Find Answer for Gradient Question Starting at (3,2)

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In summary: Thanks!In summary, The correct method for calculating the rate of change of a function in a given direction is to evaluate the directional derivative at the given point. This can be done by taking the dot product of a unit vector in the desired direction and the gradient of the function at that point. Simply calculating the gradient does not give the desired result.
  • #1
bryanosaurus
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I am given z = 32 - x[tex]^{2}[/tex] - 4y[tex]^{2}[/tex]
Starting at the point (3,2) in i + j direction,
find if you are going up or down the hill and how fast.

The way I thought to proceed was that the gradient would tell me if I was going down or up hill and that [tex]\left|\nabla z \right|[/tex] would give me "how fast". My answer of [tex]\sqrt{292}[/tex] isn't correct however, so I'm obviously doing something wrong. Can anyone point me in the right direction of how to proceed?
 
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  • #2
bryanosaurus said:
I am given z = 32 - x[tex]^{2}[/tex] - 4y[tex]^{2}[/tex]
Starting at the point (3,2) in i + j direction,
find if you are going up or down the hill and how fast.

The way I thought to proceed was that the gradient would tell me if I was going down or up hill and that [tex]\left|\nabla z \right|[/tex] would give me "how fast". My answer of [tex]\sqrt{292}[/tex] isn't correct however, so I'm obviously doing something wrong. Can anyone point me in the right direction of how to proceed?
The magnitude of the gradient effectively gives you the magnitude of the greatest increase/decrease of the function, rather than the rate of change of the function in a given direction, which is what you were asked. Instead of simply calculating the gradient of the function, you need to evaluate the directional derivative of the function at the given point, in the given direction.
 
  • #3
What gradient did you compute?

A few notes:

1. "The (i + j) direction" is misleading, because (i + j) is not a unit vector!

2. The directional derivative in the direction of a unit vector [itex]\mathbf{\hat u}[/itex] is given by:

[tex]\mathbf{\hat u} \cdot \nabla f[/tex]
 
  • #4
Ben Niehoff said:
What gradient did you compute?

A few notes:

1. "The (i + j) direction" is misleading, because (i + j) is not a unit vector!

2. The directional derivative in the direction of a unit vector [itex]\mathbf{\hat u}[/itex] is given by:

[tex]\mathbf{\hat u} \cdot \nabla f[/tex]


The gradient I computed was:
-2xi - 8yj

If I am supposed to calculate [tex]\mathbf{\hat u} \cdot \nabla f[/tex], what unit vector am I supposed to use? As you said, i + j isn't a unit vector...
 
  • #5
Do you not know how to construct a unit vector in the direction of, say, vector u? Just divide by its length.
 
  • #6
HallsofIvy said:
Do you not know how to construct a unit vector in the direction of, say, vector u? Just divide by its length.

I just realized that was what I was over looking. Thanks, if I use

i + j / |i + j|

i get the correct answer.
 

1. What is a gradient?

A gradient is a mathematical concept used in calculus and vector analysis to describe the rate of change of a function at a given point. It is represented by a vector and indicates both the direction and magnitude of the function's steepest increase.

2. How do I find the gradient of a function?

To find the gradient of a function, you must first find its derivative. The derivative is a function that represents the slope of the original function at any given point. Once you have the derivative, you can evaluate it at the given point to find the gradient.

3. What is the relationship between gradient and slope?

The gradient is a generalization of slope for multivariable functions. In two-dimensional space, the gradient and slope are equivalent. However, in higher dimensions, the gradient represents the rate of change in all directions, while slope only represents the change in one direction.

4. What does the gradient vector tell us?

The gradient vector tells us the direction in which a function is increasing the fastest at a given point. The magnitude of the vector represents the rate of change in that direction. This information is useful in many areas of science, including physics, engineering, and economics.

5. How can I use the gradient to optimize a function?

The gradient can be used to find the maximum or minimum values of a function. This is done by setting the gradient equal to zero and solving for the variables. The resulting values are the critical points of the function, which can be used to optimize it.

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