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[agk-s30]

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Exercise_1.py (Binary Search):

• Correctness: The binary search implementation is correct. It properly handles the search by narrowing down the search space based on comparison with the mid element.
• Time Complexity: O(log n) - Correctly identified.
• Space Complexity: O(1) - Correctly identified.
• Code Quality: Well-structured with clear comments. The code is readable and follows standard binary search conventions.
• Minor improvement: Could use f-strings instead of the older % formatting for Python 3.6+ compatibility.

Exercise_2.py (Quick Sort - Recursive):

• Correctness: The implementation is correct. It uses the Lomuto partition scheme with the last element as pivot and properly places the pivot in its correct position.
• Time Complexity: O(n log n) average case, O(n²) worst case - Correctly identified.
• Space Complexity: O(log n) for recursive call stack - Correctly identified.
• Code Quality: Clean implementation with good comments explaining the approach. The partition function is well-implemented.

Exercise_3.py (Middle of Linked List):

• Correctness: The two-pointer approach (slow and fast pointers) is a classic and correct method to find the middle element.
• Time Complexity: O(n) - Correctly identified.
• Space Complexity: O(1) - Correctly identified.
• Code Quality: Well-structured with proper class definitions. The implementation follows best practices for linked list operations.

Exercise_4.py (Merge Sort):

• Correctness: The merge sort implementation is correct. It properly divides the array, sorts the halves, and merges them back together.
• Time Complexity: O(n log n) - Correctly identified.
• Space Complexity: O(n) - Correctly identified.
• Code Quality: Good implementation with clear comments. The merge function is correctly implemented with all three while loops to handle remaining elements.

Exercise_5.py (Quick Sort - Iterative):

• Correctness: The iterative quicksort implementation using an explicit stack is correct. It properly simulates the recursive approach using a stack data structure.
• Time Complexity: O(n log n) average case - Correctly identified.
• Space Complexity: O(log n) - Correctly identified.
• Code Quality: Good implementation with clear comments. The use of a stack to avoid recursion is a valid approach. The code handles edge cases properly (checking low >= high and only pushing subarrays with at least two elements).

Overall Assessment:
All five exercises demonstrate a solid understanding of fundamental algorithms and data structures. The student correctly identifies time and space complexities for each solution. The code is well-documented with comments explaining the approach and complexity. The implementations follow standard textbook approaches and are production-ready.

Areas of Strength:

• Consistent documentation of time and space complexity
• Clear, readable code with meaningful variable names
• Proper handling of edge cases
• Good use of comments to explain algorithm logic

Minor Suggestions:

• Consider using f-strings for string formatting (more modern Python)
• Could add docstrings to functions for better documentation
• Exercise_2 and Exercise_5 have very similar partition functions; could extract common logic if needed