Compactness of a set of feasible solutions

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In summary, the speaker is working on an optimization problem in Operations Research and needs to prove the compactness of a set related to a property. They explain the constraints for the set of feasible solutions and ask for assistance in proving its compactness. Another person suggests using the Bolzano-Weierstrass theorem.
  • #1
gjones89
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Hi everyone,

I am working on a problem in Operations Research but I need to prove a property related to compactness of a set. Although I expect it is quite elementary, I have never studied Analysis at an advanced level so am not sure how to do it.

I have an optimisation problem in which a feasible solution may be expressed as a set of real numbers [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] which satisfies the following constraints:

[itex]\sum\limits_{i=1}^n \lambda_i\leq \lambda[/itex] (here [itex]\lambda[/itex] is a positive real number),

[itex]\lambda_i\geq 0[/itex] for all [itex]i\in\{1,2,...,N\}[/itex].

The problem is I need to prove that the set of feasible solutions satisfying the above constraints is a compact set (closed and bounded), because this will enable me to prove that an optimal solution exists to the optimisation problem I am working on. I am sure this is probably quite standard and perhaps someone might be able to point me towards a theorem somewhere which will give me what I need, or just provide an outline of the proof if it is simple.

Thanks a lot!
 
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  • #2
If the solution set [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] consists of a finite set of points, then it is certainly compact. The closed interval ##[0,\lambda]## that contains all possible solutions is also a compact space.
 
  • #3
Hi,

Sorry, I think I may not have explained it well enough. By 'set of feasible solutions' I mean the set of all sets [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] satisfying the constraints [itex]\sum_{i=1}^n \lambda_i\leq \lambda[/itex] and [itex]\lambda_i\geq 0[/itex] for all [itex]i\in\{1,2,...,N\}[/itex]. This set will obviously be infinite (and uncountable), as there are infinitely many possible combinations [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] that satisfy these constraints.

To put it another way, I am trying to prove compactness of the following set:

[itex]\left\{(\lambda_1,\lambda_2,...,\lambda_n)\in\mathbb{R}^n:\sum\limits_{i=1}^n \lambda_i\leq \lambda\text{ and }\lambda_i\geq 0\text{ for all }i\right\}.[/itex]

where [itex]\lambda[/itex] is a positive real number.

Thanks for your help!
 
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  • #4
gjones89 said:
Hi,

Sorry, I think I may not have explained it well enough. By 'set of feasible solutions' I mean the set of all sets [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] satisfying the constraints [itex]\sum_{i=1}^n \lambda_i\leq \lambda[/itex] and [itex]\lambda_i\geq 0[/itex] for all [itex]i\in\{1,2,...,N\}[/itex]. This set will obviously be infinite (and uncountable), as there are infinitely many possible combinations [itex]\{\lambda_1,\lambda_2,...,\lambda_n\}[/itex] that satisfy these constraints.

To put it another way, I am trying to prove compactness of the following set:

[itex]\left\{(\lambda_1,\lambda_2,...,\lambda_n)\in\mathbb{R}^n:\sum\limits_{i=1}^n \lambda_i\leq \lambda\text{ and }\lambda_i\geq 0\text{ for all }i\right\}.[/itex]

where [itex]\lambda[/itex] is a positive real number.

Thanks for your help!

It's just Bolzano-Weierstrass, isn't it? Closed and bounded.
 

1. What is the definition of compactness in relation to a set of feasible solutions?

Compactness refers to the property of a set of feasible solutions where all the points in the set are close to each other, without any gaps or holes. In other words, a compact set has no outliers or extreme values, and all the points are relatively close together.

2. How is compactness of a set of feasible solutions measured?

The compactness of a set of feasible solutions can be measured using different metrics, such as the diameter of the set, the distance between the farthest points, or the average distance between points. There is no single standard measure of compactness, and it can vary depending on the context of the problem.

3. What is the significance of compactness in problem-solving and decision-making?

Compactness is an important consideration in problem-solving and decision-making as it reflects the level of efficiency and optimality of a set of solutions. A more compact set of solutions indicates that there are fewer redundant or unnecessary options, leading to a more efficient and effective decision-making process.

4. Can a set of feasible solutions be both compact and optimal?

Yes, a set of feasible solutions can be both compact and optimal. In fact, in many cases, a compact set of solutions is desirable as it often leads to faster and more accurate decision-making. However, there may be situations where a non-compact set of solutions is more appropriate, depending on the specific problem and constraints.

5. How can the compactness of a set of feasible solutions be improved?

The compactness of a set of feasible solutions can be improved by using techniques such as data reduction, clustering, or optimization algorithms. These methods can help identify and eliminate redundant or irrelevant solutions, resulting in a more compact set of feasible options.

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