Interpretation of the Gradient Vector?

In summary, the conversation discusses the representation of the gradient of a scalar function as a vector field and the confusion surrounding its transformation under coordinate changes. It is clarified that the gradient is actually a 1-form, but in Riemannian manifolds, it can be seen as a section of the tangent bundle. The concept of the musical isomorphism is also mentioned, where the gradient can be thought of as a section of the cotangent bundle. In algebraic geometry, the gradient is seen as an A-derivation map.
  • #1
Mandelbroth
611
24
I've always thought of the gradient of a scalar function (id est, ##\nabla\varphi##) as being a vector field. However, I started thinking about it just now in terms of transformation with respect to coordinate changes, and I noticed that the gradient transforms covariantly. Thus, shouldn't the gradient be represented with a row vector?

I don't know why this is confusing for me. After looking through a couple websites, I saw that there were some who said "yes" and some who said "no," so I don't know what to think.
 
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  • #2
It's technically not a vector field but rather a 1-form. The components ##\partial_{i}f## transform as those of a 1-form so that's a simple reason for why i.e. ##\partial_{i'}f = \frac{\partial x^{i}}{\partial x^{i'}}\partial_{i}f##. A slightly more general explanation is as follows: https://www.physicsforums.com/showpost.php?p=4374094&postcount=18
 
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  • #3
WannabeNewton said:
It's technically not a vector field but rather a 1-form.
I thought that ##\nabla\varphi=(d\varphi)^\sharp##. Wouldn't that imply that the gradient is a vector field? Or, am I just confused as to the effects of raising the index? Or, is the equation not valid?
 
  • #4
The musical isomorphism is trivial for ##\mathbb{R}^{n}## so it really doesn't matter but if you look at the gradient component wise as ##\partial_{i}f## then it's a 1-form.
 
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  • #5
The gradient of a scalar field is actually a section of the cotangent bundle of the manifold. However, if we are given a metric tensor, then we have a natural morphism between the cotangent bundle and the tangent bundle. As such, in Riemannian manifolds, the gradient can be seen as a section of the tangent bundle. When no natural metric is given, it can not be seen as such.

In algebraic geometry, we can see the gradient as an ##A##-derivation map ##d:B\rightarrow \Omega_{B\A}##, where ##A##, ##B## is an ##A##-algebra and where ##\Omega_{B\setminus A}## is the module of relative differential forms. We can further generalize this by defining ##\Omega_{X\setminus Y}## as a quasicoherent module of sheafs, when ##X## and ##Y## are schemes.
 
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  • #6
Mandelbroth said:
I thought that ##\nabla\varphi=(d\varphi)^\sharp##. Wouldn't that imply that the gradient is a vector field? Or, am I just confused as to the effects of raising the index? Or, is the equation not valid?
Let me just add on a bit to what I said before. We can think of ##\partial_{a}## as a derivative operator on ##\mathbb{R}^{n}## that sends (n,m) tensor fields to (n,m+1) tensor fields (I'm sure you've seen this before in a more general setting!) so in this sense it sends a scalar field (i.e. a (0,0) tensor field) to a 1-form (i.e. a (0,1) tensor field); now let's say we have a metric tensor ##g_{ab}## then we can define the associated vector field ##\partial^{a}\varphi = g^{ab}\partial_{b}\varphi## which is just the musical isomorphism. In ##\mathbb{R}^{n}## we usually take ##g_{ab} = \delta_{ab}## so if ##\partial_{i}\varphi## are the components then ##\partial^{i}\varphi = \delta^{ij}\partial_{j}\varphi = \delta^{i1}\partial_{1}\varphi + \delta^{i2}\partial_{2}\varphi + \delta^{i3}\partial_{3}\varphi## so ##\partial^{1}\varphi = \partial_{1}\varphi## etc.
 

1. What is the gradient vector and why is it important in science?

The gradient vector is a mathematical concept that represents the direction and magnitude of the steepest increase of a function. It is important in science because it helps us understand the rate of change of a function in different directions, which is crucial in many scientific fields such as physics, engineering, and economics.

2. What is the difference between the gradient vector and the directional derivative?

The gradient vector is a vector that contains the partial derivatives of a multivariable function, while the directional derivative is a measure of how a function changes in a specific direction. The gradient vector gives us information about the overall rate of change of a function, while the directional derivative gives us information about the rate of change in a specific direction.

3. How is the gradient vector used in optimization problems?

The gradient vector is often used in optimization problems to find the maximum or minimum values of a function. By setting the gradient vector equal to zero, we can find critical points where the function is either increasing or decreasing the most, which can help us determine the maximum or minimum values of the function.

4. Can the gradient vector be used in any type of function?

Yes, the gradient vector can be used in any type of function, as long as the function is differentiable. This means that the function is continuous and has well-defined derivatives at every point. However, in some cases, the gradient vector may not exist at certain points, such as at discontinuities or sharp corners.

5. How does the gradient vector relate to the concept of slope?

The gradient vector is closely related to the concept of slope. In one-variable functions, the gradient vector is equivalent to the derivative of the function, which represents the slope of the tangent line at a specific point. In multivariable functions, the gradient vector represents the direction and magnitude of the steepest increase of the function, which is similar to the concept of slope in one variable.

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