"The Operation Combination Problem"

In summary: As a mathematical problem, it's completely uninteresting.In summary, the "Operation Combination Problem" involves finding all possible ways to combine integer numbers to achieve a given result, using only basic mathematical operations and without considering operation precedence. This problem can be approached by exploring the "solution space," which can be compared to exploring the different possible topologies of the universe. The problem may be of interest to mathematicians and computer scientists for its potential applications in combinatorial search techniques and solution space exploration. However, as a purely mathematical problem, it may not be of much interest.
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TL;DR Summary
Education question
"The Operation Combination Problem"

In the "Operation Combination Problem," we explore the space of possible solutions, much like we explore the different possible topologies for the universe. The problem consists of finding all possible combinations of mathematical operations that, when applied to a finite set of integer numbers from 1 to n, produce a given result R.

To solve this problem, we must navigate this "solution space" using the following constraints and specifications:

  1. Use only the operations of addition, subtraction, multiplication, and division.
  2. Each integer number from 1 to n must be used exactly once.
  3. Operations must be performed from left to right, without considering operation precedence (i.e., without applying the "PEMDAS" or "BIDMAS" rule).
By exploring this "solution space," we seek to uncover the different possible "configurations" for combining the integer numbers, just as we seek to understand the different possible topologies of the universe.

For example, if n = 3 and R = 4, one possible combination within this "solution space" might be: (1 × 2) + 3 = 4. This combination represents a specific way of combining the integer numbers from 1 to 3 to achieve the result 4, just as a specific spatial curvature represents a specific way of configuring space in the universe.

SUMMARY:

The problem involves finding all possible ways to combine integer numbers from 1 to n, using only the operations of addition, subtraction, multiplication, and division, to achieve a specific result R. Each integer number must be used exactly once, and operations must be performed from left to right without considering operation precedence.

The goal is to navigate the "solution space" to uncover all possible combinations that yield the result R. The complexity of the problem depends on the chosen values of n and R and can be approached using combinatorial search techniques and solution space exploration.

Side note:

The problem may be of interest to mathematicians and computer scientists, who might propose more efficient approaches to solving it.
 
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The relation is
[tex](((...(a_1b_1a_2)b_2a_3)b_3a_4)....)b_{n-1}a_n=R[/tex]
where
[tex]1\leq i \leq n, \{a_i\}=\{1,2,3,...,n\} [/tex]
[tex]1\leq j \leq n-1, b_j \in \{+,-,*,/ \}[/tex]
LHS has ##n! 4^{n-1}## patterns. R is a rational number which has equal or less than ##n! 4^{n-1}## different values. We can easily see variety and degeneracy in R by computer.

For an example I assume
[tex] -(n-1)(n-1)!\leq R \leq n!+1[/tex]
 
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  • #3
hdsfhsdf said:
By exploring this "solution space," we seek to uncover the different possible "configurations" for combining the integer numbers, just as we seek to understand the different possible topologies of the universe.
Why do you think this is an appropriate analogy?

hdsfhsdf said:
For example, if n = 3 and R = 4, one possible combination within this "solution space" might be: (1 × 2) + 3 = 4. This combination represents a specific way of combining the integer numbers from 1 to 3 to achieve the result 4
No, it represents a specific way of combining the integer numbers from 1 to 3 to achieve the result 5.

hdsfhsdf said:
just as a specific spatial curvature represents a specific way of configuring space in the universe.
Again, the analogy is absurd.

hdsfhsdf said:
The problem may be of interest to mathematicians and computer scientists.
Why do you think that? It might be of some interest to someone for practicing programming techniques, but no more so than the dozens of similar problems on CodeWars.
 
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FAQ: "The Operation Combination Problem"

What is the Operation Combination Problem?

The Operation Combination Problem is a mathematical or computational problem that involves finding the optimal way to combine a set of operations to achieve a desired outcome. This can involve determining the sequence, selection, or combination of operations that maximize efficiency, minimize cost, or meet other specified criteria.

Why is the Operation Combination Problem important?

The Operation Combination Problem is important because it has applications in various fields such as optimization, computer science, logistics, and manufacturing. Solving this problem can lead to more efficient processes, cost savings, and improved performance in systems that rely on a series of operations.

What are some common methods to solve the Operation Combination Problem?

Common methods to solve the Operation Combination Problem include dynamic programming, greedy algorithms, branch and bound techniques, and heuristic approaches such as genetic algorithms and simulated annealing. The choice of method often depends on the specific characteristics of the problem and the constraints involved.

Can the Operation Combination Problem be solved efficiently for large datasets?

Solving the Operation Combination Problem efficiently for large datasets can be challenging. While exact methods like dynamic programming can provide optimal solutions, they may become computationally infeasible for very large datasets. In such cases, heuristic or approximation methods are often used to find near-optimal solutions within a reasonable time frame.

What are some real-world applications of the Operation Combination Problem?

Real-world applications of the Operation Combination Problem include scheduling tasks in manufacturing, optimizing routes in logistics and transportation, selecting features in machine learning models, and planning resource allocation in project management. These applications benefit from finding the most effective combination of operations to achieve desired outcomes.

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