Grouping constrained optimization

In summary, the conversation discusses a problem involving a set S of elements and a set V of possible groupings Gj. Each grouping is a subset of S and is associated with a weight wij. The problem is to find the set of mappings from S to V that minimize the sum of the associated weights, under the constraints that each element of S can be involved in either exactly one or no mapping. The problem arises from an application involving a planar graph and finding optimized oriented loops with no common oriented edge.
  • #1
Sebastien77
5
0
Hi all,

I am looking for an efficient solution to solve the following problem. Can anybody help?

Assume a set S of elements ki and a set V of possible groupings Gj. A grouping Gj is a subset of S. Associate a weight wij to each mapping ki to Gj. The weights are infinite if ki ⊄ Gj, and finite signed number if ki ⊂ Gj.

1) Find the set of mappings from S to V minimizing the sum of the associated weights under the constraint that each element of S can be involved in exactly one or no mapping.

2) Same question but by changing the constraint to "under the constraint that each element of S must be involved in exactly one mapping".

Note: For my application V is a minute fraction of all possible groupings of the elements of S.Sébastien
 
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  • #2
Sebastien77 said:
A grouping Gj is a subset of S.
Perhaps you mean that a grouping is a collection of subsets of S that partition it in some way. If you want a "grouping" of S merely to be a subset of S, you should just call it a subset.

Associate a weight wij to each mapping ki to Gj.
It isn't clear what set of mappings you are talking about. Let's consider one of these mappings. What is its domain and what is its codomain?

Does [itex] w_{ij} [/itex] depend only on the indices [itex]i,j [/itex] or can there be a different [itex] w_{i,j} [/itex] for each mapping?
 
  • #3
Yes, sorry. I hope the following will clarify:

S is a finite set of elements ki
V is a subset of S, e.g. v4={k1,k3}
E is a finite ensemble of V, e.g. E = { v1={k1}, v2={k1,k2}, v4={k1,k3}, v4={k2,k4,k5} }
f(S, V) → ]-∞,∞], (ki,vj) → wij, with wij infinite only if kivj.

The problem is to find the ensemble M of elements of E minimizing ∑ij wij computed over all elements of S and M, under one of the following two constraints:

Problem 1) each ki is member of one or no element of M
Problem 2) each ki is member of exactly one element of M (for this case we assume that at least a valid solution exists in E)
 
  • #4
Sebastien77 said:
Yes, sorry. I hope the following will clarify:

S is a finite set of elements ki
V is a subset of S, e.g. v4={k1,k3}
I think you mean that [itex] V [/itex] is a set whose members are each subsets of [itex] S [/itex]. Do you want them to be distinct subsets? For example, can we have [itex] v_4 = \{k_1, k_2\} [/itex] and [itex] v_5 = \{k_1, k_2\} [/itex]

E is a finite ensemble of V, e.g. E = { v1={k1}, v2={k1,k2}, v4={k1,k3}, v4={k2,k4,k5} }

f(S, V) → ]-∞,∞], (ki,vj) → wij, with wij infinite only if kivj.

Do you mean "infinite if and only if [itex] k_i \notin v_j [/itex]" ?
The problem is to find the ensemble M of elements of E minimizing ∑ij wij computed over all elements of S and M, under one of the following two constraints:

Problem 1) each ki is member of one or no element of M

In that context, I think "member of one" means "member of exactly one" (as opposed to "member of at least one").
Problem 2) each ki is member of exactly one element of M (for this case we assume that at least a valid solution exists in E)

If this problem arises from an application, it might help to tell about the application.
 
  • #5
Stephen Tashi said:
I think you mean that [itex] V [/itex] is a set whose members are each subsets of [itex] S [/itex]. Do you want them to be distinct subsets? For example, can we have [itex] v_4 = \{k_1, k_2\} [/itex] and [itex] v_5 = \{k_1, k_2\} [/itex]

Do you mean "infinite if and only if [itex] k_i \notin v_j [/itex]" ?

In that context, I think "member of one" means "member of exactly one" (as opposed to "member of at least one").

If this problem arises from an application, it might help to tell about the application.

Yes, I was inaccurate, they should be distinct subsets.
Yes, I meant "infinite if and only if [itex] k_i \notin v_j [/itex]", thanks for the correction.
Yes, "member of exactly one" (problem 2) and "member of exactly one or not member of any" (problem 1).
The elements of S are the edges of a planar graph and I am looking for an ensemble of oriented loops with no common oriented edge that would optimize a given metric.
 

1. What is grouping constrained optimization?

Grouping constrained optimization is a type of mathematical optimization technique used to solve problems that involve grouping or partitioning variables into distinct subsets. This approach is commonly used in economics, operations research, and computer science.

2. How does grouping constrained optimization differ from other optimization techniques?

Grouping constrained optimization differs from other optimization techniques in that it takes into account the grouping structure of the variables being optimized. This allows for more efficient and accurate solutions to problems that involve grouping or partitioning variables.

3. What are some real-world applications of grouping constrained optimization?

Grouping constrained optimization has a wide range of applications, including resource allocation, portfolio optimization, scheduling, and network routing. It is also commonly used in machine learning and data mining to solve problems such as feature selection and clustering.

4. What are the main challenges in solving grouping constrained optimization problems?

The main challenges in solving grouping constrained optimization problems include the complexity of the grouping structure, the large number of possible solutions, and the potential for conflicting objectives within different groups. Additionally, finding an optimal solution may require a significant amount of computational resources.

5. What are some techniques used to solve grouping constrained optimization problems?

Some common techniques used to solve grouping constrained optimization problems include branch and bound algorithms, genetic algorithms, and dynamic programming. Other approaches, such as Lagrangian relaxation and column generation, have also been used to tackle these types of problems.

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