Question about grad f on the x-y plane

In summary, the author claims that the quantity: \frac{dx}{df} = \frac{f_{,x}}{f_{,x}^2 + f_{,y}^2} (similar expression for dy/df) is equivalent to the following: \frac{dy}{dx} = \frac{f_{,y}}{f_{,x}} assuming that x and y are independent. If x and y are not independent, then dx/dy will be different. Furthermore, the author claims that the geometric differences between dy/dx and v are irrelevant, and that the covector [dx,dy] = [f_{,x} , f_{,y
  • #1
7thSon
44
0
Suppose I have a smooth scalar function f defined on some region in the x-y plane. Its partial derivatives with respect to x and y are well-defined.

Someone explain this "proof" to me that the quantity:

[tex] \frac{dx}{df} = \frac{f_{,x}}{f_{,x}^2 + f_{,y}^2} [/tex] (similar expression for dy/df)

The authors from which I read this do this by taking the the total differential of f
[itex] df = \frac{\partial f}{\partial x} dx + \frac{\partial f}{\partial y} dy [/itex]

dividing both sides by dx:
[itex] \frac{df}{dx} = frac{\partial f}{\partial x} + \frac{\partial f}{\partial y} \frac{dy}{dx} [/itex]

and substituting in the relation

[tex] \frac{dy}{dx} = \frac{f_{,y}}{f_{,x}} [/tex]

which gives you the resulting expression.

I would be fine with this if they substituted in the normal result of the implicit function theorem, which gives you, for a level set of f,
[tex] \frac{dy}{dx} = - \frac{f_{,x}}{f_{,y}} [/tex]

However, it seems like since they decided they were more interested in the normal to the level set, rather than the tangent to the level set, they substituted in the different value of dy/dx! Why are they allowed to substitute in a different value for dy/dx, or is there something that I'm missing?

Another thing I was thinking was maybe there is a dual to the idea of the dot product of grad f with a tangent vector? But instead, between a total differential and a covector [dx,dy]? Is that why they can use an expression for dy/dx that is not the tangent to the level curve?
 
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  • #2
Maybe someone could start out by explaining to me whether or not it is correct to write, as this author does,

[tex] \frac{dx}{df} = \frac{\partial x}{\partial f} + \frac{\partial x}{\partial y} \frac{dy}{df} [/tex]

Isn't [itex] \frac{\partial x}{\partial y} [/itex] always zero?
 
  • #3
This thread has gotten a lot of views but no responses, can someone at least say because they are perplexed or maybe my initial post is too long :P
 
  • #4
If x and y are independent variables, then, yes, dx/dy is 0. And the formula you give makes no sense. However, I suspect that this is a situation where x and y are not independent. If f(x,y) is defined for all (x, y) in the plane but we then restrict ourselves to some curve y= u(x), we say that
[tex]\frac{df}{dx}= \frac{\partial f}{\partial x}+ \frac{\partial f}{\partial y}\frac{dy}{dx}[/tex]

which leads to the results given.
 
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  • #5
HallsofIvy said:
If x and y are independent variables, then, yes, dx/dy is 0. And the formula you give makes no sense. However, I suspect that this is a situation where x and y are not independent. If f(x,y) is defined for all (x, y) in the plane but we then restrict ourselves to some curve y= u(x), we say that
[tex]\frac{df}{dx}= \frac{\partial f}{\partial x}+ \frac{\partial f}{\partial y}\frac{dy}{dx} which leads to the results given.

Thanks HallsOfIvy. So this makes sense that there is a restriction of y to be a function of x, or vice versa... given that the partial derivative identity i described looks like a consequence of the implicit function theorem (though strangely different for a reason that still eludes me).

What I am still hoping someone could clarify are the geometric differences between

[tex] \frac{dy}{dx} vs. \frac{\partial y}{\partial x} [/tex]
[tex] \frac{df}{dx} vs. \frac{\partial f}{\partial x} [/tex] and
[tex] \frac{dx}{df} vs. \frac{\partial x}{\partial f}[/tex]

Most confusingly, the exact derivative [itex] \frac{dy}{dx}[/itex] seems to be on a level set of f, which seems to be the same case I would get if I wrote y as an implicit function of x restricted to some level contour I was considering. (i.e. why is that any different from [itex] \frac{\partial y}{\partial x} [/itex]
 
  • #6
Ok, I think 60% of my remaining doubts will be answered if someone can answer the following question:

We know that the tangent vector

[tex] \mathbf{v} = \nabla f = \frac{\partial f}{\partial x} \mathbf{e_1} + \frac{\partial f}{\partial y} \mathbf{e_2} [/tex]

points in the direction of maximum ascent of the scalar function f at some point p.

In the same way, does the covector [dx,dy] = [itex] [ f_{,x} , f_{,y} ] [/itex] point in the fastest rate of incremental ascent of f in the covector space?

Am I just completely making up concepts that don't exist? :)
 

1. What is the gradient of a function on the x-y plane?

The gradient of a function on the x-y plane is a vector that represents the rate of change of the function in both the x and y directions. It is calculated by taking the partial derivatives of the function with respect to x and y, and combining them into a vector.

2. How is the gradient represented on the x-y plane?

The gradient is typically represented as a vector with an arrow pointing in the direction of the steepest increase of the function. The length of the arrow represents the magnitude of the gradient.

3. What is the significance of the gradient on the x-y plane?

The gradient on the x-y plane is significant because it provides information about the direction and rate of change of the function at any given point. It can be used to find the maximum and minimum values of a function, as well as the direction in which the function is increasing or decreasing.

4. How is the gradient of a function on the x-y plane used in real-world applications?

The gradient is used in many real-world applications, such as in physics and engineering, to analyze the behavior of systems and optimize their performance. It is also used in machine learning and data analysis to find the best fit for a given dataset and make predictions.

5. Can the gradient on the x-y plane be negative?

Yes, the gradient on the x-y plane can be negative. This indicates that the function is decreasing in that direction, or that the rate of change is negative. A negative gradient can also represent a local minimum of the function.

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