PreCourse-2 Submission - V1 (Hemish Veeraboina_RN16AUG2025)

[v-hemish]

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[super30admin]

Exercise_1.py (Binary Search):

• Correctness: The implementation correctly follows the binary search algorithm. It handles the search space reduction properly and returns the correct index or -1 if not found.
• Time Complexity: Correctly identified as O(log n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: Clean and readable. The variable names are appropriate.
• Improvement: Could add input validation to ensure the array is sorted before searching.

Exercise_2.py (QuickSort Recursive):

• Correctness: The partition function and recursive quicksort implementation are correct.
• Time Complexity: Average case O(n log n), worst case O(n^2) if the pivot selection is poor.
• Space Complexity: O(log n) for the recursion stack (not mentioned in comments).
• Code Quality: Well-structured. The partition logic is clear.
• Improvement: Could optimize pivot selection (e.g., median-of-three) to avoid worst-case scenarios.

Exercise_3.py (Linked List Middle Element):

• Correctness: Correctly implements the fast/slow pointer approach to find the middle.
• Time Complexity: Correctly identified as O(n).
• Space Complexity: Correctly identified as O(n) for the linked list.
• Code Quality: Good structure. The push method efficiently maintains head and tail pointers.
• Improvement: Could add error handling for empty list case in printMiddle().

Exercise_4.py (MergeSort):

• Correctness: Correct implementation of the merge sort algorithm.
• Time Complexity: O(n log n) in all cases.
• Space Complexity: O(n) for the temporary arrays (not mentioned in comments).
• Code Quality: Clean implementation. The merge logic is clear.
• Improvement: Could optimize space usage by using a single auxiliary array.

Exercise_5.py (QuickSort Iterative):

• Correctness: Correctly implements iterative quicksort using a stack.
• Time Complexity: Same as recursive version (average O(n log n), worst O(n^2)).
• Space Complexity: O(log n) for the stack (better than recursive version's worst case).
• Code Quality: Good conversion from recursive to iterative approach.
• Improvement: Same pivot selection optimization as Exercise_2 applies here.

General Strengths:

• All implementations follow standard algorithms correctly.
• Good code organization and readability.
• Appropriate comments explaining complexities.

Areas for Improvement:

• Could add more edge case handling (empty inputs, etc.).
• Some space complexity explanations could be more detailed.
• Some algorithms could benefit from optimizations (pivot selection, space usage).