Optimizing Directional Derivatives in the XY Plane

In summary, the conversation discusses finding the direction in the xy plane that will result in the most rapid rate of decrease for the function f(x,y,z) = (x + y - 2)^2 + (3x - y - 6)^2, starting from the point (1,1). The gradient of the function is calculated to be (2(x+y-2) + 6(3x-y-6), 2(x+y-2) - 2(3x-y-6)), and it is determined that the minimum occurs when the unit vector is in the opposite direction of the gradient, which is (24, -8).
  • #1
dcl
55
0
Heya's
I need to find the direction in the xy plane in which one should travel, starting from point (1,1), to obtain the most rapid rate of decrease of
[tex]f(x,y,z) = (x + y - 2)^2 + (3x - y - 6)^2[/tex]

now, [tex]\nabla f = (2(x+y-2), 2(3x-y-6))[/tex]

so I'm thinking now I have to find the the unti vector 'u' which would be the direction in question. Unfortunately I do not know how to go on from here.
Somehow maximise [tex]\nabla f \cdot u[/tex] (where 'u' is a unit vector, don't know how to do vectors properly in latex :frown: )
 
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  • #2
\vec{u} (I looked it up myself just a few hours ago).

First of all, [tex]\nabla f = (2(x + y - 2) + 6(3x - y -6), 2(x + y - 2) - 2(3x - y - 6))[/tex]
Second, you are only concerned with what is going on at (1,1), where [tex]\nabla f[/tex] = (-24, 8).

Now, the question is to minimize (-24, 8)[tex]\cdot\vec{u}[/tex]. Since [tex]\vec{v}\cdot\vec{w} = vw\cos \theta[/tex], where [tex]\theta[/tex] is the angle between them, we see that the minimum occurs when [tex]\theta = \pi[/tex]. I.e., when the vectors are pointing in opposite directions.

So [tex]\vec{u}[/tex] must be the unital vector in the direction of (24, -8).
 
  • #3
Silly me, I was straining all that time trying to figure out why my method is wrong and all the time I had been working with the wrong grad f. Thanks.
 

FAQ: Optimizing Directional Derivatives in the XY Plane

What is a directional derivative?

A directional derivative is a measure of how a function changes in a particular direction. It is used to calculate the rate of change of a function at a point in a specific direction, and is often represented by the symbol ∇f.

How is a directional derivative calculated?

To calculate a directional derivative, you first need to find the gradient of the function at the given point. Then, you can take the dot product of the gradient and the unit vector in the desired direction to find the directional derivative.

What does a positive directional derivative indicate?

A positive directional derivative indicates that the function is increasing in the given direction at the given point. This means that as you move in that direction, the function values are increasing.

How does the direction of the gradient affect the directional derivative?

The direction of the gradient affects the directional derivative because the gradient is always perpendicular to the level curves of the function. This means that the directional derivative will be the greatest when the direction of the gradient aligns with the direction of the unit vector.

In what fields is the concept of directional derivatives commonly used?

Directional derivatives are commonly used in fields such as physics, engineering, economics, and computer science. They are particularly useful in optimization problems, where the goal is to find the direction of greatest change for a given function.

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