Directional Derivatives and max rate of change

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Homework Help Overview

The discussion revolves around the concept of directional derivatives and the maximum rate of change of a function, specifically in the context of a multivariable function represented by its gradient. Participants are addressing a problem involving the evaluation of the gradient at a specific point and its implications for directional derivatives.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the relationship between the gradient and the maximum rate of change, with one participant attempting to derive the directional derivative using the gradient. Questions arise regarding the implications of using a non-unit vector for direction and the challenges of solving equations involving multiple variables.

Discussion Status

The discussion is ongoing, with participants exploring different interpretations of the problem. Some guidance has been offered regarding the relationship between the gradient and directional derivatives, but there is no explicit consensus on the best approach to solve part (b) of the problem.

Contextual Notes

Participants note potential constraints, such as the complexity of solving a single equation with three variables and the implications of certain variables being zero, which could affect the directionality of the gradient.

Blkmage
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Homework Statement



See attachment.

2. Homework Equations /solution attempt

Part (a)
Well, the gradient evaluated at (1,2-1) will give the rate of change. If we want the maximum rate of change then we need the directional direction such that the unit vector \mathbf{u} is in the same direction as \nabla f(1,2,-1). So the unit vector would just be \frac{\nabla f(1,2,-1)}{\text{norm}(\nabla f(1,2,-1))}? And the max rate of change is just the direction derivative at this point and direction?

Anyways, part (b) is what I'm having trouble with:

Since we want it to be in the direction of the vector (1,-1,-1), does that mean that we want \nabla g(x_{Q},y_{Q},z_{Q}) = (1,-1,-1)? Since (1,-1,-1) isn't a unit vector, I didn't divide by the norm of the gradient...
 

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the gradient gives the direction of maximum rate of change, its magnitude is the rate of change moving along that direction

the magnitude of the gradient is equivalent to a dot product with a unit vector in the same direction, ie the value of the directional derivative in that direction
 
So for part (b), we have

\text{norm}(\nabla g(x_{Q},y_{Q},z_{Q})) = \nabla g(x_{Q},y_{Q},z_{Q}) \cdot \frac{1}{\sqrt{3}}\left(1,-1,-1\right)

This seems so tedious to solve though. It's on a practice exam my teacher gave us and his tests are very reasonable given the time limits, so I'm guessing there is something I'm missing?

edit: not to mention that would give me a single equation of 3 variables, which I wouldn't be able to solve
 
g(x,y,z) = x^2yz
\nabla g(x,y,z) = (2xyz, x^2z, x^2y)

and you want to know when is in the direction (1,-1,-1), first clearly this will not be possible if any of the varibales are 0 as it will lead tio .

substituting in gives
2xyz = -x^2z _______(1)
2xyz = -x^2y _______(2)
x^2z = x^2y _______(3)

so you know y=z from (3), as x dne 0, which should simplify things
 

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