Lagrange Multipliers: I'm having an issue with the process of lagrange multipliers. I know that the equations are: 1. g(x,y) = 0 2. fx = lambda gx 3. fy = lambda gy For some reason though, in this particular problem I get 2 different values for lambda so I'm kinda stuck. HELP! I'll attach the problem in the comments.
Here's my work: Constraint: 4x^2 + y^2 - 4 = 0 What to minimize/maximize: Distance formula sqrt(x^2 + y^2) To make things easier, we can max/min the square of the distance formula instead.
I hate these things I found the problem in the other one btw, if you want to check that post the notifications aren't working...
oh haha I'll take a look
ok that makes sense. any idea where to go with this one?
df/dx = 2x, df/dy = 2y, dg/dx = 8x, dg/dy = 2y
totally have to review LM's, so I'll need a minute I should eat first too....
hm.... sounds like a good idea. Where you at?
Guadalajara Mexico, you?
Chicago.
right on, I"m originally from Cali what's your major?
Undecided Engineering. Leaning Mechanical or Electrical. Studying at NU
cool, I'm probably going CS and Electrical Engineering in Minneapolis alright going to eat, see you in a bit!
ok I'll be here trying to figure these things out.
ok I'm ready to help, are you there?
yup
first of all. g(x,y)=k your partials are right, so by\[\nabla f(x,y,z)=\lambda\nabla g(x,y,z)\\g(x,y,z)=k\]we get the system\[8x=2\lambda x\\2y=2\lambda y\\4x^2+y^2=4\]we only need to solve for \(\lambda\) once, it doesn'tmatter that you get two different values for it if you try to solve with each equation (you actually can avoid finding lambda all together in some problems, but don't think in this one)
so use the first equation to solve for \(\lambda\) and \(x\)
From the first equation we get that lambda is 1/4, but how does that help us?
because the x's cancel...
lambda is 4... we can also solve for x, too
oh right. Lambda is 4. but how can we solve for x if it cancels out of the equation?
\[8x-2\lambda x=0\implies x=0,~\lambda=4\]
OHHH
it's the only possible solution...
so plug that value for x into the constraint and solve for y to get one set of critical points
so if x = 0 y = 2, -2 right?
right :) now we can use the second equation and \(\lambda=4\) to solve for y
then plug that back into the constraint, solve for x, and get another set of critical points
there we get that lambda is 1 and y is 0 right?
and if y = 0 , x = 1,-1
this is where lambda doesn't matter, if you use lambda=4 you get the same answer ;)
:P
that's why I say you can avoid solving for lambda at all sometimes funny little trick mr Lagrange came up with lol
so anyway....our critical points are (0,2), (0,-2), (1,0), (-1,0) and we just plug those into the original equation to figure out which are max's and which are min's. nice. thanks! you saved my rear end again....
no problem, you gave me a review again :) a fair trade in my book!
All's well that ends well.
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