Directional Derivative: Why Must Vector Be Unit Vector?

Click For Summary

Discussion Overview

The discussion centers around the concept of the directional derivative in multivariable calculus, specifically questioning why the direction vector must be a unit vector. Participants explore the implications of using unit versus non-unit vectors in the context of directional derivatives, touching on theoretical and practical aspects.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants assert that the directional derivative is defined as ##D_{\vec{v}} f = \nabla f \cdot \vec{v}## and question the necessity of ##\vec{v}## being a unit vector.
  • Others argue that ##\vec{v}## does not need to be a unit vector, suggesting that as long as measurements are not expressed in coordinates, any length is acceptable.
  • One participant elaborates that using a non-unit vector would affect the comparison of directional derivatives at different points, indicating that the length of ##\vec{v}## should be considered depending on the context of the calculation.
  • Another participant mentions that the limit definition of the directional derivative does not require ##\vec{v}## to be a unit vector, proposing that the formulation can be adjusted accordingly.
  • Some participants highlight that using unit vectors aligns well with physics problems, which often emphasize measurement.
  • One participant points out that if ##\vec{v}## is chosen in a specific direction (e.g., ##x## or ##y##), it must be a unit vector to obtain the respective directional derivatives ##f_x## and ##f_y##.
  • Another participant explains that scaling the vector affects the speed of the function along the curve, indicating that the directional derivative operator is linear.

Areas of Agreement / Disagreement

Participants express differing views on whether the direction vector must be a unit vector, with some asserting it is necessary while others maintain it is not. The discussion remains unresolved, with multiple competing perspectives presented.

Contextual Notes

Participants note that the implications of using unit versus non-unit vectors depend on the specific context of the problem being addressed, and that the definition of directional derivatives can be adapted based on the chosen vector.

Mr Davis 97
Messages
1,461
Reaction score
44
I know that ##D_{\vec{v}} f = \nabla f \cdot \vec{v}## is the directional derivative. My question is why must the vector ##\vec{v}## be a unit vector? I am sure there is an obvious answer, but my book doesn't really explain it.
 
Physics news on Phys.org
Mr Davis 97 said:
I know that ##D_{\vec{v}} f = \nabla f \cdot \vec{v}## is the directional derivative. My question is why must the vector ##\vec{v}## be a unit vector? I am sure there is an obvious answer, but my book doesn't really explain it.
It doesn't have to be a unit vector. As long as you don't measure things or express them in coordinates, it can be of any length. If you want to measure different things by the same ruler, then you can and should divide it by its length, but the definition doesn't require it. Most authors, however, use the unit vector in their definition.
 
fresh_42 said:
It doesn't have to be a unit vector. As long as you don't measure things or express them in coordinates, it can be of any length. If you want to measure different things by the same ruler, then you can and should divide it by its length, but the definition doesn't require it. Most authors, however, use the unit vector in their definition.
Can you elaborate on what would go wrong if I did the multiplication with a non-unit vector? What about ##\vec{v}## being a unit vector allows me to measure things correctly?
 
If you have a function ##f : \mathbb{R}^n \rightarrow \mathbb{R}## then ##\nabla_v f(x) = \nabla f(x) \cdot v## which gives you a number that depends on the length of ##v##. So if you want to compare the behavior of the directional derivative at different points ##x## and ##y## in a certain direction ##v##, then ##v## should always be the same (of any length). And if you want to compare the behavior at the same point ##x## in different directions ##v## and ##w##, then you should also have the length of them in mind. Depending on what you want to calculate, they don't necessarily have to be of the same length, e.g. the velocity of driving through a curve. But if you only want to know the rate of change, then this is a division by their length.

It's as always: it depends on what you want to do. Personally I don't see any advantage in the restriction to unit vectors, the limit
$$
\nabla_v f(x) = \lim_{h \to 0} \frac{f(x+hv)-f(x)}{h}
$$
doesn't need it.

One can write the entire thing as ##\nabla_v f(x) = f(x+v) - f(x) - r(v)## where the remainder ##r(v)## means: vanishes faster than linear when approaching zero. So instead of saying "faster than linear" one can write ##r(\frac{v}{||v||}) \rightarrow 0## where the "faster than linear" aspect is divided beforehand.
 
Last edited:
  • Like
Likes   Reactions: jedishrfu
The unit vector approach fits well with physics problems which are always focused on measurement of things and since we all know physics is the most important of sciences that pretty much sums it up.
 
  • Like
Likes   Reactions: vela
jedishrfu said:
The unit vector approach fits well with physics problems which are always focused on measurement of things and since we all know physics is the most important of sciences that pretty much sums it up.

That's dangerous thing to say in the math section :P
 
  • Like
Likes   Reactions: jedishrfu
If you choose ##\vec v## to be in the ##x## or ##y## direction, and you want the respective directional derivatives to be ##f_x## and ##f_y##, you need ##\vec v## to be a unit vector. To scale it otherwise in a calculus class seems to me to be very unhelpful.
 
It is necessary. Furthermore, said formula is valid when f is differentiable at the point.
 
No, it doesn't need to be a unit vector. As you know, given ##f: \mathbb{R}^m \rightarrow \mathbb{R}^n##, then we define its directional derivative at the point ##a \in \mathbb{R}^m## in the direction ##v \in \mathbb{R}^m## by ##D_v f = \frac{d}{dt} \vert_{t = 0} f(a + tv) = \lim\limits_{t \rightarrow 0} \frac{f(a+tv) - f(a)}{t}##, provided this limit exists. As you can see, we are differentiating ##f## along the curve ##a + tv##. If, for example, you decided you wanted to take the directional derivative in the direction of ##2v## then you would have ##D_{2v}f = \frac{d}{dt}\vert_{t=0} f(a + 2tv)##, so we are differentiating the function along the curve ##a + 2tv##. Now what's the difference between the curve ##a + tv## and the curve ##a + 2tv##? The image of both curves are the same, but their velocities are different. The first curve has a velocity of ##v## while the second curve has a velocity of ##2v## (just differentiate both curves with respect to ##t##). So when we take the directional derivative of ##f## along the second curve, you can think of the function ##f## traveling twice as fast as it would along the first curve.

In fact, the directional derivative operator is linear, so you immediately have ##D_{2v}f = 2D_v f##, which shows that scaling ##v## just scales the directional derivative. So if ##f## travels along the curve ##a + 2tv##, then it is traveling twice as fast as it would along the curve ##a + tv##, which is why we have ##D_{2v} f = 2D_v f##.
 
  • Like
Likes   Reactions: fresh_42

Similar threads

  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 16 ·
Replies
16
Views
3K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 8 ·
Replies
8
Views
758
  • · Replies 2 ·
Replies
2
Views
1K