Done PreCourse-2

[vaishnavi2231]

No description provided.

[super30admin]

Exercise_1.py:

• Correctness: The binary search implementation is correct and handles edge cases well. However, sorting the array inside the binary search function is unnecessary and inefficient (O(n logn) vs O(logn) for binary search).
• Time Complexity: Claimed O(logn) is correct for binary search, but sorting makes it O(n logn).
• Space Complexity: O(1) is correct.
• Code Quality: Good structure and readability. Test cases are comprehensive.
• Suggestion: Remove the sorting step since binary search requires a sorted array as input.

Exercise_2.py:

• Correctness: QuickSort implementation is correct with proper partitioning.
• Time Complexity: O(n logn) average case, O(n^2) worst case (standard for QuickSort).
• Space Complexity: O(logn) for recursion stack.
• Code Quality: Clean and well-structured. Follows standard QuickSort implementation.
• Suggestion: Consider adding comments explaining the partitioning logic.

Exercise_3.py:

• Correctness: Correct implementation of finding middle node using slow/fast pointers.
• Time Complexity: O(n) is correct.
• Space Complexity: O(1) is correct.
• Code Quality: Good structure and readability. Proper Node and LinkedList class implementation.
• Suggestion: None, this is well done.

Exercise_4.py:

• Correctness: Correct MergeSort implementation with proper combining of sorted halves.
• Time Complexity: O(n logn) is correct.
• Space Complexity: O(n) is correct for merge operation.
• Code Quality: Good structure, though could benefit from more comments explaining the combine function.
• Suggestion: Consider in-place merge to reduce space complexity.

Exercise_5.py:

• Correctness: Correct iterative QuickSort implementation using stack.
• Time Complexity: O(n logn) average case, same as recursive version.
• Space Complexity: O(n) for stack (could be O(logn) with better implementation).
• Code Quality: Well-structured and readable.
• Suggestion: Could optimize stack usage to reduce space complexity.

General strengths:

• All solutions are functionally correct and implement standard algorithms properly.
• Good attention to time/space complexity analysis.
• Comprehensive test cases in Exercise_1.py.
• Consistent code style and formatting.

Areas for improvement:

• Avoid unnecessary operations (like sorting in binary search).
• Could add more comments explaining complex logic.
• Consider space optimizations where possible.