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Let $\{e^1,...,e^m\}$ be the standard basis of $(\Bbb R^m)^*$ . Let $(U,\varphi)$ be a chart of a smooth manifold $M$ of dimM=m. Let $\varphi(p)=(x^1(p),...,x^m(p))$.

In the middle of some proof I found the following equalities that I am having trouble checking:

$d(x^i\circ \varphi^{-1})_{\varphi(p)}=d(e^i)_{\varphi(p)}=e^i$

So my question is why are these two last equalities true?

  1. why is $x^i\circ \varphi^{-1}=e^i$ ?

  2. why is $d(e^i)_{\varphi(p)}=e^i$ ? In this case shouldn't the differential of a constant function be 0?

2 Answers2

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  1. $x^i$ is by definition the $i^{th}$ component function of the map $\phi$, i.e $x^i=\text{pr}^i\circ\phi$, where $\text{pr}^i:\Bbb{R}^n\to\Bbb{R}$ is the projection map. So, $x^i\circ\phi^{-1}$ is obviously just $\text{pr}^i$, (well, ok technically, it is the restriction $\text{pr}^i|_{\phi[U]}:\phi[U]\subset\Bbb{R}^n\to\Bbb{R}$). The dual basis of the standard basis serves exactly this purpose. The map $e^i:\Bbb{R}^n\to\Bbb{R}$ is by definition the same linear map as $\text{pr}^i:\Bbb{R}^n\to\Bbb{R}$: they both take as input the tuple $(a^1,\dots, a^n)$ and spit out the number $a^i$.
  2. Note that $e^i$ is a linear map, so its derivative at every point is that same linear map. Perhaps this will ease your mind: the thing that is constant is that $d(e^i)_{a}=e^i$ for all points $a\in \phi[U]$. This should sound reasonable because the function $f:\Bbb{R}\to\Bbb{R}$, $f(x)=3x$ for example is linear and has constant derivative $f’(x)=3$ for all $x$.

Regarding (2), one other thing I guess you might be confusing things with is that while what I wrote is true, sometimes people are not considering the map $e^i$ itself, but rather the constant mapping $a\mapsto e^i$ from an open subset of $\Bbb{R}^n$ into $(\Bbb{R}^n)^*$ (well really a more common scenario is to consider the constant vector-valued map $a\mapsto e_i$, where $e_i\in\Bbb{R}^n$ is the $i^{th}$ standard basis vector). The derivative of this map does vanish. By abuse of notation, one might refer to the map $a\mapsto e^i$ simply as $e^i$ and consequently write $de^i=0$, but you should be mindful of the overload of notation, and not confuse a given map with its constant value (especially when that constant value is itself a linear function). But, let me be clear, the answer to your question is what I wrote above in the second paragraph.

peek-a-boo
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  • Thanks for the answer. I read all the links you quoted and everything. Regarding 2 and the example you gave about f(x)=3x .If the derivative of a linear map at every point is that same linear map, how come the derivative is not 3x ? Because if I just use that " its derivative at every point is that same linear map", that is what I would get. How do I solve this incongruency? – some_math_guy Jan 04 '24 at 17:57
  • Does that means that the derivative in the manifolds sense or in the multidimensional case which should be just a generalization( let me call it the tangent map to distinguish it), does not coincide with the single-variable-calculus derivative when the formula is applied to a function like f(x)=3x? – some_math_guy Jan 04 '24 at 17:57
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    $f’(x)=3$ is, for each $x\in\Bbb{R}$, just a number. Think of this as the $1\times 1$ matrix representation of the linear map $h\mapsto 3h$, which is again just the linear map $f$. The only thing you seem to be getting tripped up on now is linear maps vs their matrix representations. – peek-a-boo Jan 04 '24 at 17:58
  • uhmmm...so the stament " its derivative at every point is that same linear map." is not to be taken in the literal sense, can't it be worded in a way that reflects this? – some_math_guy Jan 04 '24 at 18:01
  • it is literal (once you regard ‘derivative’ to mean “Frechet derivative”, because that’s what I defined in that link). Look at the theorem I proved in that link. So, for $f(x)=3x$, we have for each $x$, $df_x=f$ (at each point $x$, the Frechet derivative $df_x$ equals $f$; this is a precise equality of linear transformations), and the ($1\times 1$) matrix representation of both sides gives $f’(x)=3$ (the familiar statement we all know). – peek-a-boo Jan 04 '24 at 18:02
  • You are right I know the matrix representation expresses the same as the linear map, map I am having trouble seeing when we have or the other or how to quickly go from one to the other. I think it would be clearer to say that the tangent map is the matrix representation of the linear map. So first all by definition we have $e^1(x^1,...,x^n)=x^1$, and the differential is the Jacobian matrix, so the matrix $(1,0,....,0)$ and going back to function notation I multiply this matrix by the column vector $(x^1,...,x^n)^T $this is the map $(x^1,...,x^n) \mapsto x^1 $ and therefore $e^1$ itself – some_math_guy Jan 04 '24 at 18:31
  • “tangent map is the matrix representation”… umm no, the tangent map is a linear map (that’s why we call it tangent map). Its matrix representation is often called the Jacobian matrix. – peek-a-boo Jan 04 '24 at 18:40
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First, the left side of the equation is a scalar function and the right side is a $1$-form, so the equation is obviously incorrect.

Here $x^i$ has two separate but closely related definitions. One is simply the $i$-th coordinate of $\newcommand\R{\mathbb{R}}\R^n$. The other is the function $x^i: U \rightarrow \R$. To keep track of things more easily, let's rename one of them.

Let's use $x^i$ as the $i$-th coordinate of a point in $\R^n$. In other words, denote a point in $\R^n$ by $$x = (x^1, \dots, x^n).$$ On the other hand, let's denote the coordinate map by $$\phi = (\phi^1, \dots, \phi^n),$$ where each $\phi^i: U \rightarrow \R$.

The equation can now be written as $$ d(\phi^i\circ\phi^{-1}) = e^i. $$ This can be proved trivially as follows: For any $x \in \phi(U)$, $$ \phi\circ\phi^{-1}(x) = x, $$ and therefore $$ \phi^i(\phi^{-1}(x)) = x^i. $$ Therefore, $$ d(\phi^i\circ\phi^{-1}(x)) = dx^i = e^i. $$ I agree that $d(e^i) = 0$.

ADDED COMMENT:

Why I consider $e^i$ to be an abuse of notation:

There is a distinction worth making between the manifold $\mathbb{R}^n$ and the vector space of tangent vectors $\mathbb{R}^n$. On manifold, the differential of a function is a basic concept. On the other hand, the tangent space is a vector space, which belongs to the world (i.e., categories) of linear algebra, where differentiation is never useful (for obvious reasons). Even though technically you can differentiate an element of $T_p^*M$, it's pointless because all you get is the element itself.

There is a tendency (especially by physicists) to get sloppy about the distinction between the manifold $\mathbb{R}^n$ and the tangent space $\mathbb{R}^n$. When the authors write $(\mathbb{R}^n)^*$, it indicates that they are aware of and want to account for this distinction. But then they violate it by conflating the two different meanings of $e^i$.

Too many people treat the notation of modern differential geometry as just that, convenient index-free notation. However, it's much more than that. It elucidates the functorial aspects of differential geometry, especially the parallels with the functorial aspects of linear algebra. For that reason, I like to call differential geometry "parameterized linear algebra".

Deane
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  • About $d(e^i)=0$ How is this not in conflict with the answer of peek-a-boo ? Is it because you are interpreting $e^i$ not as a linear functional but as described in his comment at the end of his answer? – some_math_guy Jan 04 '24 at 18:14
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    @some_math_guy Deane is interpreting $de^i=0$ as in my last paragraph, but the source you’re reading is intending it as in my second paragraph. – peek-a-boo Jan 04 '24 at 18:57
  • When I first learned this stuff, I found the abuse of notation quite annoying and confusing. I learned to use my own notation and translate everything into my own. But I gotta say that I find this particular abuse (first explicitly saying that $e^i$ is an element of the vector space $(\mathbb{R}^n)^$ and then using it to denote the function from $\mathbb R^n$, viewed as a manifold*, to $\mathbb{R}$ to be particularly egregious. – Deane Jan 04 '24 at 19:24
  • Why is this an abuse of notation? The dual space of a vector space, in this case of $\Bbb R^n$, is defined as the vector space of linear functionals from $\Bbb R^n$ to $\Bbb R$, so the $e^i$ are functions from the get-go. – some_math_guy Jan 04 '24 at 19:55