Lagrange Multipliers with ellipse

In summary: The y-axis goes through the origin (0,0), and the z-axis goes through the point (1,1). So the distance from the origin to the hyperboloid is equal to the distance from the origin to the point (1,1). This means that z=-1.
  • #1
Quincy
228
0

Homework Statement



Find the points on the ellipse x2 + 2y2 = 1 where f(x,y) = xy has its extreme values.

Homework Equations





The Attempt at a Solution


f(x,y,z) = x2 + y2 + z2 -- constraint
g(x,y,z) = x2 + 2y2 -1 = 0

gradient of f = [tex]\lambda[/tex] * gradient of g

2xi + 2yj + 2zk = [tex]\lambda[/tex]2xi + [tex]\lambda[/tex]4yj

2x = [tex]\lambda[/tex]2x
2y = [tex]\lambda[/tex]4y
2z = 0

I don't know where to go from here, can someone help me out?
 
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  • #2
Sorry, I made a mistake in my original post, it should be:

f(x,y,z) = xy
g(x,y,z) = x2 + 2y2 - 1

yi + xj = [tex]\lambda[/tex](2xi + 4yj)
y = 2x[tex]\lambda[/tex]
x = 4y[tex]\lambda[/tex]

y = 2(4y[tex]\lambda[/tex])[tex]\lambda[/tex] = 8y[tex]\lambda[/tex]2

[tex]\lambda[/tex] = sqrt(1/8)

where do i go from here?
 
  • #3
you have three equations and three unknowns... the two gradient equations and the fact that x2+2y2=1. Now that you have solved for one unknown, can you make it two equations with two unknowns?
 
  • #4
How about this one:

Find the minimum distance from the surface x2 - y2 - z2 = 1 to the origin. (function being minimized = x2 + y2 + z2)

2xi + 2yj + 2zk = [tex]\lambda[/tex](2xi - 2yj -2zk)

2x = 2x[tex]\lambda[/tex]
2y = -2y[tex]\lambda[/tex]
2z = -2z[tex]\lambda[/tex]
x2 = y2 +z2 + 1

- I can't figure out how to solve this system of equations, any tips?
 
  • #5
These are pretty close to being trivial. From the first equation, if [itex]x\ne 0[/itex], [itex]\lambda[/itex] must be 1. From the second equation, if [itex]y\ne 0[/itex], [itex]\lambda[/itex] must be -1, and from the third, if [itex]z\ne 0[/itex], [itex]\lambda[/itex] must be -1.

Since [itex]\lambda[/itex] cannot be both 1 and -1, at least one of the coordinates must be 0!

Suppose x= 0. Then the constraint becomes [itex]-y^2- z^2= 1[/itex] which is impossible. If x is not 0, then [itex]\lambda[/itex] must be 1, not -1, so y and z must be 0. The constraint becomes [itex]x^2= 1[/itex].

Looking at it geometrically gives an easy check. This is a "hyperboloid of two sheets" with the x-axis as axis of symmetry.
 
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What is the concept of Lagrange Multipliers?

The Lagrange Multipliers, also known as the method of undetermined multipliers, is a mathematical technique used to find the maximum or minimum values of a function subject to constraints. It involves the use of a scalar variable, known as the Lagrange multiplier, to incorporate the constraints into the optimization problem.

How does Lagrange Multipliers work with ellipses?

Lagrange Multipliers can be applied to optimize a function on an ellipse, where the constraints are defined by the equation of the ellipse. By setting up the Lagrangian function, which includes the original function and the constraints, and solving for the Lagrange multiplier, the maximum or minimum values of the function on the ellipse can be found.

What are the advantages of using Lagrange Multipliers with ellipses?

One advantage is that Lagrange Multipliers provide a systematic way of incorporating constraints into optimization problems. This allows for a more efficient way of finding the maximum or minimum values of a function on an ellipse compared to other methods. Additionally, Lagrange Multipliers can also be applied to more complex constraints, making it a versatile technique.

Can Lagrange Multipliers be used with other geometric shapes besides ellipses?

Yes, Lagrange Multipliers can be applied to optimize functions on other geometric shapes such as circles, rectangles, and triangles. However, the constraints for each shape would need to be defined and incorporated into the Lagrangian function accordingly.

What are some real-world applications of Lagrange Multipliers with ellipses?

Lagrange Multipliers with ellipses can be applied in various fields such as physics, engineering, and economics. For example, in physics, it can be used to optimize the trajectory of a particle moving in an elliptical orbit. In economics, it can be used to maximize profits for a company operating within certain constraints. Additionally, it can also be applied in optimization problems in computer science and data analysis.

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