Optimisation using constraints

In summary, we are using Lagrange multipliers to find the highest and lowest points on the curve of intersection between an elliptic paraboloid and a right circular cylinder. The Lagrangian is set up and equations for Lx and Ly are derived. However, we encounter a problem with the equation involving λ and need to explore both possibilities for y. The answer of x = 1/3, y = +-0.94 is obtained, but there is doubt about its correctness and the method is suggested to be tried directly as well. It is noted that the y=0 case must also be considered.
  • #1
lagwagon555
60
1

Homework Statement



Consider the intersection of two surfaces: an elliptic paraboloid
z = x2 + 2x + 4y2 and a right circular cylinder x2 + y2 = 1. Use Lagrange multipliers to find
the highest and lowest points on the curve of intersection

The Attempt at a Solution



L = x^2 + 2x + 4y^2 - λ(x^2 + y^2 - 1)
Lx = 2x + 2 - 2λx = 0
Ly = 8y - 2λy = 0

Rearranging gives the bizarre result of λ = 4. Aren't I supposed to be able to eliminate the lagrange multipliers by it giving me λ in terms of y? It's giving me a value instead.

I ran with this anyway, and got the answer of x = 1/3, y = +-0.94, but I don't think this is correct. I suspect I'm doing something wrong algebraically? Any help hugely appreciated :)
 
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  • #2
lagwagon555 said:
I suspect I'm doing something wrong algebraically? Any help hugely appreciated :)

You need to be careful with the last equation. I believe you divided by y, but that's valid only if y isn't zero. You need to explore both possibilities. By the way, you forgot an equation. What about Lλ?

What makes you think you have the wrong answer?
 
Last edited:
  • #3
If you don't trust the method try it directly as well. Use the constraint to eliminate y^2 in z. Now look at the extrema for x in [-1,1]. You do need to consider the y=0 case as well.
 

Related to Optimisation using constraints

1. What is optimisation using constraints?

Optimisation using constraints is a mathematical approach to finding the best possible solution to a problem while taking into account certain limitations or restrictions, known as constraints. It involves using various techniques and algorithms to optimize a particular objective function while ensuring that it satisfies all the given constraints.

2. What types of problems can be solved using optimisation with constraints?

Optimisation with constraints can be used to solve a wide range of problems in various fields such as engineering, economics, finance, and management. Some common examples include production planning, resource allocation, scheduling, and portfolio optimization.

3. How does optimisation with constraints differ from regular optimisation?

The main difference between optimisation with constraints and regular optimisation is that the former takes into account the limitations or restrictions imposed by the given constraints, while the latter does not. This means that in optimisation with constraints, the solution must not only optimize the objective function but also satisfy all the constraints, making it a more complex and challenging problem to solve.

4. What are some commonly used techniques in optimisation with constraints?

Some commonly used techniques in optimisation with constraints include linear programming, integer programming, dynamic programming, and nonlinear programming. These techniques use different algorithms and mathematical models to find the best possible solution to a problem while satisfying all the given constraints.

5. What are the benefits of using optimisation with constraints?

There are several benefits of using optimisation with constraints, including increased efficiency and cost savings, improved decision-making, and better resource allocation. By taking into account the limitations and restrictions, optimisation with constraints can help find the most optimal and feasible solution to a problem, leading to improved outcomes and results.

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