Pre Course-2 Done

[neeeraj1999]

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[super30admin]

Let's evaluate each exercise one by one:

Exercise_1.py (Binary Search):

• Correctness: The implementation correctly follows the iterative binary search algorithm.
• Time Complexity: O(log n), which is optimal for binary search.
• Space Complexity: O(1), as it uses constant extra space.
• Code Quality: The code is clean and well-structured. Comments are appropriate.
• Efficiency: No improvements needed. The implementation is optimal.

Exercise_2.py (QuickSort):

• Correctness: The implementation correctly follows the QuickSort algorithm with Lomuto partition scheme.
• Time Complexity: Average case O(n log n), worst case O(n²).
• Space Complexity: O(log n) due to recursion stack.
• Code Quality: Code is well-organized. Could benefit from a brief explanation of the partition scheme in comments.
• Efficiency: Consider adding a check for small subarrays to switch to insertion sort for optimization.

Exercise_3.py (Linked List Middle):

• Correctness: Correctly implements the two-pointer technique to find the middle.
• Time Complexity: O(n), which is optimal for this problem.
• Space Complexity: O(1), using constant extra space.
• Code Quality: Good structure. The empty list check is a nice touch.
• Efficiency: No improvements needed. The implementation is optimal.

Exercise_4.py (MergeSort):

• Correctness: Correct implementation of the MergeSort algorithm.
• Time Complexity: O(n log n) in all cases.
• Space Complexity: O(n) due to creating temporary arrays.
• Code Quality: Well-structured. Could benefit from more comments explaining the merge process.
• Efficiency: Consider implementing an in-place merge to reduce space complexity.

Exercise_5.py (Iterative QuickSort):

• Correctness: Correct implementation of iterative QuickSort using a stack.
• Time Complexity: Same as recursive version - average O(n log n), worst O(n²).
• Space Complexity: O(n) in worst case due to stack usage (better than recursive version's worst case).
• Code Quality: Good structure. Could use more comments explaining the stack operations.
• Efficiency: No major improvements needed. Could consider the same small subarray optimization as in Exercise_2.

General Observations:
Strengths:

1. All implementations follow standard algorithms correctly.
2. Code is generally clean and well-structured.
3. Appropriate use of comments in most places.

Areas for Improvement:

1. Could add more explanatory comments, especially for complex algorithms.
2. For sorting algorithms, could mention optimizations for small subarrays.
3. Consider adding edge case tests (empty arrays, single element arrays, etc.).