Can Lagrange's Undetermined Multiplier Have Imaginary Points?

In summary, the conversation is about a problem involving Lagrange's undetermined multiplier and finding the points closest to the origin. The formula d(f + λg) = 0 is used and an error is identified where x = y. The conversation ends with the person seeking help to understand if imaginary points can be closest to the origin.
  • #1
KaGa
2
0
Hey guys, new here and this is my first post. Wondering if anyone could help me.
So I've encountered a problem on Lagrange's undetermined multiplier. Usually i have no problem with these, but this one caught me off a little.

g(x,y) = x^2 + y^2 - 4xy - 6 = 0
Find the points closest to the origin.
With this in mind:
f(x,y) = x^2 + y^2
Using the formula:
d(f + λg) = 0
Let (f + g) = F
F_x = 2x + 2λx -λ4y
F_y = 2y + 2λy -λ4x
By inspection you can see x = y, so into g(x,y) and...
-2x^2-6=0
x^2 = -3

∴ x = √-3

I haven't encountered an imaginary point yet in this type of question, and since i can't quite make sense of it in my mind, i was wondering if anyone could help me? Have I made an error somewhere, or can you have imaginary points closest to the origin?

Cheers.
 
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  • #2
KaGa said:
By inspection you can see x = y

That's your error. Check the signs!
 
  • #3
AlephZero said:
That's your error. Check the signs!

KaGa said:
F_x = 2x + 2λx -λ4y
F_y = 2y + 2λy -λ4x

I only say by inspection to save writing out the rest of the calculation but I did try it.

2x + λ(2x - 4y) = 0
2y + λ(2y - 4x) = 0

λ= -2x/(2x - 4y) = -2y/(2y - 4x)
[-2x/(2x - 4y) = -2y/(2y - 4x)] *-1
2x/(2x - 4y) = 2y/(2y - 4x)
[2x/(2x - 4y) = 2y/(2y - 4x)]/2
x/(2x - 4y) = y/(2y - 4x)
rearrange:
x(2y - 4x) = y(2x - 4y)
2xy -4x^2 = 2xy -4y^2
-4x^2=-4y^2
y^2 = x^2
y = x.

Unless I'm making a basic floor I'm not seeing.
 
  • #4
##y = x## is not the only solution of ##y^2 = x^2##.
 
  • #5


I can say that Lagrange's undetermined multiplier method can indeed result in imaginary points. This is because the method involves finding the critical points of a function, which can include complex numbers. In your example, the point closest to the origin may indeed be an imaginary point, which would mean that the minimum distance to the origin is a complex number. This is not uncommon in optimization problems, and it simply means that the solution lies on the imaginary axis rather than the real axis. It is important to remember that imaginary numbers are a valid part of mathematics and can have real-world applications. So while it may seem unfamiliar, it is a valid result in this case. I would recommend double-checking your calculations and if you are still unsure, seeking further assistance from a mathematics expert.
 

FAQ: Can Lagrange's Undetermined Multiplier Have Imaginary Points?

What is Lagrange's undetermined multiplier?

Lagrange's undetermined multiplier is a mathematical concept used in constrained optimization problems. It allows for the incorporation of constraints into the objective function, making it easier to find the optimum solution.

Can Lagrange's undetermined multiplier have imaginary points?

Yes, it is possible for Lagrange's undetermined multiplier to have imaginary points. This can happen when the objective function and constraints involve complex numbers.

How does Lagrange's undetermined multiplier work?

Lagrange's undetermined multiplier works by introducing a new variable, called the multiplier, to the objective function. This variable is then used to incorporate the constraints into the objective function, making it possible to find the optimum solution.

When is Lagrange's undetermined multiplier used?

Lagrange's undetermined multiplier is used in constrained optimization problems, where the objective function needs to be optimized subject to certain constraints. It is commonly used in economics, engineering, and physics.

What are the limitations of Lagrange's undetermined multiplier?

Lagrange's undetermined multiplier can only be used for problems with differentiable objective functions and constraints. It also assumes that the constraints are independent, which may not always be the case. Additionally, it may not always provide a unique solution.

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