Gradient Vector Problem: Steepness and Slope Direction?

In summary, the gradient gives you the direction of the quickest altitude ascension (at least for this problem). You use what you calculated for the gradient to answer this part.
  • #1
bmb2009
90
0

Homework Statement


For a hill the elevation in meters is given by z=10 + .5x +.25y + .5xy - .25x^2 -.5y^2, where x is the distance east and y is the distance north of the origin.

a.) How steep is the hill at x=y=1 i.e. what is the angle between a vector perpendicular to the hill and the z axis?

b.)In which compass direction is the slope at x=y=1 steepest? Indicate whether the angle you provide is the angle measured in the standard way in the counter-clockwise direction from the x-axis (east) or whether it is the compass azimuth.



Homework Equations





The Attempt at a Solution


a.) So I got the gradient to be ∇f(x,y,z)= .5i - .25j...simply by the definition of the gradient. So i figured to figure the angle between the gradient and the z axis i could use the formula cosθ= (A dotted into B)/(lAl*lBl) but wouldn't be the z axis be the unit vector in the k hat direction making the cosθ=0...which can't be right. Any help on what to do? Also, I was given an equation that says ∇f(x,y,z) perpendicular to a 3 dimensional surface f(x,y,z) = constant. But I don't know how to use that equation to generate an answer.

b.)The gradient gives you the direction of the quickest altitude ascension (at least for this problem) so how do I use what I calculated for the gradient to answer this part?

Thanks for any help!
 
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  • #2
bmb2009 said:
a.) So I got the gradient to be ∇f(x,y,z)
Think of the altitude as a potential in 2 dimensions, just x and y. So ∇ is ?
 
  • #3
grad z gives you the x-y direction of steepest ascent, but now you have to ask yourself: when going in that direction, how much does dz change when you go a distance ds in the aforederived direction.

Think about how the x and y components of the gradient at your point (1,1) tell you what dz/ds would be.

P.S. the angle the problem asks for happens to be (90 deg - the steepness angle). Also, once you did (a) you already have (b). In fact, (b) should come first ... at least the way I look at it. Probably you were not supposed to solve for grad z for part (a) ... I don't know.
 
  • #4
Dear Rude Man,

Very nice job of doping it out. That's exactly the way I would have done it, including doing part (b) first.

Chet
 
  • #5
Chestermiller said:
Dear Rude Man,

Very nice job of doping it out. That's exactly the way I would have done it, including doing part (b) first.

Chet

Thanks Chet.
 

Related to Gradient Vector Problem: Steepness and Slope Direction?

1. What is the Gradient Vector Problem?

The Gradient Vector Problem (GVP) is a mathematical problem in vector calculus that involves finding the directional derivative of a function at a given point. It is often used in optimization problems to find the direction of steepest ascent or descent.

2. How is the Gradient Vector Problem solved?

The Gradient Vector Problem is typically solved by taking the partial derivatives of the function with respect to each variable and then combining them into a vector. This vector, known as the gradient, represents the direction of steepest increase of the function at that point.

3. What is the significance of the Gradient Vector Problem in science?

The Gradient Vector Problem has important applications in various scientific fields, including physics, engineering, economics, and computer science. It is used to solve optimization problems and to model the behavior of complex systems.

4. Can the Gradient Vector Problem be solved analytically?

In most cases, the Gradient Vector Problem can be solved analytically by taking the partial derivatives of the function and combining them into a vector. However, in some cases, numerical methods may be required to find an approximate solution.

5. Are there any real-world examples of the Gradient Vector Problem?

Yes, the Gradient Vector Problem has many real-world applications. For example, it can be used to optimize the shape of an aircraft wing for maximum lift or to find the shortest path between two points on a map. It is also used in machine learning algorithms to find the best parameters for a given model.

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