[Pre-course 2] Finished assignments

[Chowry000]

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[super30admin]

Let me evaluate each exercise separately:

Exercise_1.py (Binary Search):

• Correctness: The implementation correctly follows the binary search algorithm.
• Time Complexity: O(log n) as expected for binary search.
• Space Complexity: O(1) since it's iterative.
• Code Quality: Clean and straightforward. Could benefit from comments explaining the logic.
• Efficiency: Well optimized. No improvements needed.

Exercise_2.py (QuickSort):

• Correctness: Correct implementation of QuickSort using Lomuto partition scheme.
• Time Complexity: O(n log n) average case, O(n²) worst case.
• Space Complexity: O(log n) due to recursion stack.
• Code Quality: Good, but could use more comments explaining the partition logic.
• Efficiency: Standard implementation. Could consider adding a check for small arrays to switch to insertion sort.

Exercise_3.py (Linked List Middle):

• Correctness: Correctly finds middle using slow/fast pointer technique.
• Time Complexity: O(n) as it traverses the list once.
• Space Complexity: O(1) using constant space.
• Code Quality: Good, but could add comments explaining the two-pointer approach.
• Efficiency: Optimal solution for this problem.

Exercise_4.py (MergeSort):

• Correctness: Correct implementation of MergeSort.
• Time Complexity: O(n log n) in all cases.
• Space Complexity: O(n) due to auxiliary arrays.
• Code Quality: Good, but could benefit from more comments explaining the merge process.
• Efficiency: Standard implementation. Could optimize space usage by creating temp arrays once and reusing them.

Exercise_5.py (QuickSort Iterative/Recursive):

• Correctness: Both implementations work correctly.
• Time Complexity: O(n log n) average case, O(n²) worst case for both.
• Space Complexity: O(log n) for both (stack vs recursion stack).
• Code Quality: Excellent with detailed comments and explanations.
• Efficiency: Well implemented with good test cases. Could add more edge case testing.

General Strengths:

• All algorithms are correctly implemented
• Good use of standard approaches
• Mostly clean and readable code
• Exercise_5 shows particularly good practice with thorough testing

Areas for Improvement:

• Could add more comments explaining the algorithms
• Could add more edge case testing in earlier exercises
• Could consider optimizations like switching to insertion sort for small arrays
• Could add docstrings to functions for better documentation