How Do You Solve a Constrained Optimization Problem on a Unit Sphere?

In summary: WVzIHRoaXMgcHJldmlvdXMgYWN0aW9uLCBwbGVhc2UgYmV0dGVyIGEgdW5pdCBzcGVlIn summary, the conversation is discussing the temperature of a point on a unit sphere and its partial derivatives. The speaker mentions that the equation of a unit sphere will be a constraint and uses a theorem to find the critical points. They then ask for clarification on the next steps and receive a suggestion to explore the possibilities of λ=0 and x=z.
  • #1
notnottrue
10
0

Homework Statement


The temperature of a point on a unit sphere, centered at the origin, is given by
T(x,y,y)=xy+yz

Homework Equations


I know that the equation of a unit sphere is x^2+y^2+x^2=1, which will be the constraint.


The Attempt at a Solution


The partial derivatives of T are y, x+z and y respectively.
Unit circle partial derivatives are 2x, 2y and 2z.

From a theorem in the lecture notes∇T(x,y,z)=[itex]\lambda[/itex]∇G(x,y,z)
G being the constraint. With the critical points when these equal 0.

So I get y-[itex]\lambda[/itex]2x=0
x+z-[itex]\lambda[/itex]2y=0
y-[itex]\lambda[/itex]2z=0
with [itex]\lambda[/itex]2x=[itex]\lambda[/itex]2z

Firstly, am I on the right track? If so, what is the next move?
Thanks
 
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  • #2
notnottrue said:

Homework Statement


The temperature of a point on a unit sphere, centered at the origin, is given by
T(x,y,y)=xy+yz

Homework Equations


I know that the equation of a unit sphere is x^2+y^2+x^2=1, which will be the constraint.


The Attempt at a Solution


The partial derivatives of T are y, x+z and y respectively.
Unit circle partial derivatives are 2x, 2y and 2z.

From a theorem in the lecture notes∇T(x,y,z)=[itex]\lambda[/itex]∇G(x,y,z)
G being the constraint. With the critical points when these equal 0.

So I get y-[itex]\lambda[/itex]2x=0
x+z-[itex]\lambda[/itex]2y=0
y-[itex]\lambda[/itex]2z=0
with [itex]\lambda[/itex]2x=[itex]\lambda[/itex]2z

Firstly, am I on the right track? If so, what is the next move?
Thanks

Well, you haven't actually stated the question, so I would start with that...
 
  • #3
notnottrue said:

Homework Statement


The temperature of a point on a unit sphere, centered at the origin, is given by
T(x,y,y)=xy+yz

Homework Equations


I know that the equation of a unit sphere is x^2+y^2+x^2=1, which will be the constraint.


The Attempt at a Solution


The partial derivatives of T are y, x+z and y respectively.
Unit circle partial derivatives are 2x, 2y and 2z.

From a theorem in the lecture notes∇T(x,y,z)=[itex]\lambda[/itex]∇G(x,y,z)
G being the constraint. With the critical points when these equal 0.

So I get y-[itex]\lambda[/itex]2x=0
x+z-[itex]\lambda[/itex]2y=0
y-[itex]\lambda[/itex]2z=0
with [itex]\lambda[/itex]2x=[itex]\lambda[/itex]2z

Firstly, am I on the right track? If so, what is the next move?
Thanks

The last equality implies either (a) λ = 0; or (b) x = z. Try to see what else happens in both cases (a) and (b).

RGV
 

Related to How Do You Solve a Constrained Optimization Problem on a Unit Sphere?

1. What is Constrained Optimisation?

Constrained optimisation is a mathematical method used to find the optimal value of a function given a set of constraints. It involves maximizing or minimizing a function while taking into account limitations or restrictions on the variables.

2. What are some examples of Constrained Optimisation problems?

Some examples of constrained optimisation problems include maximizing profits while minimizing costs, finding the shortest route between two points while avoiding certain obstacles, and determining the best allocation of resources to achieve a desired outcome.

3. How is Constrained Optimisation different from Unconstrained Optimisation?

Constrained optimisation takes into account limitations or restrictions on the variables, while unconstrained optimisation does not have any such restrictions. This makes constrained optimisation more complex as it involves finding an optimal solution within a given set of constraints.

4. What are the common techniques used in Constrained Optimisation?

Some common techniques used in constrained optimisation include linear programming, nonlinear programming, and dynamic programming. Other methods such as genetic algorithms and simulated annealing may also be used depending on the problem.

5. How is Constrained Optimisation used in real-life applications?

Constrained optimisation is used in a wide range of real-life applications, such as financial portfolio management, resource allocation, supply chain optimization, and machine learning. It is also commonly used in engineering, economics, and operations research to solve complex problems and make data-driven decisions.

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