Calculus problem with directional vectors

In summary: The gradient is a vector that points in the direction of maximum increase of a function at a given point. The rate of maximum increase is the magnitude of the gradient vector, and the direction of maximum increase is the direction of the gradient vector. Essentially, the gradient provides information about the rate and direction of maximum increase for a function.
  • #1
jgreene2313
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At the point (2,-1,2) on the surface z = x(y^2), find the direction vector for the direction of greatest decrease of z.

A) i - 2j
B) i - 4j
C) (i - 4j)/sqrt(17)
D) -i + 4j
E) i + j

Do I need a function f(x,y,z)? If so then f(x,y,z) = z = xy^2.

Then the gradient vector would be <Fx, Fy, Fz>.

This would be give me. < -y^2, -2xy, 1>

At the point (2,-1,2) we get < -1, 4, 1>

But how do i get the direction of the vector of greatest decrease?

From the choices though it seems they only find the gradient vector of z = F(x,y) = xy^2

In which case the gradient vector is < fx, fy>

< y^2 , 2xy> .
At the point (2, -1 ,2) we get < 1, -4>
Again i am not sure how to find the vector of greatest decrease.

It says in my book the direction of minimum increase is - norm( grad f(x,y,z)). But i am not getting any of these answers can some one help?
 
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  • #2
jgreene2313 said:
At the point (2,-1,2) on the surface z = x(y^2), find the direction vector for the direction of greatest decrease of z.

A) i - 2j
B) i - 4j
C) (i - 4j)/sqrt(17)
D) -i + 4j
E) i + j

Do I need a function f(x,y,z)? If so then f(x,y,z) = z = xy^2.
No, it's z=f(x,y). You're given a function from R2 to R, not R3 to R.
Then the gradient vector would be <Fx, Fy, Fz>.

This would be give me. < -y^2, -2xy, 1>

At the point (2,-1,2) we get < -1, 4, 1>

But how do i get the direction of the vector of greatest decrease?

From the choices though it seems they only find the gradient vector of z = F(x,y) = xy^2

In which case the gradient vector is < fx, fy>

< y^2 , 2xy> .
At the point (2, -1 ,2) we get < 1, -4>.

Again i am not sure how to find the vector of greatest decrease.

It says in my book the direction of minimum increase is - norm( grad f(x,y,z)). But i am not getting any of these answers can someone help?
I doubt your book says that. The norm of the gradient is a number, not a vector; it doesn't have a direction.

How are the gradient and the rate and direction of maximum increase related?
 

FAQ: Calculus problem with directional vectors

1. What are directional vectors in calculus?

Directional vectors in calculus are vectors that represent the direction and magnitude of change in a function at a specific point. They are often used to calculate the rate of change or slope of a function.

2. How do you find the directional vector of a function?

To find the directional vector of a function, you first choose a direction or a unit vector. Then, you take the partial derivatives of the function with respect to each variable and multiply them by the chosen direction vector. The resulting vector is the directional vector.

3. What is the significance of directional vectors in calculus?

Directional vectors are important in calculus because they help us understand the rate of change of a function in a specific direction. They also allow us to find the maximum or minimum rate of change and the direction in which it occurs.

4. How do you use directional vectors to solve calculus problems?

To solve calculus problems using directional vectors, you first find the directional vector of the function. Then, you use this vector to calculate the rate of change or slope of the function in a specific direction. This can help you find critical points, find the maximum or minimum rate of change, and solve optimization problems.

5. Can directional vectors be used in three-dimensional calculus problems?

Yes, directional vectors can be used in three-dimensional calculus problems. In this case, the directional vector will have three components, one for each variable, and it will represent the change in the function in a specific direction in three-dimensional space.

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