Possible Gradient Vector question

In summary, the temperature of a plate in the (x,y) plane is given by T(x,y) = 50 - x^2 - 2y^2. A bug on the plate is initially at the point (2,1). To ensure that the temperature decreases as rapidly as possible, the bug's velocity should be along the negative of the gradient vector. Using this, the equation for the curve the bug should follow can be found by taking the derivative of y with respect to x, which is equivalent to finding the slope of the tangent line to the curve. This is given by dy/dx = 4y/2x.
  • #1
CAF123
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Homework Statement


The temperature T of a plate lying in the (x,y) plane is given by [tex] T(x,y) = 50 - x^2 - 2y^2. [/tex] A bug on the plate is intially at the point (2,1). What is the equation of the curve the bug should follow so as to ensure that the temperature decreases as rapidly as possible?

The Attempt at a Solution


So, [tex] \vec{∇}T(2,1) = <-4,-4> [/tex] If we want the temperature to decrease as rapdily as possible we are looking for when the directional derivative is minimal. So the direction is [itex] -\vec{∇}T(2,1) = <4,4>. [/itex] My professor gave a hint to find dy/dx and then solve for y. Using the Implicit Function theorem this gives; [tex] \frac{dy}{dx} = -\frac{-2x}{-4y}. [/tex] I don't see how this method takes into account that we are considering the minimal temperature? (In fact, the gradient vector is not used at all)?
 
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  • #2
CAF123 said:

Homework Statement


The temperature T of a plate lying in the (x,y) plane is given by [tex] T(x,y) = 50 - x^2 - 2y^2. [/tex] A bug on the plate is intially at the point (2,1). What is the equation of the curve the bug should follow so as to ensure that the temperature decreases as rapidly as possible?

The Attempt at a Solution


So, [tex] \vec{∇}T(2,1) = <-4,-4> [/tex] If we want the temperature to decrease as rapdily as possible we are looking for when the directional derivative is minimal. So the direction is [itex] -\vec{∇}T(2,1) = <4,4>. [/itex] My professor gave a hint to find dy/dx and then solve for y. Using the Implicit Function theorem this gives; [tex] \frac{dy}{dx} = -\frac{-2x}{-4y}. [/tex] I don't see how this method takes into account that we are considering the minimal temperature? (In fact, the gradient vector is not used at all)?

At every point on the path the bug's velocity should be along the negative of the gradient, so if the bug moves at constant speed v, it should move by dx = -v*(-2x)/D, dy = -v*(-4y)/D, where D = norm of the gradient vector = √(4x^2 + 16y^2). If you take dy/dx that gets rid of v and D.

RGV
 
  • #3
Ray Vickson said:
At every point on the path the bug's velocity should be along the negative of the gradient, so if the bug moves at constant speed v, it should move by dx = -v*(-2x)/D, dy = -v*(-4y)/D, where D = norm of the gradient vector = √(4x^2 + 16y^2). If you take dy/dx that gets rid of v and D.

RGV
Could you show how you got those eqns for dx and dy? By the Implicit function theorem, I get [tex] \frac{dy}{dx} = -\frac{x}{2y}, [/tex] while from the method above using those eqns derived, it is [itex] \frac{dy}{dx} = \frac{x}{2y} [/itex] out by a minus sign?
 
  • #4
CAF123 said:
Could you show how you got those eqns for dx and dy? By the Implicit function theorem, I get [tex] \frac{dy}{dx} = -\frac{x}{2y}, [/tex] while from the method above using those eqns derived, it is [itex] \frac{dy}{dx} = \frac{x}{2y} [/itex] out by a minus sign?

Your dy/dx would take the bug along a level curve (a curve of constant T). You want the bug to move perpendicular to that direction.

RGV
 
  • #5
Oh I see. As said in my previous post, the directional derivative is minimal when we take the negative of the gradient vector. (The gradient vector points in the direction of increasing value for a function). From what RGV said, dy/dx will be parallel to -∇T and so in the same direction as <2x,4y> which means dy/dx = 4y/2x.
Could you still explain how your formulae for dx and dy are derived?
 

Related to Possible Gradient Vector question

1. What is a gradient vector?

A gradient vector represents the direction and magnitude of the steepest ascent of a function. It is a vector that points in the direction of greatest increase of the function at a specific point.

2. How is a gradient vector calculated?

A gradient vector is calculated by taking the partial derivatives of a multivariable function with respect to each variable and combining them into a vector. The gradient vector is then evaluated at a specific point to determine its direction and magnitude.

3. What information does a gradient vector provide?

A gradient vector provides information about the slope or rate of change of a function at a specific point. It can also be used to find the direction of maximum increase or decrease of the function.

4. Can a gradient vector be negative?

Yes, a gradient vector can be negative. This indicates that the function is decreasing in that direction. However, the magnitude of the vector will still represent the steepest decrease or increase.

5. How is a gradient vector used in real-world applications?

Gradient vectors are used in many fields, including physics, engineering, and economics, to analyze and optimize functions. They can be used to find the maximum or minimum of a function, such as in optimization problems, or to study the behavior of a system over time.

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