Add the algorithm to search the minimum element of an array using the… (#9688)

* Add the algorithm to search the minimum element of an array using the divide-and-conquer algorithm for selection sort

* Update minimum_element_of_array.py

Resolved Ruff warning

* Update minimum_element_of_array.py

Add type hint for every function

* Update minimum_element_of_array.py

Resolve Ruff error: Error: divide_and_conquer/minimum_element_of_array.py:13:1: I001 Import block is un-sorted or un-formatted

* Update minimum_element_of_array.py

Resolve Ruff errors

* Update minimum_element_of_array.py

* Update minimum_element_of_array.py

Update name of variables

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update minimum_element_of_array.py

Update line lenght

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix comments and documentation in minimum_element_of_array.py

Corrected minor grammatical errors and improved clarity in comments.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Refactor selection_sort return type

Updated the return type of selection_sort function to simplify it.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

Author: 3 people

divide_and_conquer/minimum_element_of_array.py

@@ -0,0 +1,129 @@
+"""
+Return the minimum element of an array using the
+divide-and-conquer algorithm for selection sort.
+Like quicksort, it partitions the input array recursively.
+But unlike quicksort,
+which recursively processes both sides of the partition,
+this algorithm works on only one side of the partition.
+The expected running time of this selection sort algorithm is 0(n),
+assuming that the elements are distinct.
+It returns the ith smallest element of the array A[p: r], where 1 ≤ i ≤ r-p+1.
+(From Introduction to Algorithms, Fourth Edition, Cormen, 2022: Chapter 9.2)
+"""
+
+from __future__ import annotations
+
+import random
+
+
+def partition(array: list, starting_index: int, ending_index: int) -> int:
+    """
+    Partition the array.
+    Args:
+        array: list of elements
+        starting_index: starting index of the array
+        ending_index: ending index of the array
+
+    Returns:
+        index of the pivot
+
+    >>> arr = [-2, 3, -10, 11, 99, 100000, 100, -200]
+    >>> partition(arr, 0, len(arr) - 1)
+    0
+    """
+    pivot = array[ending_index]
+    i = starting_index - 1
+    for j in range(starting_index, ending_index):
+        if array[j] <= pivot:
+            i += 1
+            array[i], array[j] = array[j], array[i]
+    array[i + 1], array[ending_index] = array[ending_index], array[i + 1]
+    return i + 1
+
+
+def randomized_partition(array: list, starting_index: int, ending_index: int) -> int:
+    """
+    Randomized partition of the array.
+    Args:
+        array: list of elements
+        starting_index: starting index of the array
+        ending_index: ending index of the array
+
+    Returns:
+        call to partition function
+
+    >>> arr = [-2, 3, -10, 11, 99, 100000, 100, -200]
+    >>> arr1 = randomized_partition(arr, 0, len(arr) - 1)
+    >>> arr == arr1
+    False
+    """
+
+    rand_idx = random.randint(starting_index, ending_index)
+    array[rand_idx], array[ending_index] = array[ending_index], array[rand_idx]
+    return partition(array, starting_index, ending_index)
+
+
+def selection_sort(
+    array: list, starting_index: int, ending_index: int, smallest_element: int
+) -> list | None:
+    """
+    Returns a list of sorted array elements using selection sort.
+    Using selection to find a minimum is O(n) overkill vs. a linear scan — the
+    value here is the DAC/partition demonstration.
+
+    Args:
+        array: list of elements
+        starting_index: starting index of the array
+        ending_index: ending index of the array
+        smallest_element: the ith smallest element of
+                          the array A[p: r], where 1 ≤ i ≤ r-p+1
+
+    Returns:
+        sorted array
+
+    >>> from random import shuffle
+    >>> arr = [-2, 3, -10, 11, 99, 100000, 100, -200]
+    >>> shuffle(arr)
+    >>> selection_sort(arr, 0, len(arr) - 1, 1)
+    -200
+
+    >>> shuffle(arr)
+    >>> selection_sort(arr, 0, len(arr) - 1, 1)
+    -200
+
+    >>> arr = [-200]
+    >>> selection_sort(arr, 0, len(arr) - 1, 1)
+    -200
+
+    >>> arr = [-2]
+    >>> selection_sort(arr, 0, len(arr) - 1, 1)
+    -2
+
+    >>> arr = []
+    >>> selection_sort(arr, 0, len(arr) - 1, 1)
+    []
+    """
+
+    if not array:
+        return array
+
+    if starting_index == ending_index:
+        # 1 <= i <= r - p + 1 when p == r means that i == 1
+        return array[starting_index]
+
+    q = randomized_partition(array, starting_index, ending_index)
+
+    k = q - starting_index + 1
+
+    if smallest_element == k:
+        return array[q]  # the pivot value is the answer
+    if smallest_element < k:
+        return selection_sort(array, starting_index, q - 1, smallest_element)
+    else:
+        return selection_sort(array, q + 1, ending_index, smallest_element - k)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()