Directional derivatives and non-unit vectors

In summary, the conversation discusses the definition of directional derivatives for smooth real-valued functions on open sets. It is shown that the directional derivative is a linear function of the direction vector, and its value may depend on both direction and speed of travel. There is a distinction between when the direction vector is required to have unit length and when it is not, as the former allows for a clearer interpretation of the rate of change of the function.
  • #1
Rasalhague
1,387
2
Lee: Introduction to Smooth Manifolds, definition A.18:

Now suppose [itex]f : U \rightarrow \mathbb{R}[/itex] is a smooth real-valued function on an open set [itex]U \subseteq R^n[/itex], and [itex]a \in U[/itex]. For any vector [itex]v \in \mathbb{R}^n[/itex], we defi ne the directional derivative of [itex]f[/itex] in the direction [itex]v[/itex] at [itex]a[/itex] to be the number

[tex]D_vf(a)=\frac{\mathrm{d} }{\mathrm{d} t} \bigg|_0 f(a+vt). \enspace\enspace(A.18)[/tex]

(This de nition makes sense for any vector v; we do not require v to be a unit vector as one sometimes does in elementary calculus.)

He then shows, by the chain rule, that

[tex]D_vf(a_0)= \sum_{i=1}^n v^i \frac{\partial }{\partial x^i}f(a) \bigg|_{a_0}[/tex]

It seems to me, though, that this number depends not only on the direction of [itex]v[/itex] but also on its length. For example if [itex]f(x,y,z) = xyz[/itex], and [itex]v=(1,0,0)[/itex], then

[tex]D_vf(2,3,4) = \begin{pmatrix}yz & xz & xy
\end{pmatrix} \bigg|_{(2,3,4)+0(1,0,0)} \begin{pmatrix}1\\0\\0\end{pmatrix} = 12.[/tex]

But if [itex]w=(2,0,0)[/itex], then the directional derivative of [itex]f[/itex] "in the direction of [itex]w[/itex]" (which is the same direction as the direction of [itex]v[/itex]) will be

[tex]D_vf(2,3,4) = \begin{pmatrix}yz & xz & xy
\end{pmatrix} \bigg|_{(2,3,4)+0(1,0,0)} \begin{pmatrix}2\\0\\0\end{pmatrix} = 24.[/tex]

So how does the definition make sense for any vector? What am I missing here?
 
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  • #2
Rasalhague said:
It seems to me, though, that this number depends not only on the direction of [itex]v[/itex] but also on its length.
...
So how does the definition make sense for any vector? What am I missing here?
It's not that you're missing something, but you're adding in something incorrect -- the hypothesis that the directional derivative doesn't depend on length.

The directional derivative is, in fact, a linear function of v.
 
  • #3
Perhaps an application would be clarify things. Suppose you are moving along a surface measuring air temperature. If you require that the direction vector, v, be a unit vector "one sometimes does in elementary calculus" (that is, you always move with speed 1), then the measured rate of change of temperature will depend only on the direction . But since, here, he is NOT requireing that v be the unit vector, it will depend on both direction and speed of travel.
 
  • #4
Ah, okay. Thanks Hurkyl. So is the following correct?

When the vector is required to have unit length, the number at called "the directional derivative of f at a in the direction of v" lives up to its name directional, and tells us the rate of change of the function in that direction; otherwise, it's just a number that could be any number, and so doesn't, by itself, tell us anything about f. Only if we know the function used to produce this number, or know the length of the vector used to produce it, can we tell anything about the behaviour of f.

Ah & aha, thanks HallsofIvy; I was just previewing this before posting and saw your example. Yes, the relaxation of the unit length requirement seems less arbitrary now.
 

1. What is a directional derivative?

A directional derivative is a measure of the rate of change of a function in a specific direction. It gives the slope of the function in the direction of a given vector.

2. How is a directional derivative calculated?

A directional derivative is calculated using the dot product between the gradient vector of the function and the unit vector in the specified direction.

3. What is the significance of non-unit vectors in directional derivatives?

Non-unit vectors allow us to calculate the directional derivative in any direction, not just unit vectors. This can be useful in real-world applications where the direction of change may not align with a unit vector.

4. Can the directional derivative be negative?

Yes, the directional derivative can be negative. This indicates that the function is decreasing in the specified direction.

5. How is the direction of maximum change determined using directional derivatives?

The direction of maximum change is determined by finding the unit vector that maximizes the directional derivative. This can be done by taking the gradient vector of the function and normalizing it to a unit vector.

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