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I was doing exercise 8 from do Carmo's Riemannian geometry and I stumbled upon the definition of gradient given.

Let $M$ be a Riemannian manifold... $f \in \mathcal{D}(M)$ .. the gradient of $f$ as a vector field $\text{grad} \; f$ on $M$ defined by $$ \langle \text{grad} \; f, v \rangle = df_p(v) \;\; p \in M, v \in T_pM \;\;\;\;\; (1) $$

here $\langle \cdot , \cdot\rangle$ is the Riemannian metric on $M$ and $f$ is a differentiable function on $M$. No the Riemannian metric is a bilinear map $$\langle \cdot,\cdot \rangle : T_p M \times T_p M \to \mathbb{R}$$ but the differential $df_p$ is a map between tangent spaces, namely $$ df_p : T_p M \to T_{f(p)} \mathbb{R} \cong \mathbb{R} $$

So in a nutshell I'm confused about the equality in $(1)$ because the lhs is a scalar in the field while the rhs is vector, though isomorphic to the scalar field. This definition actually makes a bit tricky for me to understand how to do the exercises, because any of the computations I do give me equalities that don't really make sense.

Can you clarify how the gradient is actually defined? I also own Tu's Differential Geometry, but I don't see these definitions (I'm kind of reading the two in parallel).

user8469759
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  • I disagree, do Carmo defines $df_p$ as a differential which is a map between two tangent spaces. If I use differential forms notation however $df_p$ is a cotangent vector, and hence a functional and so your argument works. Unless he meant differential of $f$ at $p$ I don't see how the math can work. – user8469759 Jun 28 '20 at 16:32
  • I see what you're getting at, I misunderstood you and my previous comment didn't address your point of confusion. I'll delete it. – Ninad Munshi Jun 28 '20 at 16:34
  • It's fine, as usual do Carmo's notation isn't the best despite the great content. – user8469759 Jun 28 '20 at 16:35
  • The definition do Carmo has agrees with the generalization for maps $f:M\to N$. I think this has to do with the fact that the gradient of scalar in some ways is special, a derivative of an arbitary map would not fit into the metric that way. – Ninad Munshi Jun 28 '20 at 16:40
  • Which definition? the differential? the gradient? – user8469759 Jun 28 '20 at 16:42
  • Yes, a differential of a function $f:M\to N$ is a map $df:M_p \to N_{f(p)}$ – Ninad Munshi Jun 28 '20 at 16:49
  • Is there a different reference you could mention for me to look up for the definition of gradient? – user8469759 Jun 28 '20 at 16:50
  • I first learned it from Carroll. There he talks about an object called the covariant derivative - it is the same thing as the gradient when the function is a scalar. – Ninad Munshi Jun 28 '20 at 16:54
  • I'm not sure that I understand the question. The equation says that you can define the gradient as the (unique) vector field that satisfies $\langle \text{grad } f, v \rangle = df_p(v)$ for all vectors $v$ and all points $p$. On the left you have the inner product on two vectors which returns a number. On the right you have the evaluation of a covector on a vector which also returns a number. Essentially a Riemannian metric provides (pointwise) a map $T_pM \cong T_p^\ast M$ (this is a linear algebra fact which is exactly the fact stated) and under this isomorphism, you can identify $df_p$... – Osama Ghani Jun 28 '20 at 17:20
  • ...with a vector that we will call $(\text{grad }f)_p$. Gluing all the $(\text{grad }f)_p$ pointwise gives us a vector field that we define as $\text{grad }f$ – Osama Ghani Jun 28 '20 at 17:21
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    I'm sure you noticed this but the equation in Euclidean space (or even in local coordinates) says exactly that $D_vf = \nabla f \cdot v$ where $D_v$ is the directional derivative in the direction of $v$. – Osama Ghani Jun 28 '20 at 17:30
  • @Osama the lhs of (1) is a scalar value but the right isn't (according to the definition give of differential by doCarmo this would return a vector and not a scalar value). – user8469759 Jun 28 '20 at 18:40
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    $df_p$ is a map from $T_pM \to \mathbb{R}$ (I know you wrote $T_{f(p)}\mathbb{R} \cong \mathbb{R}$ but not only are they isomorphic, they are canonically so, so you can consider $df_p$ as a map from $T_pM \to \mathbb{R}$ without any choices). Since $v \in T_pM$, $df_p(v)$ is just the evaluation sending $v$ to the element in $\mathbb{R}$ so it is a scalar. – Osama Ghani Jun 28 '20 at 18:56
  • The key element is that $df_p$ is without a doubt a covector (you need to convince yourself that this is true without any choices). The metric pointwise provides a unique isomorphism between vectors and covectors, so mapping $df_p$ to its vector counterpart gives a vector everywhere i.e. a vector field. – Osama Ghani Jun 28 '20 at 19:00
  • You're writing the definition of cotangent vector. Which would definitely make sense to me. – user8469759 Jun 28 '20 at 19:01
  • Right, so I guess to solve the confusion we just note that $df_p$ is a covector because $T_{f(p)}\mathbb{R} \cong \mathbb{R}$ canonically and then from there it's just linear algebra of non-degenerate forms. – Osama Ghani Jun 28 '20 at 19:04
  • I guess there's no other way to look at that equation then. – user8469759 Jun 28 '20 at 19:51
  • I can confirm assuming $df_p$ is a differential form part of the exercise becomes a piece of cake. – user8469759 Jun 29 '20 at 09:42

1 Answers1

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It's natural to have some confusion about these things. There are many similar things that come up in differential geometry and smooth manifold theory (and even much of other parts of math) where we take shortcuts or "make identifications" that make our lives easier once we understand their meaning, but can make the uninitiated's life needlessly difficult when it comes time to write proofs and ask if we really understand the shortcuts we take.

For any smooth map $f\colon M\to \mathbb R$ there is the global differential map, $df\colon TM\to T\mathbb R$ defined by $$ df(p,v) = (f(p),df_p(v)), $$ and the vector $df_p(v)$ acts on smooth functions $h$ on $\mathbb R$ by $df_p(v)(h) = v(h\circ f)$. For fixed $p\in M$, the map $df_p\colon T_pM\to T_{f(p)}\mathbb R$ is the differential of $\pmb f$ at $\pmb p$. For any point $q\in\mathbb R$, there is a canonical vector space isomorphism $L_q\colon \mathbb R\cong T_{q}\mathbb R$ defined by $$ L_q(v) = v\frac{d}{dt}\bigg|_q, $$ i.e., sending the number $v$ to the directional derivative with respect to the "vector" $v$ (which is of course merely multiplication of the number $v$ with the usual derivative operator for smooth functions on $\mathbb R$.) We can compose $L_{f(p)}$ with $df_p$ to get a linear map $$ \widetilde{df_p} \equiv L_{f(p)}\circ df_p\colon T_pM\to \mathbb R. $$ Local coordinates $(x^1,\dots,x^n)$ near $p$, give a basis $\partial_{x^1}|_p,\dots,\partial_{x^n}|_p$ for $T_pM$, with respect to which, the linear map $\widetilde{df_p}$ is simply the row vector $$ \begin{bmatrix} \displaystyle\frac{\partial f}{\partial x^1}(p) & \dotsb & \displaystyle\frac{\partial f}{\partial x^n}(p) \end{bmatrix}. $$ For $f\colon M\to\mathbb R$, we also have a well-defined covector field $df\colon M\to T^*M$. In local coordinates $(x^1,\dots,x^n)$ near $p$, we can express the covector field $df$ in terms of the local coframe $dx^1,\dots,dx^n$ (dual frame of $\partial_{x^1},\dots,\partial_{x^n}$) as $$ df = \sum_i\frac{\partial f}{\partial x^i}\,dx^i. $$ At each point $p$, we thus have a covector $df_p\colon T_pM\to \mathbb R$ expressed in terms of the basis $dx^1|_p,\dots,dx^n|_p$ by $$ df_p = \frac{\partial f}{\partial x^i}(p)\,dx^i|_p. $$ so with respect to the basis $dx^1|_p,\dots,dx^n|_p$, $df_p\in T_p^*M$ can be expressed as the row vector $$ \begin{bmatrix} \displaystyle\frac{\partial f}{\partial x^1}(p) & \dotsb & \displaystyle\frac{\partial f}{\partial x^n}(p) \end{bmatrix}. $$ So really, $df_p$ the differential and $df_p$ the covector are literally the same object up to the canonical isomorphism $L_{f(p)}$. I think that we remind ourselves of this isomorphism $L$ maybe the first few times we identify the differential $df_p$ and the covector $df_p$, but we will drop it entirely after we get used to it. With more experience, one comes to appreciate the "intent of the law" rather than strictly follow the "letter of the law," and the interpretations we make are ultimately dictated by the purposes we have in mind.

That said, if one wants to define $\mathrm{grad}f$ "right," without making identifications, then I'd say you need to be comfortable with covector fields, and the musical isomorphism $(\cdot)^\sharp\colon T^*M\cong TM$ that the metric $g$ gives us, so we can do things properly and say simply and without ambiguity that $\mathrm{grad} f = (df)^\sharp$.

Alex Ortiz
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  • I think my source of confusion lies in the fact that doCarmo defines the gradient as an operator acting on some function. Vectors and covector might be isomorphic but this only means you can associate to each vector a unique covector and viceversa linearly but they're not overall the same thing. The musical isomorphism is indeed a construction to provide a vector given a covector. I understand the point of being the same, but they're actually not. – user8469759 Aug 22 '20 at 09:00