Optimization with integers as results

In summary, the conversation discusses the problem of finding integer values for A, B, and C in a quadratic function that fits a given dataset (X, Y). The speaker suggests a possible approach using a minimization problem and also mentions the challenges of combinatorial optimization. They also mention the use of heuristics, such as relaxation, to find a good solution. The conversation ends with a question about the specific problem at hand and the definition of "fitting" a dataset to a function.
  • #1
maistral
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Say for example I have a dataset (X, Y) which I need to fit to the function y = Ax^2 + By + Cxy.

How do I retrieve values of A, B, and C such that they can only be integers? As of now I'm doing grid search which is so taxing.
 
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  • #2
What does "I have" mean? Do you have a data set you already know, it satisfies such an equation? Do you want the best fit, and if, what is your measure for "best"? Which domains are we talking about? E.g. if all data are some finite decimal numbers, then you could simply multiply with all denominators.
 
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  • #3
maistral said:
Say for example I have a dataset (X, Y) which I need to fit to the function y = Ax^2 + By + Cxy.

How do I retrieve values of A, B, and C such that they can only be integers? As of now I'm doing grid search which is so taxing.
Could perhaps be an interesting (diophantine) minimization problem: Given a set of points ##\{x_i,y_i\}##,
find ##(A,B,C)\in\mathbb{Z}## such that some error metric such as:
$$
\int_a^b \big|g(x)-h(x)\big|dx
$$
is minimum where say ##g(x)## is a (relatively) good or good enough fit of the data and ##h(x)=Ax^2+Bx+C##? Seems to me there would always exist a minimum solution to such a problem. Wouldn't think such an exhaustive search of a reasonably-bounded integer 3D space (n,m,p) would take very long on a fast machine.
 
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  • #4
You've just discovered by combinatorial optimization is considered a "hard" problem. In general searching for an optimum integer solution takes a very long time as you have discovered.

If you can accept merely a pretty-good solution, there are heuristics you can apply. One of them is "relaxation". Solve the problem in real numbers, then round the resulting values to integers. This will in general not give an optimal solution but in many cases you can prove that it is within some bound relative of the optimum solution (for instance, no more than 30% worse).

These are general comments. I can't quite understand what your particular problem is. I don't know what it means to fit a dataset to a function. Do you just mean you have a bunch of (x, y) values and you're trying to find the best fitting quadratic with integer coefficients?
 

FAQ: Optimization with integers as results

What is optimization with integers as results?

Optimization with integers as results is a mathematical process that involves finding the best possible solution to a problem, where the solution is restricted to be an integer value. This type of optimization is often used in real-world scenarios where the solution needs to be a whole number, such as in resource allocation or scheduling problems.

How is optimization with integers as results different from regular optimization?

The main difference between optimization with integers as results and regular optimization is that the solution must be a whole number in the former, while in the latter, the solution can be any real number. This added constraint makes the problem more challenging and requires specialized techniques to find the optimal solution.

What are some common techniques used in optimization with integers as results?

Some common techniques used in optimization with integers as results include branch and bound, dynamic programming, and integer linear programming. These methods involve breaking down the problem into smaller subproblems and using mathematical algorithms to find the optimal solution.

What are some applications of optimization with integers as results?

Optimization with integers as results has many practical applications, such as in production planning, inventory management, and project scheduling. It is also used in various fields of science, including engineering, economics, and computer science, to solve complex problems and improve decision-making processes.

How can I learn more about optimization with integers as results?

There are many resources available to learn about optimization with integers as results, including textbooks, online courses, and research papers. Additionally, there are various software tools and libraries that can help with solving these types of problems. It is also helpful to have a strong understanding of mathematical concepts such as linear programming, combinatorics, and graph theory.

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