Carroll GR: Tangent Space & Partial Derivatives

In summary, the conversation discusses the use of a coordinate chart and a curve to define a function and how to differentiate this function with respect to a parameter. The concept of functions as maps is introduced and its application in exporting structure from one space to another is discussed.
  • #1
chartery
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TL;DR Summary
Difficulty understanding first equality in equation 2.9 (p 43) of Carroll's lecture notes.
He draws an n-manifold M, a coordinate chart φ : M → Rn, a curve γ : R → M, and a function f : M → R, and wants to specify ##\frac d {d\lambda}## in terms of ##\partial_\mu##.
##\lambda## is the parameter along ##\gamma##, and ##x^\mu## the co-ordinates in ##\text{R}^n##.

His first equality is ##\frac d {d\lambda}\text{f}## = ##\frac d {d\lambda}##(##\text {f} \circ \gamma##).
It is not clear to me how he can equate the derivative of a map from M to R, with that of a composite map from R to R.

(Feel free to indicate that the question shows my knowledge is inadequate for this level of study! :rolleyes:)
 
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  • #2
He really means the function ##f## seen as a function of ##\lambda##, so ##f\circ \gamma## so the first step is more of a definition of what he means.
 
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  • #3
Concrete (well, partly concrete) example: you have a function ##f(x,y)## defined on a 2d plane and a path ##\gamma## on that plane whose points have coordinates ##x_\gamma(\lambda)## and ##y_\gamma(\lambda)##, so the curve is parameterised by ##\lambda##. To calculate ##\frac d{d\lambda}f(x,y)## you just sub in the curve - ##\frac d{d\lambda}f(x_\gamma(\lambda),y_\gamma(\lambda))##, and now you've got a function of ##\lambda## that you can differentiate. OK so far?

The above maths, though, makes assumptions about coordinates that you may not wish to make (e.g. sometimes you can't make a single coordinate system cover all the space and the path might move between patches), and also assumes a dimension of 2. So Carrol is observing without explicit dependence on coordinates or dimensionality that to differentiate ##f## with respect to ##\lambda## he first needs to express ##f## as ##f(\lambda)##, and that's what ##f\circ\gamma## does.
 
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  • #4
Many thanks for helpful replies. To be sure I understand, he is defining his (plain!) f here to be f(##\gamma##(##\lambda##)) ?
 
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  • #5
chartery said:
Many thanks for helpful replies. To be sure I understand, he is defining his (plain!) f here to be f(##\gamma##(##\lambda##)) ?
Yes, you will often see this implicit assumption for brevity of notation.
 
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  • #6
Obvious with hindsight, though I think the diagram preceding, and the lack of any reference to f as a composite sent me on the detour. Thanks again.
 
  • #7
Carroll's notes were the first GR text I read. I think that idea of functions as maps from one space of one dimensionality to another, and chaining those maps together in order to export structure from one space into another, is one of the things I found harder to integrate into my thinking. Not because it's particularly difficult, but because it's a very different way of looking at something very familiar, and Carroll perhaps doesn't spend quite enough time (for me, anyway) on introducing it before using it with cheerful abandon.
 
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  • #8
@Ibix I'm with you on the cheerful, not to say complete, abandon :-)
 
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FAQ: Carroll GR: Tangent Space & Partial Derivatives

What is tangent space in mathematics?

Tangent space is a mathematical concept that describes the local behavior of a curved surface at a specific point. It is a vector space that is tangent to the surface at that point, meaning that it touches the surface at only one point and is parallel to the surface at that point.

How is tangent space related to partial derivatives?

Tangent space is closely related to partial derivatives because it is defined using the partial derivatives of a function. The basis vectors of the tangent space are the partial derivatives of the function with respect to each variable. This allows us to calculate the tangent space at a specific point by taking the partial derivatives of the function at that point.

Why is tangent space important in multivariable calculus?

Tangent space is important in multivariable calculus because it allows us to study the behavior of a function at a specific point on a curved surface. It helps us understand how the function changes in different directions at that point, and is essential for finding critical points and determining the behavior of a function near those points.

How is the tangent space of a surface defined?

The tangent space of a surface is defined as the set of all tangent vectors at a specific point on the surface. These tangent vectors are defined using the partial derivatives of the surface's parametric equations. The tangent space is a vector space, meaning that it has a basis and can be used for vector operations.

Can tangent space be extended to higher dimensions?

Yes, tangent space can be extended to higher dimensions. In fact, it is defined for any differentiable manifold, which is a mathematical object that can be described using coordinates and functions. Tangent space is essential for studying these manifolds and understanding their behavior at specific points.

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