Help with some questions from spivak calc on manifolds

In summary, the conversation is about a problem related to directional derivatives and differentiability in a multivariable calculus textbook. The problem involves showing that the directional derivative in one direction is equal to the directional derivative in another direction, and also that it is equal to the differential of the function at a point. The conversation also includes a request for clarification on the notation used in the problem.
  • #1
osnarf
209
0
Hi everyone, been away for a while I got bogged down with my classes so didn't have time to work on this book and haven't been on the forums much. Was getting caught back up to where I was before in here and I ran into a problem that I can't figure out the notation on.

I am only looking for help understanding what he is asking, please no hints to the problem (yet, lol).

In my book, it is on page 33 (problem 2-29).

Homework Statement


Let f: Rn ---> R. For x in Rn, the limit

limit as t-->0 of [ ( f(a+tx) - f(a) ) / t ]

if it exists, is denoted Dxf(a), and called the directional derivative of fa at a, in the direction x.

(part a) Show that Deif(A) = Dif(a) <----edit: kind of hard to see, the first subscript is ei

(part b) Show that Dtxf(a) = tDxf(a)

(part c) If f is differentiable at a, show that Dxf(a) = Df(a)(x), and therefore Dx+yf(a) = Dxf(a) + Dyf(a).

Homework Equations



The Attempt at a Solution



Again, haven't really tried to solve it yet, as I'm not sure what it's asking. (I'll probably start working on part b immediately after posting this, because I'm pretty sure I get what he's saying there). So, my questions:

(part a) ? what is ei? I looked earlier in the book, and I can't see anything close to what this might mean (he denotes second partial derivatives Di,jf(a), so that's not it). I would assume the ith element in the e vector, but that doesn't really seem to make sense here...

(part b) Just confirm for me that he means t [tex]\in[/tex] R, and we're all set.

(part c) I'm pretty sure I get this, I've been slightly unsure of what he means when he uses notation like this: Df(a)(x). Is this the same as saying f'(a)*(x), where x is the jacobian? Or is it f'(a)*a*x?

He mentions it earlier on in the book (Referencing page 20, theorem 2-3, part 5), for instance:
If p: R2-->R is defined by p(x,y) = x*y, then
Dp(a,b)(x,y) = bx + ay

I'm a little confused about the notation here, as well. In fact this has kind of had me confused the whole book.

In the quoted theorem,

p'(a,b) = (b a)

(b a) * (x y)transform = 1x1 matrix (bx + ay)

Does this mean that Dp(a,b) is the matrix of the linear transformation (Dp(a,b) = p'(a,b) ?) and he is multiplying it by the vector (x y)transform? Earlier on, he refers to Df(a) as the linear transform, so I'm assuming (x,y) are the inputs to be 'plugged in' to the linear transform Df(a) (like x and y are the variables to be plugged into the function f(x,y)). But I still feel like it should be written Dp(a,b)((x,y)transform) if this is the case (or written vertically to begin with), so I can't feel sure about it.

I wish he was a little more descriptive with his notations, as those are the only problems I have had with this book, his explanations of concepts are great, as in his single variable book.

Thanks for reading, I appreciate your help.
PS: Is there a way to input matrices, or at least vertical vectors so you don't have to put (x, y, z)t? I'm not very handy with tex yet.
 
Last edited:
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  • #2
If you don't feel like reading it all, more than anything else I want to know what he means in part a.
 
  • #3
Usually when I see [tex]e_i[/tex] I think of a basis vector, if that helps.
 
  • #4
That makes perfect sense actually, and its pretty easy to see why it's true after that. Don't know how I didn't get that thanks, been buggin me all night.

edit:
On further review (page 3 of the book), he defines the usual basis vectors as e1, e2, etc... didn't go back far enough i guess :( Fail on my part.

Still could use clarification on part C.
 

1. What are manifolds in calculus?

A manifold is a mathematical concept used in calculus to describe a space that is locally similar to Euclidean space. It is a topological space that can be described by coordinates and has a smooth structure, making it suitable for calculus operations.

2. What is the purpose of studying manifolds in calculus?

Studying manifolds in calculus allows us to generalize concepts and techniques from Euclidean space to more complex spaces. This is particularly useful in fields such as differential geometry, where manifolds are used to study curved spaces.

3. How are manifolds different from other mathematical concepts?

Manifolds are different from other mathematical concepts because they are defined in terms of local properties, rather than global properties. This means that a manifold can have different properties in different regions, making it a more flexible and versatile concept.

4. What are some examples of manifolds?

Some common examples of manifolds include spheres, tori, and projective spaces. In physics, spacetime is also considered a manifold. Additionally, any smooth surface in three-dimensional space can be described as a manifold.

5. How can I use manifolds in practical applications?

Manifolds have many practical applications in fields such as physics, engineering, and computer science. They are used to model and analyze complex systems, such as fluid flow, electromagnetism, and artificial intelligence. Additionally, manifolds are used in data analysis and machine learning to extract useful information from large datasets.

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