Finding Extrema with Lagrange Multipliers

In summary, the method of Lagrange multipliers is being used to find the maximum and minimum values of f(x,y,z) = x^2y^2-y^2z^2 + z^2x^2 subject to the constraint of x^2 + y^2 + z^2 = 1. By setting up and solving a system of equations using the gradient, 26 critical points are found. The extrema are achieved at (±1,0,0), (0,±1,0), (0,0,±1), (±√(3/5),±√(1/5),±√(1/5)), (0,±1/√
  • #1
ElDavidas
80
0
I'm stuck on the following question

"Find the maximum and minimum values of f(x,y,z) = [tex] x^2y^2-y^2z^2 + z^2x^2[/tex] subject to the constraint of [tex] x^2 + y^2 + z^2 = 1[/tex] by using the method of lagrange multipliers.

Write the 4 points where the minimum value is achieved and the 8 points where the max is achieved"

So far, I've managed to calculate the gradient for both formulae and have the resulting equations:

[tex]2xy^2 + 2z^2x = \lambda(2x)[/tex]

[tex]2x^2y - 2z^2y = \lambda(2y)[/tex]

[tex]2x^2z - 2y^2z = \lambda(2z)[/tex]

Do I just substitute these equations in turn into the constraint equation and determine what the points are that satisfy it?

Also, how do you find out if a point is a local max/min when you know the critical point when looking at a case like this?
 
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  • #2
You should be able to simplify those. Obviously, x= 0 makes the first of those equations true. If x is not 0, then the first equation is [itex]y^2+ z^2= \lamba[/itex].

Similarly, y= 0 satisfies the second equation. If y is not 0, then the second equation is [itex]x^2- z^2= \lamda[/itex]

z= 0 satisfies the third equation. If z is not 0, then the third equation is [itex]x^2- y^2= \lambda[/itex].

Setting [itex]x^2- z^2= x^2- y^2[/itex] gives [itex] z^2= y^2[/itex] or z= y. Similarly, setting [itex]y^2+ z^2= x^2- y^2[/itex] gives z= x. Can you finish from here?
 
  • #3
Ok, I can follow you up to a certain point.

For whenever x,y or z = 0, I get the following critical points:
(1,0,0)
(-1,0,0)
(0,1,0)
(0,-1,0)
(0,0,1)
(0,0,-1)

HallsofIvy said:
setting [itex]y^2+ z^2= x^2- y^2[/itex] gives z= x.

I get [itex] z^2 = \frac{x^2} {3} [/itex] here.

I then get a bit lost from now on. By substitution and a number of steps I get [itex] z = \frac {1} {\sqrt{5}} [/itex] and [itex] z = -\frac {1} {\sqrt{5}} [/itex]. Do I just substitute that in and get the critical points?
 
  • #4
hmm..you've missed out quite a few points:
Rewrite your equations as follows:
[tex]2x(y^{2}+z^{2}-\lambda)=0[/tex]
[tex]2y(x^{2}-z^{2}-\lambda)=0[/tex]
[tex]2z(x^{2}-y^{2}-\lambda)=0[/tex]
[tex]x^{2}+y^{2}+z^{2}=1[/tex]
If you set x=0, you get in addition to your own points the following 4:
[tex](0,\pm\frac{1}{\sqrt{2}},\pm\frac{1}{\sqrt{2}}), (\lambda=-\frac{1}{2})[/tex]

Having dealt with those, note that from combining the first and fourth equation, we must have:
[tex]x^{2}=1-\lambda[/tex]
(Replacing the first and eliminating x from further consideration)
[tex]2y(1-z^{2}-2\lambda)=0[/tex]
[tex]2z(1-y^{2}-2\lambda)=0[/tex]
[tex]y^{2}+z^{2}=\lambda[/tex]
You can now proceed further with these three equations.

If I've counted correctly, there are a total of 18 (x,y,z) solutions here, in addition to the 8 previous ones.
 
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  • #5
ElDavidas said:
Ok, I can follow you up to a certain point.

For whenever x,y or z = 0, I get the following critical points:
(1,0,0)
(-1,0,0)
(0,1,0)
(0,-1,0)
(0,0,1)
(0,0,-1)



I get [itex] z^2 = \frac{x^2} {3} [/itex] here.
Oops, you are right! Now you know y= z and x2= 3z2. Now put those into your constraint:
[itex]x^2+ y^2+ z^2= 3z^2+ z^2+ z^2= 5z^2= 1[/itex]
So, yex, [itex]z= \pm\frac{1}{\sqrt{5}}[/itex].

Now use y= z and x2= 3z2 to find corresponding values of x and y.
 
  • #6
FYI, the max/min is [itex]\pm\frac{1}{4}[/itex]
 
  • #7
Since [tex]f(x,z,y) = x^2z^2-z^2y^2 + y^2x^2 = x^2y^2-y^2z^2 + z^2x^2 = f(x,y,z)[/tex]

which is to say that f is symmetric in the variables y and z, doesn't it follow that the extrema will have y=z?
 
  • #8
benorin said:
Since [tex]f(x,z,y) = x^2z^2-z^2y^2 + y^2x^2 = x^2y^2-y^2z^2 + z^2x^2 = f(x,y,z)[/tex]

which is to say that f is symmetric in the variables y and z, doesn't it follow that the extrema will have y=z?
Nope; what follows is that for any particular extremum with one choice of y and z, there must also be another extremum with their values commuted.
The 26 critical points are:
[tex](\pm{1},0,0), (0,\pm{1},0), (0,0,\pm{1}),(\pm\sqrt{\frac{3}{5}},\pm\sqrt{\frac{1}{5}},\pm\sqrt{\frac{1}{5}}),(0,\pm\frac{1}{\sqrt{2}},\pm\frac{1}{\sqrt{2}}),(\pm\frac{1}{\sqrt{2}},0,\pm\frac{1}{\sqrt{2}}),(\pm\frac{1}{\sqrt{2}},\pm\frac{1}{\sqrt{2}},0)[/tex]
The last 12 are the extrema.
 

Related to Finding Extrema with Lagrange Multipliers

1. What are Lagrange multipliers and what are they used for?

Lagrange multipliers are a mathematical tool used to optimize a function subject to constraints. They are used to find the maximum or minimum value of a function while satisfying a set of constraints.

2. How do Lagrange multipliers work?

Lagrange multipliers work by converting a constrained optimization problem into an unconstrained one. This is done by introducing a new variable, known as the Lagrange multiplier, and using it to create a new function that includes both the original objective function and the constraints.

3. What are the conditions for using Lagrange multipliers?

The conditions for using Lagrange multipliers are:

  1. The objective function and constraints must be differentiable.
  2. The constraints must be independent.
  3. The constraints must be continuous.
  4. The constraints must be convex.
  5. The solution must be feasible.

4. Can Lagrange multipliers be used for both maximization and minimization problems?

Yes, Lagrange multipliers can be used for both maximization and minimization problems. The Lagrange multiplier method is a general method for solving constrained optimization problems, regardless of whether the objective function is to be maximized or minimized.

5. What are some real-world applications of Lagrange multipliers?

Lagrange multipliers are commonly used in physics, engineering, and economics to solve constrained optimization problems. They have applications in fields such as structural design, portfolio optimization, and control systems. They are also used in machine learning and data analysis for feature selection and dimensionality reduction.

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