Algorithm to solve a maximization problem

In summary: No, I'm not talking about the length of C_j. What I mean is that no vector in C_j would increase in value by substituting in bk, where j is a value listed in b's preferences about i. For example, if b1 was in C10, you could not switch it to C9 and increase its value (since 9 is ranked above 10).
  • #1
mathlete
151
0
I have two sets of vectors:

A: a1, a2, a3... an
B: b1, b2, b3... bm

n > m and n/m is an integer, p.

Each vector bi has ranked, in order of preference, a set of vectors from A. For example, b1 may "prefer" a1, a9, and a10. The only constraint on this set is that each vector ai from A appears only p total times across all these ranked sets.

The problem is to find the set of vectors, C, such that:

Ci = ai[tex]\sum b_k[/tex]

where:

- The sum of bk is a sum of p vectors from B that listed ai as a preferred vector
- No vector Cj would increase in value by substituting in bk, where j is a value listed in b's preferences about i. Using the example of b1 above, if b1 was in C10, you could not switch it to C9 and increase its value (since 9 is ranked above 10).

The last condition is the one that's giving me a problem. This seems close to, but not exactly, a maximization problem.

Simple example (too simple because each b vector lists all c vectors as preferences which makes it somewhat trivial to solve, but gives an idea of what I'm trying to do):

a1 = { 1, 2, 1 }
a2 = { 2, 1, 2 }

b1 = { 5, 1, 2} Lists preferences as: a2, a1
b2 = { 1, 5, 2} Lists preferences as: a1, a2
b3 = { 8, 0, 6} Lists preferences as: a2, a1
b4 = { 0, 9, 0} Lists preferences as: a2, a1

Answer: c1: a1 paired with b1 and b3; c2: a2 paired with b2 and b4
 
Last edited:
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  • #2
I don't understand your example since I don't see any a's listed in the example In the problem description you said each vector b listed a set of a' s that it preferred. But in the example, each b listed c's that it preferred.


Is it true that the lists of preferences for each the [tex] b_i [/tex] contain the same number of elements?
 
  • #3
Sorry, you're right -- I fixed the example. And yes, each b vector has the same number of preferences listed, but we don't necessarily know how many (though we can assume it's > p and < n).
 
  • #4
The example doesn't explain the meaning of [tex] C_i = a_i \sum b_k [/tex]
That formula seems to involve multiplying two vectors together. Is it supposed to be an inner product?
 
  • #5
Bad notation on my part. It's shorthand for the inner product, yes: ai*bk_1+ai*bk_2 + ... + ai*bk_p where the bk are chosen as stated above.
 
  • #6
What does it mean to say "no vector [tex] C_j [/tex] would increase"? Are you talking about the length of [tex] C_j [/tex] ? -- that would be the square root of the sum of the squares of its components.
 

What is an algorithm to solve a maximization problem?

An algorithm to solve a maximization problem is a step-by-step procedure or set of rules for finding the maximum value of a given function or set of variables. It is used in mathematical and scientific fields to optimize resources and make decisions based on desired outcomes.

How does an algorithm to solve a maximization problem work?

An algorithm to solve a maximization problem typically involves iteratively testing different values or combinations of variables to find the maximum value of the function. The algorithm may also involve constraints or limitations on the variables to ensure the solution is feasible.

What are some common techniques used in algorithms to solve maximization problems?

Some common techniques used in algorithms to solve maximization problems include linear programming, dynamic programming, and greedy algorithms. These techniques involve different approaches for optimizing resources and making decisions based on desired outcomes.

Can an algorithm to solve a maximization problem find the global maximum?

It depends on the problem and the specific algorithm being used. In some cases, an algorithm may be able to find the global maximum, which is the absolute highest value of the function. However, in other cases, the algorithm may only be able to find a local maximum, which is the highest value within a specific region of the function.

Are there any limitations or potential issues with algorithms to solve maximization problems?

Like any algorithm, those used to solve maximization problems may have limitations or potential issues. These can include computational complexity, sensitivity to initial conditions, and the potential for suboptimal solutions. It is important for scientists to carefully consider these limitations when choosing an algorithm for a specific problem.

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