Discrete Optimization Problem?

In summary, the expression A = M! / (r1! * r2!) is considered, where M = r1 + r2 and r1 = M - 2r2. The maximum value of A occurs when r2 = 1/2 * r1. This can be determined by examining the ratio R(r) = A(r+1) / A(r) and observing when it goes from R>1 to R<1 as r increases. However, if M is odd, the solution will be at the two neighboring integers to M/2.
  • #1
iScience
466
5
Consider the expression:$$A = \frac{ M! }{ r_1!\ r_2! }$$

where [itex] M = r_1 + r_2 [/itex],
where [itex] r_1 = (M - 2r_2) [/itex]

$$A = \frac{ (r_1 + r_2)! }{ r_1!\ r_2! } \\ \ \\ \
= \frac{ ((M-2r_2) + r_2)! }{ (M-2r_2)!\ (r_2)! } \\ \ \\ \
= \frac{ (M-r_2)! }{ (M-2r_2)!\ r_2! }

$$

Then, for a given M, A maximum occurs at : [itex] r_2 = \frac{1}{2}r_1 [/itex].
I know this because I can generate a table algorithmically. But I'd like to know how to arrive here analytically. Any pointers would be appreciated.
 
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  • #2
Given that ##M = r_1 + r_2## and ##r_1 = (M - 2r_2)##, then if we substitute the second expression for ##M## in the first expression, we get ##M = M - r_2##, implying that ##r_2 = 0##. Then ##A ≡ 1##.
 
  • #3
tnich said:
Given that ##M = r_1 + r_2## and ##r_1 = (M - 2r_2)##, then if we substitute the second expression for ##M## in the first expression, we get ##M = M - r_2##, implying that ##r_2 = 0##. Then ##A ≡ 1##.
Maybe you meant to say ##r_1 = M - r_2##
 
  • #4
iScience said:
Consider the expression:$$A = \frac{ M! }{ r_1!\ r_2! }$$

where [itex] M = r_1 + r_2 [/itex],
where [itex] r_1 = (M - 2r_2) [/itex]

$$A = \frac{ (r_1 + r_2)! }{ r_1!\ r_2! } \\ \ \\ \
= \frac{ ((M-2r_2) + r_2)! }{ (M-2r_2)!\ (r_2)! } \\ \ \\ \
= \frac{ (M-r_2)! }{ (M-2r_2)!\ r_2! }

$$

Then, for a given M, A maximum occurs at : [itex] r_2 = \frac{1}{2}r_1 [/itex].
I know this because I can generate a table algorithmically. But I'd like to know how to arrive here analytically. Any pointers would be appreciated.

For
$$ A(r) = \frac{M!}{r! (M-r)!},$$
look at the ratio
$$R(r) = \frac{A(r+1)}{A(r)}$$
and examine when ##R(r)## goes from ##R>1## to ##R < 1## as ##r## increases.

BTW: having ##r_1 = \frac{M}{2}## is possible only if ##M## is even; if ##M## is odd, the solution will be at the two neighboring integers to ##M/2.##
 

1. What is a discrete optimization problem?

A discrete optimization problem is a type of mathematical problem where the goal is to find the best possible solution from a finite set of options. This means that the solution must be chosen from a limited number of possibilities, rather than being a continuous range of options.

2. What are some common examples of discrete optimization problems?

Some common examples of discrete optimization problems include the knapsack problem, the traveling salesman problem, and the job scheduling problem. These problems can arise in various fields such as computer science, operations research, and engineering.

3. What are the key differences between discrete and continuous optimization problems?

The main difference between discrete and continuous optimization problems is the nature of the solution space. In discrete optimization, the solution space is finite and consists of a discrete set of options, while in continuous optimization, the solution space is infinite and can take on any value within a given range.

4. What are some common methods for solving discrete optimization problems?

Some common methods for solving discrete optimization problems include dynamic programming, branch and bound, and heuristic algorithms. These methods use different approaches to search through the solution space and find the optimal solution.

5. What are the real-world applications of discrete optimization problems?

Discrete optimization problems have many real-world applications, such as in logistics and supply chain management, resource allocation, scheduling, and network design. They are also used in various industries, including transportation, manufacturing, and telecommunications, to improve efficiency and reduce costs.

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