Directional derivative at a point

In summary: Well, it IS possible to have a well-defined directional derivatives in some directions ##\vec{d}## at a point ##\vec{r_0}= (x_0,y_0,z_0)##, but to not have well-defined, finite partial derivatives at ##\vec{r_0}##. However, the present example is not so pathological.
  • #1
catch22
62
0

Homework Statement


upload_2015-10-29_21-55-55.png


Homework Equations

The Attempt at a Solution


part a) finding partial derivatives:
upload_2015-10-29_22-5-49.png


and plugging in (2,0,1) into each, I get the gradient which is <0,-2,0>

to find the directional derivative, it is the dot product of the gradient and unit vector of (3,1,1):

upload_2015-10-29_22-1-26.png


part b) isn't the direction of the greatest increase just the gradient, which I found in part A?

upload_2015-10-29_22-4-50.png


and the greatest increase at (2,0,1) is the magnitude of the gradient there, which is

upload_2015-10-29_22-7-4.png
 
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  • #2
catch22 said:
part b) isn't the direction of the greatest increase just the gradient, which I found in part A?
Yes.
A pedant might insist that a direction should be given as a normalized vector. But we are not pedants.
 
  • #3
catch22 said:

Homework Statement


View attachment 91055

Homework Equations

The Attempt at a Solution


part a) finding partial derivatives:
View attachment 91058

and plugging in (2,0,1) into each, I get the gradient which is <0,-2,0>

to find the directional derivative, it is the dot product of the gradient and unit vector of (3,1,1):

View attachment 91056

part b) isn't the direction of the greatest increase just the gradient, which I found in part A?

View attachment 91057

and the greatest increase at (2,0,1) is the magnitude of the gradient there, which is

View attachment 91059

Some of your partial derivatives are incorrect (although not by very much).
catch22 said:

Homework Statement


View attachment 91055

Homework Equations

The Attempt at a Solution


part a) finding partial derivatives:
View attachment 91058

and plugging in (2,0,1) into each, I get the gradient which is <0,-2,0>

to find the directional derivative, it is the dot product of the gradient and unit vector of (3,1,1):

View attachment 91056

part b) isn't the direction of the greatest increase just the gradient, which I found in part A?

View attachment 91057

and the greatest increase at (2,0,1) is the magnitude of the gradient there, which is

View attachment 91059

One of your partial derivatives is wrong.
 
  • #4
Also, you don't say anything about the continuity of the first partial derivatives in a neighborhood of ##(2,0,1)##. It is under that condition that the formula ##(D_{\vec v}f)(2,0,1) = (\nabla f. \vec v) (2,0,1) ## is valid.
 
  • #5
Ray Vickson said:
Some of your partial derivatives are incorrect (although not by very much).One of your partial derivatives is wrong.
wow, I can't believe I didn't spot that +x at the end.

it should be (-yz e^-xyz) + 1
 
  • #6
the correct answers after fixing the partial derivative with respect to x

upload_2015-10-30_4-27-18.png
 

Attachments

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  • #7
geoffrey159 said:
Also, you don't say anything about the continuity of the first partial derivatives in a neighborhood of ##(2,0,1)##. It is under that condition that the formula ##(D_{\vec v}f)(2,0,1) = (\nabla f. \vec v) (2,0,1) ## is valid.
do you mean like saying the domain is from negative infinity to positive infinity?
 
  • #8
Not at all and I wonder why you ask that ?
 
  • #9
geoffrey159 said:
Not at all and I wonder why you ask that ?

Well, it IS possible to have a well-defined directional derivatives in some directions ##\vec{d}## at a point ##\vec{r_0}= (x_0,y_0,z_0)##, but to not have well-defined, finite partial derivatives at ##\vec{r_0}##. However, the present example is not so pathological.
 

1. What is the concept of directional derivative at a point?

The directional derivative at a point is a measure of the rate of change of a function in a specific direction at that point. It represents the slope of the function in the direction of a given vector.

2. How is the directional derivative at a point calculated?

The directional derivative at a point is calculated using the partial derivatives of the function with respect to each variable, as well as the direction vector. The formula is given by: Dvf(x,y) = ∂f/∂x * cosθ + ∂f/∂y * sinθ, where θ is the angle between the direction vector and the positive x-axis.

3. What is the significance of the directional derivative at a point?

The directional derivative at a point is significant because it helps in determining the maximum rate of change of a function in a particular direction. It is also useful in optimization problems, where the goal is to find the direction in which the function has the steepest increase or decrease.

4. How does the directional derivative at a point relate to the gradient?

The directional derivative at a point can be seen as the projection of the gradient vector onto the direction vector. In other words, it is the dot product of the gradient and the unit vector in the direction of interest. This relationship helps in understanding the direction in which the function changes the most.

5. Can the directional derivative at a point be negative?

Yes, the directional derivative at a point can be negative. This indicates that the function is decreasing in the direction of the given vector. A positive directional derivative indicates an increase in the function, while a value of zero indicates no change in the function in the given direction.

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