Optimizing Multivariable Functions with Lagrange Multipliers

In summary, you can take the gradient of f and set it equal to 0 to get three equations. From those equations, you can find the value of x, y, and z.
  • #1
reklaws89
6
0
We're suppose to minimize f(x,y,z)=x^2+y^2+z^2 subject to 2x+y+2z=9.

I only ever remember learning how to do f(x,y) would it be the same equation? Thus, f(x,y,[tex]\lambda[/tex]) = f(x,y) + [tex]\lambda[/tex] g(x,y)? Meaning f(x,y,z,[tex]\lambda[/tex]) = x^2+y^2+z^2 + [tex]\lambda[/tex] (2x+y+2z-9) and then continue solving for each variable from there?

Any help?
 
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  • #2
"Lagrange Multipliers" doesn't apply to a single equation.

"Lagrange Multipliers" says that at a min or max of f(x,y,z), subject to the additional condition that g(x,y,z)= constant, the two gradients must be parallel: one is a multiple of the other. [itex]\nabla f= \lambda \nabla g[/itex]. That is the same as saying [itex]\nabla \left(f(x,y,z)+ \lambda g(x,y,z)\right)= 0[/itex] for all [itex]\lambda[/itex] s what you suggest will work.

Please do not post the same thing more than once. I have merged your two posts.
 
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  • #3
so you're saying that if i use f(x,y,z,[tex]\lambda[/tex]) = x^2+y^2+z^2 + [tex]\lambda[/tex] (2x+y+2z-9) that i will be able to find the value of each after taking partial derivatives of each variable. So would I just have three points?

p.s. - Sorry!
 
  • #4
reklaws89 said:
so you're saying that if i use f(x,y,z,[tex]\lambda[/tex]) = x^2+y^2+z^2 + [tex]\lambda[/tex] (2x+y+2z-9) that i will be able to find the value of each after taking partial derivatives of each variable. So would I just have three points?

p.s. - Sorry!
Yes, take the gradient of f, set it equal to 0 and the three partial derivatives give you three equations to solve for x, y, z (eliminating [itex]\lambda[/itex], reduces that to 2 equations but you also know 2x+ 2y+ 2z= 9.)

I'm not sure what you mean by "just have three points". There is one point on that plane that minimizes f.
 
  • #5
Yea, I misspoke. I meant one point with three numbers meaning a x-value, y-value, and z-value.
 
  • #6
HallsofIvy said:
Yes, take the gradient of f, set it equal to 0 and the three partial derivatives give you three equations to solve for x, y, z (eliminating [itex]\lambda[/itex], reduces that to 2 equations but you also know 2x+ 2y+ 2z= 9.)

what do you mean eliminating [itex]\lambda[/itex] and reducing to two equations.

f_x = 2x+2[itex]\lambda[/itex]
f_y = 2y+[itex]\lambda[/itex]
f_z = 2z+2[itex]\lambda[/itex]
2_[itex]\lambda[/itex] = 2x+y+2z-9

and from there find the value of each and put them back into f(x,y,z)=x^2+y^+z^2
 
  • #7
YOu haven't set them equal to 0 yet! If [itex]2x+ 2\lambda= 0[/itex] and [itex]2z+ 2\lambda= 0[/itex], what do you get when you eliminate [itex]\lambda[/itex]?
 

Related to Optimizing Multivariable Functions with Lagrange Multipliers

What are Lagrange multipliers?

Lagrange multipliers are a mathematical technique used to optimize a function subject to constraints. They allow for the identification of the maximum or minimum value of a function while satisfying certain constraints.

How do Lagrange multipliers work?

Lagrange multipliers work by incorporating constraints into the objective function through the use of a Lagrange multiplier, which is a constant multiplier added to the objective function. This creates a new function that can be optimized using traditional methods.

Why are Lagrange multipliers useful?

Lagrange multipliers are useful because they allow for the optimization of a function subject to constraints, which is a common problem in many fields of science and engineering. They also provide a more efficient and accurate solution compared to other methods of optimization.

What are some applications of Lagrange multipliers?

Lagrange multipliers have numerous applications in fields such as physics, economics, engineering, and computer science. They can be used to solve optimization problems in areas such as mechanics, economics, and machine learning.

What are the limitations of Lagrange multipliers?

One limitation of Lagrange multipliers is that they can only be used for problems with continuous functions and constraints. They also may not always provide a global optimum solution, as they only identify local extrema. Additionally, they can become computationally expensive for high-dimensional problems.

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