The aim of this experiment is to understand and implement divide-and-conquer sorting algorithms that work by reducing the problem size through partitioning. Students will learn how algorithms like QuickSort and its variants use pivot elements to partition arrays into smaller subarrays, recursively sort these partitions, and combine them to achieve a fully sorted array.

Learning Objectives:

1. Understand the Divide-and-Conquer Paradigm: Learn how complex sorting problems can be broken down into smaller, more manageable subproblems.

2. Master Partitioning Techniques: Understand how pivot selection and partitioning strategies affect algorithm performance.

3. Analyze Time and Space Complexity: Explore best-case, average-case, and worst-case scenarios for partition-based sorting algorithms.

4. Compare Different Pivot Selection Strategies: Study the impact of different pivot selection methods (first element, last element, random, median-of-three) on algorithm performance.

5. Visualize the Sorting Process: Observe how the array gets progressively sorted through recursive partitioning and how the algorithm reduces the problem size at each step.

6. Understand Performance Trade-offs: Learn when to use partition-based sorting algorithms and their advantages over other sorting methods.