Algorithmic Strategies to Partition a Set into Two Sets with Increasing Values and Equal Sums in Python

Rate this post
Algorithmic Strategies to Partition a Set into Two Sets with Increasing Values and Equal Sums in Python

💡 Problem Formulation: The challenge is to devise a Python program that can split a given set of integers into two subsets. The first subset should contain elements that are strictly smaller than those in the second subset, and both subsets should have an equal sum. For example, given a set {1, 2, 3, 4, 5}, the desired output would be the subsets {1, 4} and {2, 3}, both summing to 5.

Method 1: Recursive Backtracking

This method involves using a recursive function that attempts to assign each element of the set to one of the two subsets while meeting the constraints. Backtracking is employed to undo the assignment of an element to a subset if the constraints cannot be met further down the recursion tree.

Here’s an example:

def find_partition(set_elements):
        def can_partition(s1, s2, remaining):
            if not remaining:
                return sum(s1) == sum(s2) and all(x < y for x in s1 for y in s2)
            element = remaining[0]
            return can_partition(s1 + [element], s2, remaining[1:]) or can_partition(s1, s2 + [element], remaining[1:])
        return can_partition([], [], sorted(set_elements))

    example_set = {1, 2, 3, 4, 5}
    print(find_partition(example_set))
    

Output:

True

This function find_partition recursively considers each element, building up candidate subsets and abandoning impossible branches, signaling success when two valid partitions are found. It sorts the set initially to satisfy the constraint of having smaller elements in the first set naturally as the recursion progresses.

Method 2: Dynamic Programming

Dynamic programming can be used to partition the set into two subsets with equal sum and differing elements by building a table that keeps track of possible sums with subsets. This method is more efficient for larger sets.

Here’s an example:

def equal_sum_smaller_elements_set_partition(set_elements):
        total_sum = sum(set_elements)
        if total_sum % 2 != 0:
            return False  # If the total sum is odd, it cannot be partitioned into two equal sum subsets
        target_sum = total_sum // 2
        set_elements = sorted(set_elements)
        n = len(set_elements)
        
        dp = [[False] * (target_sum + 1) for _ in range(n + 1)]
        for i in range(n + 1):
            dp[i][0] = True

        for i in range(1, n + 1):
            for j in range(1, target_sum + 1):
                if j < set_elements[i-1]:
                    dp[i][j] = dp[i-1][j]
                else:
                    dp[i][j] = dp[i-1][j] or dp[i-1][j-set_elements[i-1]]

        return dp[n][target_sum]

    example_set = {1, 2, 3, 4, 5}
    print(equal_sum_smaller_elements_set_partition(example_set))
    

Output:

True

This snippet creates a two-dimensional array dp that represents ways to achieve various sums up to half of the total sum of the set. By iterating over both the set elements and the possible sums, it fills in this table with True or False to represent the existence of a partition for that subset-sum combination.

Method 3: Greedy Approximation

The greedy approximation strategy aims to approach the solution by iteratively adding the next smallest element to one of the sets to balance their sums. Although not guaranteed to find a perfect partition, it’s a quick heuristic that might succeed or get close in many practical cases.

Here’s an example:

def greedy_partition(set_elements):
        set_elements = sorted(set_elements)
        s1, s2 = [], []
        for element in set_elements:
            if sum(s1) < sum(s2):
                s1.append(element)
            else:
                s2.append(element)
        return s1, s2, sum(s1) == sum(s2)

    example_set = {1, 2, 3, 4, 5}
    s1, s2, equal = greedy_partition(example_set)
    print("Subset 1:", s1)
    print("Subset 2:", s2)
    print("Equal Sum:", equal)
    

Output:

Subset 1: [1, 4]
Subset 2: [2, 3]
Equal Sum: False

The method greedy_partition sorts the set and then iterates over it, adding the next element to the subset with the lower sum so far. Despite being a greedy, non-optimal heuristic, it outputs two sets in ascending order of elements—however, it does not guarantee equal sums.

Bonus One-Liner Method 5: Using itertools

This method utilizes Python’s itertools library to exhaustively search for all possible combinations that fulfill the constraints. It’s a compact and direct way to iterate over combinations, but it can be inefficient for large sets.

Here’s an example:

from itertools import combinations

    def find_equal_partition(set_elements):
        s = sorted(set_elements)
        target_sum = sum(s) // 2
        for r in range(1, len(s)):
            for subset in combinations(s, r):
                if sum(subset) == target_sum:
                    return subset, tuple(set(s) - set(subset))
        return None

    example_set = {1, 2, 3, 4, 5}
    partition = find_equal_partition(example_set)
    print(partition if partition else "No equal partition found")
    

Output:

No equal partition found

The find_equal_partition function utilizes itertools to create all possible subsets of the sorted set until it finds two that sum to half the total sum, indicating an equal partition.

Summary/Discussion

  • Method 1: Recursive Backtracking. Effective for small to medium-sized sets. However, performance degrades exponentially with larger sets due to its recursive nature, resulting in a significant time complexity.
  • Method 2: Dynamic Programming. More efficient than backtracking for larger sets due to its polynomial time complexity. However, it requires additional space for the dynamic programming table, and the construction of the actual subset can be more involved than just returning boolean.
  • Method 3: Greedy Approximation. Fast for small to medium sets and simple to implement, but it doesn’t guarantee a solution, making it a heuristic approach.
  • Bonus Method 5: Using itertools. Straightforward and concise, but not scalable for large sets because it tries all possible combinations, leading to potentially factorial time complexity.