A directional, partial derivative of a scalar product?

In summary, the conversation discusses how to express the directional derivative of the dot product of two vector fields, a and b, along a specific direction, while only considering changes in b. The goal is to find a simple and clean way to express this without using unit vectors or absolute value notation. It is clarified that the gradient of a•b is equal to a, not b as previously thought. The question is also raised about the difference between the dot product and the directional derivative of a•b along a.
  • #1
particlezoo
113
4
Let's say I have two vector fields a(x,y,z) and b(x,y,z).

Let's say I have a scalar field f equal to ab.

I want to find a clean-looking, simple way to express the directional derivative of this dot product along a, considering only changes in b.

Ideally, I would like to be able to express this without invoking unit vectors and without || 's, while still using vector notation.
 
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  • #2
Henryk said:
## \vec a \cdot (\nabla \vec b)##

If vectors in a had x-components only, and vectors in b had y-components only, wouldn't ## \vec a \cdot (\nabla \vec b)## still return values with y-components only provided that b varies with x, as opposed to zero, which ab would be equal to everywhere?

Edit: Maybe I am conflating ## \vec a \cdot (\nabla \vec b)## with ## (\vec a \cdot \nabla) \vec b##

Edit2: Also, It seems like you gave the gradient of the dot product of ab, considering only changes in b. I was wondering about the directional derivative of ab along a, considering only changes in b.
 
Last edited:
  • #3
My mistake
 

Related to A directional, partial derivative of a scalar product?

1. What is a directional, partial derivative?

A directional, partial derivative is a mathematical concept used to measure the rate of change of a scalar function with respect to a specific direction. It is a combination of the partial derivative and the directional derivative.

2. How is a directional, partial derivative calculated?

A directional, partial derivative is calculated by taking the dot product of the gradient of the scalar function and the unit vector in the direction of interest.

3. What is the difference between a directional, partial derivative and a regular partial derivative?

The main difference is that a directional, partial derivative takes into account the direction of change, whereas a regular partial derivative only considers the change in one variable while holding the others constant.

4. What is the significance of a directional, partial derivative?

A directional, partial derivative is important in many fields of science, such as physics and engineering, as it allows us to understand how a scalar function changes in a specific direction. This can be useful in optimizing processes and predicting outcomes.

5. Can a directional, partial derivative be negative?

Yes, a directional, partial derivative can be negative. This indicates that the scalar function is decreasing in the given direction.

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