Directional Derivative and unit vectors

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Discussion Overview

The discussion centers around the concept of the directional derivative in calculus, specifically addressing the implications of using a unit vector versus a non-unit vector in its calculation. Participants explore the mathematical formulation and interpretation of the directional derivative, including its relationship to gradients and approximations.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant suggests that if a unit vector is not used in calculating the directional derivative, the result will be multiplied by a scalar, indicating that the directional derivative should be calculated using a unit vector.
  • Another participant provides a first-order approximation for the directional derivative, drawing a parallel to single-variable calculus and suggesting a formula involving the gradient and a vector.
  • A different participant reiterates the importance of using a unit vector, explaining that the directional derivative represents the projection of a function's gradient along a direction and that using a non-unit vector results in scaling the derivative by the vector's length.
  • One participant proposes a formula for adjusting the directional derivative when a non-unit vector is used, suggesting that dividing by the vector's length yields the same result as using a unit vector from the outset.
  • Another participant comments on the notation used in the formulas, clarifying the correct symbols for gradient and partial derivatives in LaTeX.

Areas of Agreement / Disagreement

Participants generally agree on the necessity of using a unit vector for calculating the directional derivative, but there are variations in the explanations and formulations presented. The discussion includes corrections and refinements of earlier claims, indicating some level of contention regarding the details.

Contextual Notes

Some participants reference Taylor expansions and approximations, which may imply underlying assumptions about the continuity and differentiability of the functions involved. The discussion does not resolve the nuances of these mathematical interpretations.

Gott_ist_tot
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What happens if a unit vector is not used in calculating the directional derivative. From when I worked it out the directional derivative is multiplied by a scalar if a unit vector is not used. So I gather that the directional derivative must be calculated by a unit vector. Because it is still calculating a rate of change and a rate of change multiplied by a number is not the same answer. I was just curious if my answer was on the right track or way off. Thanks.
 
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Here's a more useful interpretation IMO.

In the same way as [itex]f(x+a) \approx f(x) + af'(x)[/itex] (sometimes known as Euler's approximation) in single variable calculus,

[tex]f(\vec{r}+\vec{A}) \approx f(\vec{r}) + \nabla f (\vec{r}) \cdot \vec{A}[/tex]

gives a first order approximation of the value of f(r+A).
 
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Gott_ist_tot said:
What happens if a unit vector is not used in calculating the directional derivative. From when I worked it out the directional derivative is multiplied by a scalar if a unit vector is not used. So I gather that the directional derivative must be calculated by a unit vector. Because it is still calculating a rate of change and a rate of change multiplied by a number is not the same answer. I was just curious if my answer was on the right track or way off. Thanks.

A directional derivative is just the projection of a function's gradient along some direction. To get the projection of a vector in a particular direction you take the dot product of the vector with a unit vector in the direction you're looking at.

If you don't use a unit vector, then your directional derivative will be multiplied by the length of the vector that you do use.

quasar, I think you need to fix a few errors in your post

[tex]f(x+a) \approx f(x) + af^\prime (x)[/tex]

[tex]f(\vec{r}+\vec{A}) \approx f(\vec{r})+\vec{A} \cdot \nabla f(\vec{r})[/tex]

(note, this is basically just taking a Taylor expansion up to first-order)
 
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Data said:
If you don't use a unit vector, then your directional derivative will be multiplied by the length of the vector that you do use.

So, in other words, if your original vector wasn't a unit vector, just divide your answer by its length:
[tex]D_{\vec{v}}f= \frac{\vec{v}\cdot\del f}{|\vec{v}|}[/itex]<br /> <br /> Of course, since [itex]\frac{\vec{v}}{|\vec{v}|}[/itex] <b>is</b> the unit vector in the direction of [itex]\vec{v}[/itex], that is exactly the same as reducing to a unit vector in the first place.[/tex]
 
Strangely, \del doesn't do anything in Latex afaik. [itex]\nabla[/itex] is \nabla and [itex]\partial[/itex] is \partial. I'm sure everyone knew, but here it is anyway,

[tex]D_{\vec{v}}f= \frac{\vec{v}\cdot\nabla f}{|\vec{v}|}[/tex]
 

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