Extrema in 3D Problem: Finding Highest and Lowest Points on a Defined Surface

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  • #1
namu
33
0

Homework Statement



Consider the surface defined by

F(x,y,z)=1/2(x+y)^2+(y+z)^2+(x+z)^2=9

(a) Evaluate grad(F)

My Solution:
grad(F)=(3x+y+2z, x+3y+2z, 2x+2y+4z)

(b) Find the highest and lowest points on the surface (i.e. the points where z attains a maximum or minimum).

Problem:
So I can solve for z=g(x,y) using the quadratic formula and get a nasty expression (see attached) and
then go about finding the extrema, but this is ugly.

If I set each component of the gradient to zero, then the only solution is (x,y,z)=(0,0,0) which is NOT an
extrema (see attached image), rather there are two extrema.

How do I do this the "easy" way?

(c) The surface is illuminated from far above by light rays that are directed parallel to the z-axis. Find the
shape of its shadow in the plane below the surface parallel to the (x,y) coordinate plane.

Problem:

So now this is projecting the surface onto the (x,y)-plane. I have no idea how to do this.

Please help.
 

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  • #2
namu said:

Homework Statement



Consider the surface defined by

F(x,y,z)=1/2(x+y)^2+(y+z)^2+(x+z)^2=9

(a) Evaluate grad(F)

My Solution:
grad(F)=(3x+y+2z, x+3y+2z, 2x+2y+4z)

(b) Find the highest and lowest points on the surface (i.e. the points where z attains a maximum or minimum).

Problem:
So I can solve for z=g(x,y) using the quadratic formula and get a nasty expression (see attached) and
then go about finding the extrema, but this is ugly.

If I set each component of the gradient to zero, then the only solution is (x,y,z)=(0,0,0) which is NOT an
extrema (see attached image), rather there are two extrema.

How do I do this the "easy" way?

(c) The surface is illuminated from far above by light rays that are directed parallel to the z-axis. Find the
shape of its shadow in the plane below the surface parallel to the (x,y) coordinate plane.

Problem:

So now this is projecting the surface onto the (x,y)-plane. I have no idea how to do this.

Please help.

Use the equation F(x,y,z) = 0 to evaluate [itex] z_x = \partial z / \partial x[/itex] and [itex] z_y = \partial z / \partial y[/itex] at a given point [itex] (x_0,y_0,z_0)[/itex] on the surface; that is, you need to figure out [itex] \Delta z[/itex] such that
[tex] F(x_0 + \Delta x, y_0 + \Delta y, z_0 + \Delta z) = 0. [/tex] You don't need the exact[itex] \Delta z[/itex]; you just need the ``first-order'' expression that is linear in [itex] \Delta x \mbox{ and } \Delta y.[/itex] Then you need [itex] z_z = 0[/itex] and [itex] z_y = 0. [/itex]

RGV
 

1. What is an extrema in 3D problem?

An extrema in 3D problem involves finding the highest and lowest points on a defined surface in three-dimensional space. This is often used in mathematics and science to determine the maximum and minimum values of a function.

2. How do you find the highest and lowest points on a defined surface?

To find the highest and lowest points on a defined surface, you can use the gradient vector to determine the direction of steepest ascent and descent. Then, you can use the directional derivatives to find the points where the function is increasing and decreasing the most rapidly, which will be the highest and lowest points on the surface.

3. What is the gradient vector?

The gradient vector is a vector that points in the direction of the steepest ascent or descent of a function. It is calculated by taking the partial derivatives of the function with respect to each variable and combining them into a vector.

4. Can extrema in 3D problems have multiple solutions?

Yes, extrema in 3D problems can have multiple solutions. This can happen when the function has multiple local maxima or minima, or when the surface has multiple regions with different maximum and minimum points.

5. How are extrema in 3D problems used in science?

Extrema in 3D problems are commonly used in various scientific fields such as physics, engineering, and economics. They can help determine the optimal values for different variables in a system, such as finding the minimum energy required for a mechanical system or the maximum profit in an economic model.

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