Dot Product Differentiation question

In summary, the directional derivative is a specific type of derivative which is used when you are looking at how a vector changes when you add 1 but in a different direction.
  • #1
n0_3sc
243
1
If I differentiate two unit vectors, one with respect to the other, would it just be the dot product between the two vectors (namely the cosine of the angle between them)?

I don't understand the physical meaning of the result...
 
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  • #2
Do you mean, given [itex]\vec{v}[/itex] and [itex]\vec{u}[/itex], differentiate one with respect to the other? That makes no more sense than differentiating one number with respect to another- you have to differentiate a function. If [itex]\vec{f}(\vec{v})[/itex] is a vector valued function of the vector [itex]\vec{v}[/itex] then you could differentiate [itex]\vec{f}[/itex] with respect to [itex]\vec{v}[/itex]. If [itex]\vec{v}[/itex] is in Rn and [itex]\vec{f}(\vec{v})[/itex] is in Rm then the derivative would be a linear transformation from Rn to Rm, representable by an n by m matrix.
 
  • #3
If you treat the first unit vector as just a vector with a given direction and magnitude of 1, then differentiating it with respect to the other unit vector really means how it's magnitude changes if you add 1 but in a different direction. This is equivalent to a so called directional derivative (see http://mathworld.wolfram.com/DirectionalDerivative.html, (6)). In this extreme case you differentiate the first vector by its own unit vector, which becomes 1. then dot product the result with the second unit vector. And yes, it becomes the dot product of the two unit vectors, namely the cosine of their angles.
 
  • #4
ylo010 said:
If you treat the first unit vector as just a vector with a given direction and magnitude of 1, then differentiating it with respect to the other unit vector really means how it's magnitude changes if you add 1 but in a different direction. This is equivalent to a so called directional derivative (see http://mathworld.wolfram.com/DirectionalDerivative.html, (6)). In this extreme case you differentiate the first vector by its own unit vector, which becomes 1. then dot product the result with the second unit vector. And yes, it becomes the dot product of the two unit vectors, namely the cosine of their angles.
No. "How its magnitude changes if you add 1 but in a different direction" is NOT the directional derivative. The wolfram site you give makes it very clear, as I said before, that you cannot differentiate a "vector", you must differentiate a "vector function".

It is true that if f(x,y,z) (f is a numerical valued function, not a vector function) and [itex]\vec{v}[/itex] a given vector, then "the derivative of f in the direction of [itex]\vec{v}[/itex]" is
[tex]\frac{\grad f\cdot\vec{v}}{|\vec{v}|}[/tex]

But that has nothing to do with "differentiating one vector by another".
 
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  • #5
I'm going to agree with HallsofIvy here... I think I need to re-look what my question is.
Thanks.
 

FAQ: Dot Product Differentiation question

What is a dot product differentiation?

A dot product differentiation is a mathematical operation that calculates the rate of change of a dot product between two vectors. It is used to determine how a small change in one vector affects the dot product with the other vector.

How is dot product differentiation used in science?

Dot product differentiation is used in many areas of science, such as physics, engineering, and computer science. It is commonly used to calculate forces, velocities, and accelerations in physical systems and to optimize algorithms in computer science.

What is the formula for dot product differentiation?

The formula for dot product differentiation is d/dx (A · B) = A' · B + A · B', where A and B are vectors and A' and B' are their respective derivatives with respect to the variable x.

How is dot product differentiation related to the chain rule?

Dot product differentiation is related to the chain rule in that it follows the same principles of differentiating composite functions. The dot product can be seen as a composite function of its individual vector components, and the chain rule is used to find the derivative of this composite function.

What are the applications of dot product differentiation?

Some common applications of dot product differentiation include finding the gradient of a scalar field, calculating the work done by a force, and optimizing algorithms in machine learning and data analysis. It is also used in other areas such as economics, statistics, and biology.

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