2

In elementary vectors class, we learn a nice formula for the dot product of two vectors,

$$\mathbf{a}\cdot\mathbf{b}=|\mathbf{a}||\mathbf{b}|\cos\theta,\tag{1}$$ where $\theta$ is the angle between the two vectors. Let's work in 2D to keep things simple.

In general relativity (and presumably in differential geometry), we learn that the scalar product of two vectors is given by

$$\mathbf{a}\cdot\mathbf{b}=a_\mu b^\mu=g_{\mu\nu}a^\nu b^\mu=g_{11}a^1b^1+g_{12}a^1b^2+g_{21}a^2b^1+g_{22}a^2b^2,\tag{2}$$

where $g_{\mu\nu}$ is the metric tensor. Both formulas agree when we use Cartesian coordinates. What about polar coordinates? For flat space in polar coordinates, the metric is

$$g_{\mu\nu}=\begin{bmatrix}1 & 0\\0 & r^2\end{bmatrix}.$$

(Right?) Writing our vectors component-wise in polar coordinates, $\mathbf{a}=\left(r_a,\theta_a\right),\,\mathbf{b}=(r_b,\theta_b),$ and naively applying the metric-based formula,

$$\mathbf{a}\cdot\mathbf{b}=r_ar_b+r^2\theta_a\theta_b.$$

Clearly this is incorrect, and inconsistent with $(1)$. As discussed in the following posts, 1, 2, the issue stems from confusing a point $(r,\theta),$ with a vector from the origin pointing to $(r,\theta).$ Writing our vectors as tuples $\mathbf{a}=\left(r_a,\theta_a\right)$ is incorrect.

Clearly, if we have two arbitrary vectors in 2D euclidean space, we can talk about the angle between them, and so the $(1)$ should still hold even if we are working in a different coordinate system.

How can $(2)$ be applied to vectors in a polar coordinate system, such that it reduces to $(1)$? How must we write our vector-components for $\mathbf{a}$ such that $(2)$ can be applied?

  • The inner product depends on the metric $g$. With other metrics, we use different notation such as $\langle a,b\rangle_g$ or $g_p(a,b)$ rather than $a\cdot b$ to emphasise this. – A. Goodier Dec 30 '19 at 20:54
  • 2
    The formulas $(1)$ and $(2)$ are both right (if interpreted properly), and the formula $\vec{a} \cdot \vec{b} = r_ar_b + r^2 \theta_a \theta_b$ is also correct, provided you interpret it correctly. The main issue you're having is not distinguishing a point in a manifold, and tangent vectors in the tangent space at that point. Also, your notation of $(r_a, \theta_a)$ is very misleading, because $r_a$ is NOT the length of the vector $\vec{a}$, and $\theta_a$ is NOT the "angle $\vec{a}$ makes with the positive $x$-axis". – peek-a-boo Dec 30 '19 at 21:05
  • @peek-a-boo Apologies for the misleading notation. What should $r_a$ and $\theta_a$ be if $(2)$ is to hold? Perhaps I could work backwards from $(1).$ – aRockStr Dec 30 '19 at 21:07
  • Your formula for $g_{\mu \nu}$ is also correct by the way. But the thing is, you're using a lot of shorthand notation, so unless you know PRECISELY what you mean, you're bound to make mistakes and get confused. – peek-a-boo Dec 30 '19 at 21:07
  • It is ok to write things like $r_a, \theta_a$, but you need to know exactly what you mean (I must admit, I also made an error when trying to figure out what was going on... and the reason was the notation). If I have time I'll try to write something up to hopefully clarify – peek-a-boo Dec 30 '19 at 21:08
  • @peek-a-boo Clearly I don't know exactly what I mean. I look forward to your response. – aRockStr Dec 30 '19 at 21:10
  • perhaps a better question would be: what is meant by the angle between two vectors? In familiar high-school geometry this is "obvious" because we probably didn't pay much attention to how things were defined. My point is that the angle $\alpha$ between two (non-zero) vectors $\xi, \eta$ is defined to be exactly that quantity such that $g(\xi, \eta) = \sqrt{g(\xi, \xi)} \sqrt{g(\eta, \eta)} \cdot \cos \alpha \equiv \lVert \xi \rVert \lVert \eta \rVert \cdot \cos \alpha$. – peek-a-boo Dec 30 '19 at 22:51
  • 1
    In other words, we DEFINE the angle $\alpha$ by the equation $\alpha := \arccos \left( \dfrac{g(\xi, \eta)}{\sqrt{g(\xi, \xi)} \sqrt{g(\eta, \eta)} }\right)$. So, in this sense, the answer to your question is that pretty much everything is equivalent by definition, and there is nothing to prove. This may not be very satisfying, so I'll write up an answer which highlights some things. But at the moment I'm not sure how better to phrase my answer, so it may seem slightly circular... perhaps someone else might have a better way of framing the argument/ though process – peek-a-boo Dec 30 '19 at 22:52
  • @peek-a-boo in my earlier comment about misleading notation, I actually meant the following. What should (what I've called) $r_a$ and $\theta_a$ represent, if not the length and angle? – aRockStr Dec 31 '19 at 02:03

1 Answers1

5

A "warning": there are a lot of subtle issues here, but in order to not write a textbook, I tried to limit myself to emphasize only some of the issues. Also, the organization definitely isn't perfect, but hopefully this clarifies some issues.


My first remark is more of a "sanity check". The notion of a metric tensor field $g$ is meant to generalize the familiar concept of dot product in Euclidean spaces to arbitrary smooth manifolds. So, obviously, if this generalization is to be useful in any reasonable sense, it better reproduce the old results. Second, changing coordinates is a completely "artificial" idea in the following sense: the naive definition of a vector is "an arrow with a certain magnitude and direction, emanating from a certain point".

Well, this "definition" can certainly be made more precise, but here's the key point: an arrow is an arrow! The arrow doesn't know anything about coordinates or components with respect to a basis, so it doesn't care whether you use cartesian/polar/elliptical/parabolic/hyperbolic or any other coordinate system. If you do the math properly, you should be describing the vector equally well in any coordinate system.


(I'll not write boldface simply for ease of typing). First, we recall the following definition:

Definition: A (Riemannian) metric tensor field on a smooth manifold $M$ is a map which assigns, in a "smooth" way, to each point $p \in M$ an inner product $g_p$ on the tangent space $T_pM$.

In your case, we shall specialize to the case $M = \Bbb{R}^2$, and when $g$ is the "standard metric". Now a point in $M$ is simply a tuple of numbers. In polar coordinates, we can specify a point using a radius $r$ and an angle $\theta$ as follows: $p = (r \cos \theta, r \sin \theta)$ (don't think of this as the cartesian components or "cartesian representation of polar coordinates" or anything else...this is simply a tuple of real numbers, and hence it is a point in $\Bbb{R}^2$. That's it.)

Now, let $a,b \in T_pM$ (i.e arrows which start at $p$). Now, note that $T_pM$ is a $2$-dimensional vector space, and hence it is spanned by two linearly independent vectors. There are several bases we can choose, but here are two of them: $\left\{\dfrac{\partial}{\partial x}\bigg|_p, \dfrac{\partial}{\partial y} \bigg|_p\right\}$ and $\left\{\dfrac{\partial}{\partial r}\bigg|_p, \dfrac{\partial}{\partial \theta}\bigg|_p\right\}$. What this means is that the vector $a$ can be written as \begin{align} a &= x_a \dfrac{\partial}{\partial x}\bigg|_p + y_a \dfrac{\partial}{\partial y}\bigg|_p \end{align} for some $x_a, y_a \in \Bbb{R}$. But there is nothing special with this basis, so we could just as well write \begin{align} a &= r_a \dfrac{\partial}{\partial r}\bigg|_p + \theta_a \dfrac{\partial}{\partial \theta}\bigg|_p \tag{$*$} \end{align} for some $r_a, \theta_a \in \Bbb{R}$. Note that the numbers $x_a, y_a, r_a, \theta_a$ are simply expansion coefficients when writing a vector relative to a basis! They by themselves do not have any physical/geometrical meaning it is the actual vectors $a,b, \dfrac{\partial}{\partial x}\bigg|_p...$ which have physical meaning, and it is the metric $g$ which contains all the geometric information about the space $M$ (if this point is unclear, you should revisit some linear algebra).

Now, let's figure out what $x_a$ and $y_a$ are. Use the fact that \begin{align} \dfrac{\partial}{\partial r}\bigg|_p &= \dfrac{\partial x}{\partial r}\bigg|_p \dfrac{\partial}{\partial x}\bigg|_p + \dfrac{\partial y}{\partial r}\bigg|_p\dfrac{\partial}{\partial y}\bigg|_p \\ &= \cos \theta \dfrac{\partial}{\partial x}\bigg|_p + \sin \theta \dfrac{\partial}{\partial y}\bigg|_p \end{align} and

\begin{align} \dfrac{\partial}{\partial \theta}\bigg|_p &= \dfrac{\partial x}{\partial \theta}\bigg|_p \dfrac{\partial}{\partial x}\bigg|_p + \dfrac{\partial y}{\partial \theta}\bigg|_p\dfrac{\partial}{\partial y}\bigg|_p \\ &= -r \sin \theta \dfrac{\partial}{\partial x}\bigg|_p + r\cos \theta \dfrac{\partial}{\partial y}\bigg|_p \end{align}

Plugging these equations into $(*)$ shows that \begin{align} a &= \left(r_a \cos \theta - r \theta_a \sin \theta \right) \dfrac{\partial}{\partial x} \bigg|_p + \left(r_a \sin \theta + r \theta_a \cos \theta \right) \dfrac{\partial}{\partial y} \bigg|_p \tag{$\ddot \smile$} \end{align} A similar equation holds for $b$ (just replace all $a$'s with $b$). Once again for emphasis: $r, \theta$ describe the point $p = (r \cos \theta, r \sin \theta) \in \Bbb{R}^2$ which is a distance $r$ from the origin, and an angle $\theta$ in the usual sense, whereas $r_a, \theta_a$ are simply the expansion coefficients of the vector $a \in T_pM$ relative to a specific basis. So, $r_a$ is NOT the length of the vector $a \in T_pM$, and $\theta_a$ is NOT the angle the vector $a$ makes!

So, what is the length of vector $a$? By definition, it is $\sqrt{g_p(a,a)}$ (the square root of the inner product of $a$ with itself). Now, \begin{align} \lVert a\rVert &= \sqrt{g_p(a,a)} \\ &= \sqrt{r_a^2 + r^2 \theta_a^2} \end{align} Similarly for $b$ (by the way, as a sanity check, verify for yourself that taking the sum of the squares of the expansion in $\ddot{\smile}$ is what is under the square root above).

What is the angle $\alpha_a$ which the vector $a$ makes with the positive $x$-axis? Well, as I mentioned in the comments, the notion of angle may seem slightly circular (but it isn't). But for the sake of routine computation, let's go with the flow. To avoid taking inverse trig functions, note that from the basis expansion $(\ddot{\smile})$, we have \begin{align} \cos(\alpha_a) &= \dfrac{r_a \cos \theta - r \theta_a \sin \theta}{\sqrt{r_a^2 + r^2 \theta_a^2}} \quad \text{and} \quad \sin(\alpha_a) = \dfrac{r_a \sin \theta + r \theta_a \cos \theta}{ \sqrt{r_a^2 + r^2 \theta_a^2}} \end{align} (what I'm actually doing here is using the fact that $T_pM = T_p \Bbb{R}^2$ has a canonical isomorphism with $\Bbb{R}^2$ as an inner product space, where the isomorphism is given by $\xi\dfrac{\partial}{\partial x} \bigg|_p + \eta\dfrac{\partial}{\partial y} \bigg|_p \mapsto (\xi,\eta)$.) A similar thing holds for the vector $b$.

So, finally, we can compute. Let $\alpha = \alpha_a - \alpha_b$; THIS is the angle between the vectors $a$ and $b$: \begin{align} \lVert a \rVert \lVert b \rVert \cos(\alpha_a - \alpha_b) &= \lVert a \rVert \lVert b \rVert \left( \cos \alpha_a \cos\alpha_b + \sin \alpha_a \sin \alpha_b \right) \\ & \dots \\ &= r_ar_b + r^2 \theta_a \theta_b \end{align} I have already given all the relevant formulas, so I leave it to you to plug in everything and verify the algebra in the steps $\dots$ I omitted.


So, to summarize, my claim is that really, there is actually nothing to be proven here, because the very notion of "angle between two vectors" is defined such that the inner product formula holds. But hopefully my answer above highlights some of the subtleties, most important of which is that $r_a, \theta_a$ (and $x_a, y_a$) are merely expansion coefficients with respect to a chosen basis, which means they tell you very roughly speaking "how much the vector $a$ points in the radial direction, and the angular direction", and that they are NOT the length of the vector and the angle it makes.

Also, it is very crucial to realize that these basis expansion coefficients $x_a, \dots, \theta_a$ by themselves do not have any meaning, it is only the vectors $a,b, \dfrac{\partial}{\partial x}\bigg|_p \dots, \dfrac{\partial}{\partial \theta}\bigg|_p$ which have meaning, and that it is the metric $g$ which encodes all the geometry of the manifold $M$ in question.

peek-a-boo
  • 55,725
  • 2
  • 45
  • 89
  • Thank you for a fantastic answer. The only source of confusion I have is your discussion of basis vectors, especially the derivation of $\frac{\partial}{\partial r}\left.\right\rvert_p$ and $\frac{\partial}{\partial \theta}\left.\right\rvert_p.$ The derivation makes sense to me intuitively if I think of the basis vectors as old-fashioned unit vectors like $\hat{r}$, but the notation is unfamiliar. I assume this is standard notation in differential geometry, but I have not studied the subject in any detail. Could you kindly direct me to some material which introduces this notation? – aRockStr Dec 31 '19 at 17:00
  • 1
    @aRockStr Thinking of derivations as elements of the tangent space is definitely one of the first few topics in differential geometry. For references, I suggest reading John Lee's book Introduction to Smooth Manifolds. Another equivalent definition of tangent space is as equivalence classes of smooth curves in the manifold. I think it's the most geometric and intuitive definition of a tangent vector, and an explanation of this can be found in Loomis and Sternberg's Advanced Calculus book (freely and legally available online) in Section 9.4. (But for a first introduction I suggest Lee's book) – peek-a-boo Dec 31 '19 at 17:11
  • 1
    One word of caution though... the vector $\dfrac{\partial}{\partial r} \bigg|p$ does indeed correspond to the $\hat{r}|_p$ vector which we're all familiar with. However, $\dfrac{\partial}{\partial \theta} \bigg|_p$ does not correspond to the usual $\hat{\theta}|_p$ vector, because $\hat{\theta}|_p$ is normalized, while $\dfrac{\partial}{\partial \theta} \bigg|_p$ is not. But the relationship between them is easy to establish. From your formula for the components of the metric in polar coordinates, we have $g{22} = r^2$. – peek-a-boo Dec 31 '19 at 17:16
  • What this actually means is $g_p\left( \dfrac{\partial}{\partial \theta} \bigg|_p, \dfrac{\partial}{\partial \theta} \bigg|_p\right) = r^2$, in other words, $\dfrac{\partial}{\partial \theta} \bigg|_p$ has length $\sqrt{r^2} = |r| = r$, so that $\dfrac{\partial}{\partial \theta} \bigg|_p = r \hat{\theta}|_p$ – peek-a-boo Dec 31 '19 at 17:17
  • Excellent point. This was also confusing because the equivalence of my equations $(1)$ and $(2)$ appears to rely crucially on $\frac{\partial}{\partial\theta}\left.\right\rvert_p\sim r$. Is this choice to not normalize critical for the equivalence of $(1)$ and $(2)$? – aRockStr Dec 31 '19 at 17:22
  • @aRockStr Yes and no, it depends how you define things. Consider the statement (similar to what you wrote) "the metric tensor $g_{\mu \nu}$ in polar coordinates has components \begin{pmatrix} 1 & 0 \0 & r^2 \end{pmatrix}". People very often say this, but this is an incomplete and vague statement if you analyze it carefully (however people usually know what they're talking about so they're too lazy to explain fully). The full and explicit statement is that "the matrix representation of the metric $g_p$ (at the point $p$) WITH RESPECT TO the ordered basis ${\partial_r, \partial_{\theta}}$" – peek-a-boo Dec 31 '19 at 17:28
  • 1
    (I'm being lazy in typing so I use $\partial_r$ to mean $\dfrac{\partial}{\partial r} \bigg|p$ etc) ... (cont) of $T_pM$ is $\begin{pmatrix} 1 & 0 \ 0 & r^2 \end{pmatrix}$. i.e what we're saying is that $g_p(\partial_r, \partial_r) = 1$ and $g_p(\partial_r, \partial{\theta}) = g_p(\partial_{\theta}, \partial_r) = 0$ and $g_p(\partial_{\theta}, \partial_{\theta}) = r^2$. Now, I refer you to my second paragraph where I put in bold that an arrow is an arrow, and that vectors do not care what coordinates/ basis you use. As long as you carry the computations carefully, you'll always get (cont) – peek-a-boo Dec 31 '19 at 17:33
  • (cont) the correct answer. Now, note that ${\partial_r, \partial_{\theta}}$ is simply one basis for $T_pM$. but, there are infinitely many others. In particular, we could use the orthonormal basis ${\partial_r, \partial_{\theta}/ r}$. With respect to this basis, the components of the metric tensor are simply $g_{\mu \nu} = \delta \mu \nu$ (because $g_p(\partial_{\theta}/ r, \partial_{\theta}/r) = 1/r^2 g_p(\partial_{\theta}, \partial_{\theta}) = 1$). But not what happens is that because we changed the basis vector from $\partial_{\theta} \to \partial_{\theta}/r$, the expansion coefficient – peek-a-boo Dec 31 '19 at 17:38
  • $\theta_a$ will also change by $\theta_a \to r \theta_a$. So, the point is that changing the basis vectors changes the expansion coefficients in such a way so as to keep the net result consistent. If you keep track of these changes carefully, then once again, you'll get the equivalence of (1) and (2). This is what I meant above when I said "yes and no". – peek-a-boo Dec 31 '19 at 17:41
  • 1
    @aRockStr By the way, since you tagged general relativity, I HIGHLY recommend going to youtube and watching Frederic Schuller's lectures on general relativity, especially the first few lectures, which carefully builds up all the mathematical machinery needed (Lecture $5$ in particular is about tangent spaces). (I've watched the lectures many times but I still learn new things everytime I watch his lectures). And be sure to pay very close attention to lecture $3$ on multilinear algebra! That is very very very very crucial – peek-a-boo Dec 31 '19 at 17:50
  • I love Frederic Schuller’s lectures! Will definitely check his GR lectures out. – aRockStr Dec 31 '19 at 17:52
  • I'm still struggling a little bit with the idea of this point $p = {r\cos\theta, r\sin\theta }$ not being a vector. It seems as though if we define the vector (define everything at the point $p$ specifically): $\partial_r = \partial_r x \partial_x + \partial_r y \partial_y = \cos\theta \hat{x} + \sin\theta \hat{y}$, then doesn't that imply that really $p = r\cos\theta \hat{x} + r\sin\theta \hat{y} = x \hat{x} + y \hat{y}$, and thus $p$ is a vector? – DilithiumMatrix Jan 05 '20 at 02:29
  • 1
    @DilithiumMatrix The term vector is often oversued. But, what I meant is the follwing: asking the question "is $\dots$ a vector?" is completely meaningless. What is a meaningful question is: "is $\dots$ an ELEMENT of a vector space?". If the answer to this question is yes, then we can call that object a vector in that vector space. But recall that a vector space is more than just a set! There is also the operation of addition and scalar multiplication defined. So, often when we say $(x,y)$ is a 2d vector or somehting to that effect, the more precise statement is that – peek-a-boo Jan 05 '20 at 14:48
  • 1
    "$(x,y) \in \Bbb{R}^2$ is an element of the vector space $(\Bbb{R}^2, +, \cdot)$, where $+$ and $\cdot$ are the "usual" operations of addition and scalar multiplication." Now, The objects $\partial_r|_p, \partial_x|_p...$ are all elements of the set $T_p \Bbb{R}^2$ (the tangent space to $\Bbb{R}^2$ at the point $p \in \Bbb{R}^2$). $T_p\Bbb{R}^2$ itself can be shown to have a vector space structure. But note that the tangent space is a completely different vector space from $\Bbb{R}^2$ (they are of course naturally isomorphic but still they are different spaces) – peek-a-boo Jan 05 '20 at 14:53
  • 1
    Now, $\Bbb{R}^n$ is a very nice space because it is one of the simplest examples of all sorts of spaces, such as topological space, metric space, vector space, Banach space, Hilbert space, smooth manifold. In my beginning paragraph though, what I really wanted to say is that throughout this discussion we shall consider $\Bbb{R}^2$ not as a vector space (I mean we could, but this extra structure is completely irrelevant to the discussion and also clouds our thinking) but rather to consider it as a smooth manifold. As an analogy (probably a bad one) consider the electromagnetic spectrum of light – peek-a-boo Jan 05 '20 at 14:57
  • 1
    (let's think purely classically of course) What is light? Well it is simply an electromagnetic field (electric fields and magnetic fields oscillating to produce waves). But of course there are many different parts of the spectrum: microwaves, radiowaves, infrared, visible light, UV light, X rays, Gamma rays. These are all of course the same thing: electric and magnetic fields oscillating. But they have very different behaviour (because of the frequency differnces). In other words, we have the same fundamental object: an electromagnetic field, but depending on its frequency, it has – peek-a-boo Jan 05 '20 at 15:00
  • 1
    completely different properties (eg. visibile light... obviously... lets us see, while $X$ rays are used in hospitals etc). So my point is that the same object can be viewed in several different forms/sides/perspectives. It's the same with $\Bbb{R}^2$. The fundamental object is the underlying set of points $\Bbb{R}^2$, which by definition is an ordered tuple of two real numbers ${(x,y)| x \in \Bbb{R}, y \in \Bbb{R}}$. But we can consider this same set in several different ways. For example, in most of high-school algebra, we've been thinking of $\Bbb{R}^2$ as a vector space, but my point is – peek-a-boo Jan 05 '20 at 15:04
  • 1
    that this is not the only aspect of $\Bbb{R}^2$ which we can consider. In this question, we want to consider it as a smooth manifold and in general a manifold is not a vector space... because you can't add points on a manifold (for example, imagine the unit circle in the plane. What does it even mean to add $(1,0)$ and $(-1,0)$? Should their sum be $(1,0) + (-1,0) = (0,0)$? No, because $(0,0)$ does not even lie on the circle). So, basically, I just wanted the reader in this example to consider $\Bbb{R}^2$ only as a smooth manifold, and not a vector space. – peek-a-boo Jan 05 '20 at 15:08
  • 1
    (If you really wanted to, you could consider it as a vector space as well, but that's just adding extra pieces of information, and it will completely cloud the concepts) If you haven't taken sufficiently many "pure-math" courses, what I say may sound like pedantry and a huge hassle. And I admit, having to change your thinking about something which you thought you knew very well since middle school is challenging, but conceptually things will (hopefully eventually) become clearer. btw I HIGHLY recommend you watch Frederic's Schuller's lecture $3$ on multilinear algebra (part of his GR course) – peek-a-boo Jan 05 '20 at 15:11
  • @peek-a-boo What is the difference between choosing to write $p={r\cos\theta,r\sin\theta},$ and $p={r,\theta}$? Is this an example of choosing different charts for $\mathbb{R}^2?$ – aRockStr Jan 06 '20 at 16:26
  • @aRockStr I assume you mean round brackets ()? because that's usually what we mean. Note that equality = is one of the most important symbols in math so it is important to not misuse it. If we take $p = (r \cos \theta, r \sin \theta)$ and $p=(r,\theta)$ literally. Then we have two equations. $r \cos \theta = r$ and $r\sin\theta = \theta$. If $r = 0$ then this immediately implies $\theta = 0$. If $r \neq 0$, there is no $\theta$ which satisfies both equations. but of course this is not what we mean! We do not want to solve a system of equations. An ordered tuple should be taken literally – peek-a-boo Jan 06 '20 at 16:59
  • as just an ordered list of two numbers. So, no, it has nothing (yet) to do with charts. It is just a bad abuse of notation where we mean one thing, and write something blatently different. So, what do we actually mean, and how do we write it properly? Fix a point $p \in \Bbb{R}^2$. This means $p$ is an ordered tuple $(p_1, p_2)$ where $p_1, p_2$ are real numbers. That's all it is! A priori, $p$ is just an ordered collection of numbers. Now how do charts and stuff fit into this discussion? On $\Bbb{R}^2$, we have a polar coordinate chart; formally this means we define – peek-a-boo Jan 06 '20 at 17:03
  • Sorry for not being clear. We assign to each point in $M$ a tuple of numbers. We may assign an arbitrary point $p$ the tuple $(x,y)$ using Cartesian coordinates. We may re-parameterize the point by writing instead $p=(r\cos\theta,r\sin\theta).$ This is still Cartesian, just rewritten in polar coordinates. We could have instead chosen to assign to each point in $M$ a tuple $(r,\theta).$ Is this an example of choosing a different chart for the manifold? I am referencing Schuller's lectures when I use the word chart. – aRockStr Jan 06 '20 at 17:05
  • Schuller's definition of chart is a collection $(U, \phi)$ where $U$ is an open subset of the manifold $M$ and $\phi : U \to \phi[U] \subset \Bbb{R}^n$ (for some $n$) is a homeomorphism. Let's for use the terms "coordinates" and "chart" synonymously. So what exactly is a cartesian coordinate system? If $M = \Bbb{R}^2$, then the cartesian coordinates/chart is the chart $(\Bbb{R}^2, I)$, where $I : M=\Bbb{R}^2 \to \Bbb{R}^2$ is the identity map. (this is a trivial chart, but we interpret the domain $\Bbb{R}^2$ as being a manifold, and the target $\Bbb{R}^2$ as a vector space) – peek-a-boo Jan 06 '20 at 17:10
  • Another example of a chart is $(U, \alpha)$ where $U = \Bbb{R}^2 \setminus {(x,0)| x \geq 0}$ is the open subset of $M$ and $\alpha^{-1}: (0, \infty) \times (0,2\pi) \to U$ defined as $\alpha^{-1}(r, \theta) = (r \cos \theta, r \sin \theta)$. You can check that $\alpha$ is indeed invertible on the respective domain, but it is easier to define $\alpha^{-1}$ rather than $\alpha$, because this requires a lot of cases (because $\arctan(y/x)$ only works in certain quadrants). – peek-a-boo Jan 06 '20 at 17:13
  • So, we call $(U, \alpha)$ the polar coordinate chart/polar coordinate system on the manifold $M = \Bbb{R}^2$. Now, given a point $p \in M = \Bbb{R}^2$, we can consider its image under two chart maps. We can consider $I(p) = p$ (which is a rather trivial image under the identity chart), or we can consider its image point $\alpha(p)$ under the polar coordinate chart. – peek-a-boo Jan 06 '20 at 17:16
  • Sorry, I think I misread what you wrote hence I deleted some comments. Yes, you're right, this is an example of using different charts in an overlapping chart-domain. So, yes, given $p \in U$ (the intersection of chart domains), there exist UNIQUE $(x,y) \in I[U] = U$ such that $p = I^{-1}(x,y)$, and there exists unique $(r, \theta) \in \alpha[U]=(0, \infty) \times (0, 2\pi)$ such that $p = \alpha^{-1}(r, \theta)$. So, we have $p=I^{-1}(x,y)=\alpha^{-1}(r,\theta)$. By now applying the definitions of $I^{-1}$ and $\alpha^{-1}$, we finally find that $p=(x,y)=(r \cos\theta,r\sin\theta)$. – peek-a-boo Jan 06 '20 at 17:31
  • 1
    But I would simply avoid statements like "this is still cartesian written in terms of polar coordinates". Personally, I prefer to (as much as possible) only use the language of chart domains, chart maps, the inverse of chart maps etc. i.e I like the chart and atlas terminology, because if you systematically use this, the definitions are so clear, it is almost impossible to be confused by it. Often our ideas are very good and valuable, but the language/sentences used to express it is confusing...(this is how I view a lot of introductory physics: so many great ideas expressed not as well) – peek-a-boo Jan 06 '20 at 17:37