left( \pd{v}{x}, \pd{v}{y},\d{v}{z}\right)
= \left( \pd{f}{x}, \pd{f}{y},\d{f}{z}\right) + \left( \pd{f}{x}, \pd{f}{y},\d{f}{z}\right)$ so both tterms are equal and parallel, but why does that make the $\nabla$ parts parallel?
 
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ognik said:
Book states: $\nabla f\left(u, v\right) =0 = \frac{\partial f}{\partial u}\nabla u + \frac{\partial f}{\partial v} \nabla v, \therefore \nabla u$ and $ \nabla v $ are parallel

1. I know $d f\left(u, v\right) = \frac{\partial f}{\partial u}du + \frac{\partial f}{\partial v} dv$, but how can we just replace the d<...> by $\nabla <... >$ like that? seems a little circular?

It's a generalization for multiple dimensions.
The chain rule for multiple dimensions says:
$$\d f x = \pd f u \pd u x + \pd f v \pd v x$$

2. Can't anyway see how that makes them parallel?
I get $ =\frac{\partial f}{\partial u}\left( \pd{u}{x}, \pd{u}{y},\d{u}{z}\right) + \frac{\partial f}{\partial v}\left( \pd{v}{x}, \pd{v}{y},\d{v}{z}\right)
= \left( \pd{f}{x}, \pd{f}{y},\d{f}{z}\right) + \left( \pd{f}{x}, \pd{f}{y},\d{f}{z}\right)$ so both tterms are equal and parallel, but why does that make the $\nabla$ parts parallel?

Are they really parallel?
And does $\pd f u \pd u x = \pd f x$ hold?

Let's pick an example.
Suppose we pick $f(u,v)=u^2+v^2-1, \quad u(x,y)=x+y, \quad v(x,y)=x-y$
How would that work out?Oh, and I've moved your thread to Calculus, which is where Partial Derivatives and Multi-variable Calculus belongs.
Please check the descriptions of the forums on the main page.
 
I like Serena said:
It's a generalization for multiple dimensions.
The chain rule for multiple dimensions says:
$$\d f x = \pd f u \pd u x + \pd f v \pd v x$$.
So $ \pd{f}{x}, \pd{f}{y} = \nabla f = \left( \pd{f}{u}\pd{u}{x}+\pd{f}{v}\pd{v}{x}, \pd{f}{u}\pd{u}{y}+\pd{f}{v}\pd{v}{y} \right) = \pd{f}{u} \left( \pd{u}{x},\pd{u}{y} \right) + \pd{f}{v}\left( \pd{v}{x},\pd{v}{y} \right) ...$?

I like Serena said:
Are they really parallel?.
I couldn't see why they should be, but the book says if $\nabla f = 0$, then they are, I can't see this is a general case?

I like Serena said:
And does $\pd f u \pd u x = \pd f x$ hold?.
didn't understand why you asked, its missing the $\pd{f}{v}...$ part?

I like Serena said:
Oh, and I've moved your thread to Calculus, which is where Partial Derivatives and Multi-variable Calculus belongs.
Please check the descriptions of the forums on the main page.
Thanks, I was honestly undecided 'cos in my book this is a section on vectors, just looking at grad, curl etc...
 
ognik said:
So $ \pd{f}{x}, \pd{f}{y} = \nabla f = \left( \pd{f}{u}\pd{u}{x}+\pd{f}{v}\pd{v}{x}, \pd{f}{u}\pd{u}{y}+\pd{f}{v}\pd{v}{y} \right) = \pd{f}{u} \left( \pd{u}{x},\pd{u}{y} \right) + \pd{f}{v}\left( \pd{v}{x},\pd{v}{y} \right) ...$?
Correct.

I couldn't see why they should be, but the book says if $\nabla f = 0$, then they are, I can't see this is a general case?
I actually overlooked that it was given that $\nabla f = 0$.
In that case they have to be parallel, since the only way 2 vectors can sum up to zero is if they are equal and opposite.
Since the partial derivatives are only scalars, that implies that one vector must be a multiple of the other. That is, parallel.

didn't understand why you asked, its missing the $\pd{f}{v}...$ part?
It's not true, which should become clear if we work out the example I posted.

Thanks, I was honestly undecided 'cos in my book this is a section on vectors, just looking at grad, curl etc...
If you look at the descriptions of the forums, it should be clear where it should go, regardless of the section in your book.
 

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