Unbounded Feasible Region for Lagrange with Two Constraints

In summary: But there must be a maximum value, at least.Right, and because the feasible region (the set of allowed ##(x,y,z)## values) is unbounded. We can find feasible points ##(x,y,z)## with ##x,z \to -\infty,\: y \to +\infty## (and opposite); and of course, ##f \to +\infty## for such points.But there must be a maximum value, at least.
  • #1
Kaura
122
22

Homework Statement



kuGWwwg.jpg


Homework Equations



Partials for main equation equal the respective partials of the constraints with their multipliers

The Attempt at a Solution



UmkCpuX.jpg


Basically I am checking to see if this is correct
I am pretty sure that 25/3 is the minimum but I am not sure how to find the maximum
The max an min at the bottom can be ignored or replaced with minimum
I have a lot to do today to prepare for midterms so any help would be much appreciated
 
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  • #2
Kaura said:

Homework Statement



kuGWwwg.jpg


Homework Equations



Partials for main equation equal the respective partials of the constraints with their multipliers

The Attempt at a Solution



UmkCpuX.jpg


Basically I am checking to see if this is correct
I am pretty sure that 25/3 is the minimum but I am not sure how to find the maximum
The max an min at the bottom can be ignored or replaced with minimum
I have a lot to do today to prepare for midterms so any help would be much appreciated

Your final solution looks OK, but I did not check the rest because I generally do not look at solutions given as posted images.

You should think about why your solution method does not give you a maximum.
 
Last edited:
  • #3
So is 25/3 the only extrema and a minimum?
 
  • #4
Kaura said:
So is 25/3 the only extrema and a minimum?
You tell me. But more importantly, what is the reason?
 
  • #5
Ray Vickson said:
You tell me. But more importantly, what is the reason?

Yes? because the function is not bound and is continuous?
 
Last edited:
  • #6
Kaura said:
Yes? because the function is not bound and is continuous?

Right, and because the feasible region (the set of allowed ##(x,y,z)## values) is unbounded. We can find feasible points ##(x,y,z)## with ##x,z \to -\infty,\: y \to +\infty## (and opposite); and of course, ##f \to +\infty## for such points.
 

What is the Lagrange multiplier method?

The Lagrange multiplier method is a mathematical technique used to find the maximum or minimum value of a function subject to constraints. It involves using a Lagrange multiplier, which is a constant, to incorporate the constraints into the function and then solving for the maximum or minimum value.

What is the difference between Lagrange with one constraint and Lagrange with two constraints?

The main difference between Lagrange with one constraint and Lagrange with two constraints is the number of constraints that are included in the function. Lagrange with one constraint involves only one constraint, while Lagrange with two constraints incorporates two constraints into the function.

How is the Lagrange multiplier calculated in Lagrange with two constraints?

In Lagrange with two constraints, the Lagrange multiplier is calculated by taking the partial derivative of the function with respect to each constraint and setting them equal to each other. This will give you the value of the Lagrange multiplier, which is used to find the maximum or minimum value of the function.

What is the significance of the Lagrange multiplier in Lagrange with two constraints?

The Lagrange multiplier in Lagrange with two constraints is a constant that helps incorporate the constraints into the function. It allows for the constraints to be taken into account when finding the maximum or minimum value of the function, which would not be possible without the Lagrange multiplier.

What are some real-life applications of Lagrange with two constraints?

Lagrange with two constraints has many real-life applications, such as in economics, engineering, and physics. It can be used to optimize production processes, minimize costs, and find the most efficient solutions to problems with multiple constraints. It is also commonly used in game theory to find the optimal strategies for players in a game.

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