Directional derivative and gradient concepts

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SUMMARY

This discussion centers on the concepts of directional derivatives and gradients in multivariable calculus. Participants explore the relationship between directional derivatives and partial derivatives, particularly in the context of the function f(x,y) = x² + y². Key points include the assertion that the gradient ∇f is perpendicular to the level curves of f and the clarification that the maximum and minimum rates of change at a point P cannot be 7 and -5 simultaneously, as they should be equal in magnitude but opposite in sign.

PREREQUISITES
  • Understanding of multivariable calculus concepts, specifically directional derivatives and gradients.
  • Familiarity with the properties of level curves and their relationship to gradients.
  • Knowledge of the dot product and its application in calculating directional derivatives.
  • Basic understanding of continuous differentiable functions.
NEXT STEPS
  • Study the properties of gradients and their geometric interpretations in multivariable calculus.
  • Learn how to compute directional derivatives using the dot product method.
  • Explore the implications of the chain rule for functions of multiple variables.
  • Investigate the relationship between level curves and the behavior of functions in higher dimensions.
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Students and educators in mathematics, particularly those focusing on multivariable calculus, as well as professionals applying these concepts in fields such as physics, engineering, and data science.

clairez93
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Homework Statement



A series of true/false questions. I guess I don't understand the concepts of this very well:

1. If you know the directional derivative of f(x,y) in two different directions at a point P, we can find the derivative with respect to the x and y axes and thus we can determine the derivatives at this point P.

2. If f(x,y) = x^{2} + y^{2} then
\nabla f\bot graph(f).


For the level curves in the figure and point P:
a) If u is a unit vector and the level curves of f(x,y) are given as shown, then at the point P, we have

f_{u} = D_{u}f = \nabla f.

b) For the same f and the unit vector v shown,
f_{v} = D_{v}f =(I can't get this to come out right, but it's supposed to say that the magnitude of the gradient of f times cos theta)


c) There is a function z = f(x,y) and a point P so that the maximum rate of change in f as you move away from P is 7 and the minimum rate of chang ein f as you move from P is -5.

Homework Equations





The Attempt at a Solution




1. Not sure about this, but I want to guess that it has something to do with the chain rule for many variables?

2. I want to say true, because I know that the gradient is always perpendicular to the level curve, but I'm not sure if that is what "graph(f)" refers to?

a) Not a clue really here. I want to say false because I don't think that the directional derivative is equal to the gradient, unless they're parallel? In any case I don't think that they would be equal to the partial derivative...

b) I kind of want to say false again because, because where did the unit vector go? The dot product of the gradient and the unit vector u should be equal to the directional derivative, but the u has disappeared, unless u is equal to 1? Which I don't think it is.

c) So they're trying to say that the magnitude of the gradient is 7 and the negative of that is -5? I think that's wrong, because isn't the maximum and minimum the same except that the minimum is the negative of the magnitude? So it can't be 7 and -5?



Most of my answers here are complete guesswork. Help would be appreciated.
 

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do most of these assume a conitinuous differentiable function?

clairez93 said:

Homework Statement



A series of true/false questions. I guess I don't understand the concepts of this very well:

1. If you know the directional derivative of f(x,y) in two different directions at a point P, we can find the derivative with respect to the x and y axes and thus we can determine the derivatives at this point P.

2. If f(x,y) = x^{2} + y^{2} then
\nabla f\bot graph(f).


For the level curves in the figure and point P:
a) If u is a unit vector and the level curves of f(x,y) are given as shown, then at the point P, we have

f_{u} = D_{u}f = \nabla f.


b) For the same f and the unit vector v shown,
f_{v} = D_{v}f =(I can't get this to come out right, but it's supposed to say that the magnitude of the gradient of f times cos theta)


c) There is a function z = f(x,y) and a point P so that the maximum rate of change in f as you move away from P is 7 and the minimum rate of chang ein f as you move from P is -5.

Homework Equations





The Attempt at a Solution




1. Not sure about this, but I want to guess that it has something to do with the chain rule for many variables?
start with the directional derivative using the dot product, assuming f is differentiable at the point, then think vectors & linear independence
clairez93 said:
2. I want to say true, because I know that the gradient is always perpendicular to the level curve, but I'm not sure if that is what "graph(f)" refers to?
not sure what graph(f) is either...
clairez93 said:
a) Not a clue really here. I want to say false because I don't think that the directional derivative is equal to the gradient, unless they're parallel? In any case I don't think that they would be equal to the partial derivative...
once again use the dot product form of the directional derivative? note that u looks perpindicular to the level curves at that point... (i'd make that assumption)
clairez93 said:
b) I kind of want to say false again because, because where did the unit vector go? The dot product of the gradient and the unit vector u should be equal to the directional derivative, but the u has disappeared, unless u is equal to 1? Which I don't think it is.
doesn't make sense... aren't we onto v & cos(theta)? anyway write out the directional derivative...
clairez93 said:
c) So they're trying to say that the magnitude of the gradient is 7 and the negative of that is -5? I think that's wrong, because isn't the maximum and minimum the same except that the minimum is the negative of the magnitude? So it can't be 7 and -5?
your reasoning is good here,

It helps me to think of the plane that is tangent to the function at a given point. The rate of maximum change on the plane is the direction of the gradient, and as you point out moving in the negative direction will have the same magnitude but a negtive rate of change (think of the dot product form of the directional derivative again)

when you move perpindicular to the gradient you're moving horizontally along the plane & the rate of change is zero (think dot product again)
clairez93 said:
Most of my answers here are complete guesswork. Help would be appreciated.
 

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