**π‘ Problem Formulation:** Given a sequence of numbers, the challenge lies in determining the minimum cost of rearranging them into either ascending or descending order. The “cost” is defined by the number of swaps needed to achieve the ordered sequence. For example, converting the input `[3, 1, 2]`

into an ascending order `[1, 2, 3]`

would incur some cost based on the swaps performed.

## Method 1: Bubble Sort

This traditional sorting method involves comparing adjacent elements and swapping them if they are in the wrong order. Applied to each pair of numbers through multiple passes until the entire list is sorted, Bubble Sort is a straightforward method to calculate the cost of arranging numbers.

Here’s an example:

def bubble_sort_cost(arr): cost = 0 for i in range(len(arr)): for j in range(0, len(arr)-i-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] cost += 1 return cost print(bubble_sort_cost([3, 1, 2]))

Output: `2`

Bubble Sort’s strength lies in its simplicity and ease of implementation. However, the O(n^2) time complexity makes it less efficient for large datasets.

## Method 2: Selection Sort

Selection Sort improves on Bubble Sort by reducing the number of swaps. It selects the smallest (or largest) element from an unsorted sublist and swaps it with the first unsorted element of the list. The cost is equivalent to the number of swaps required to sort the list.

Here’s an example:

def selection_sort_cost(arr): cost = 0 for i in range(len(arr)): min_idx = i for j in range(i+1, len(arr)): if arr[min_idx] > arr[j]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] if min_idx != i: cost += 1 return cost print(selection_sort_cost([3, 1, 2]))

Output: `1`

Selection Sort is intuitive and performs well on small lists. Nevertheless, with an O(n^2) complexity for time, its efficiency drops for more extensive data sets.

## Method 3: Insertion Sort

Insertion Sort builds the sorted list one element at a time, picking the next element and inserting it into its correct position. The cost is the number of swaps needed for each element to reach its proper place.

Here’s an example:

def insertion_sort_cost(arr): cost = 0 for i in range(1, len(arr)): key = arr[i] j = i - 1 while j >= 0 and key < arr[j]: arr[j + 1] = arr[j] cost += 1 j -= 1 arr[j + 1] = key return cost print(insertion_sort_cost([3, 1, 2]))

Output: `2`

While Insertion Sort is efficient for small datasets or nearly sorted lists, its typical O(n^2) complexity makes it less suitable for larger, unsorted data.

## Method 4: Merge Sort

Merge Sort employs a divide-and-conquer strategy, breaking the list into halves, sorting each half, and then merging them back together. It can be adapted to count the minimum number of swaps as well.

Here’s an example:

# Merge Sort adapted to count swaps is a bit more involved # and is not shown here for brevity.

While Merge Sort is more complex, it boasts a better O(n log n) time complexity, making it a great choice for larger datasets. Its downside is that it consumes additional memory for the temporary arrays used during the merge process.

## Bonus One-Liner Method 5: Python’s Built-in Sorting

Python’s built-in `sorted()`

function and `list.sort()`

method are highly optimized and handle sorting internally. Although these functions do not provide the cost directly, they can serve as a baseline to understand the optimal sorting performance.

Here’s an example:

arr = [3, 1, 2] sorted_arr = sorted(arr) # Cost can't be extracted directly using built-in sorting

Built-in sorting methods in Python are fast and efficient, but they don’t allow tracking the number of swaps directly.

## Summary/Discussion

**Method 1: Bubble Sort**. Simple implementation. However, inefficient for large datasets due to quadratic time complexity.**Method 2: Selection Sort**. Reduces the number of swaps. Still, it is not the best option for complex data sorting tasks.**Method 3: Insertion Sort**. Effective for small or nearly sorted datasets. Inefficient for larger, unsorted lists.**Method 4: Merge Sort**. Performs well with large datasets. Requires extra space, which could be a drawback for memory-constrained environments.**Bonus Method 5: Built-in Sorting**. Fastest and most efficient but doesn’t offer insights into the cost of sorting.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.