preCourse 2 exercises

[ravithakur2k]

No description provided.

[super30admin]

Let me evaluate each exercise separately:

Exercise_1.py (Binary Search):

• Correctness: The implementation is mostly correct but has a bug when the element is not found. The condition while (l < r) should be while (l <= r) to handle cases where the element is at the last position.
• Time Complexity: Correctly identified as O(log n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: Good variable naming and structure. Could add comments explaining the edge case handling.
• Efficiency: The current implementation might miss some edge cases.

Exercise_2.py (QuickSort):

• Correctness: Implementation is correct and follows the standard QuickSort approach.
• Time Complexity: Correctly identified as O(n log n) average and O(n^2) worst case.
• Space Complexity: Not mentioned, but it's O(log n) due to recursion stack.
• Code Quality: Well-structured with good comments explaining the logic.
• Efficiency: Good implementation, though choosing a random pivot could improve worst-case performance.

Exercise_3.py (Linked List Middle):

• Correctness: Correct implementation of finding the middle node.
• Time Complexity: Correctly identified as O(n).
• Space Complexity: Correctly identified as O(1) for the algorithm (O(n) for the list itself).
• Code Quality: Good structure and variable naming. Could add more comments.
• Efficiency: Optimal solution using fast/slow pointers.

Exercise_4.py (MergeSort):

• Correctness: Correct implementation of MergeSort.
• Time Complexity: Correctly identified as O(n log n).
• Space Complexity: Correctly identified as O(n) for the merge operation.
• Code Quality: Well-structured with good variable naming. Could add more comments.
• Efficiency: Standard implementation, no obvious optimizations needed.

Exercise_5.py (Iterative QuickSort):

• Correctness: Correct implementation of iterative QuickSort.
• Time Complexity: Correctly identified as O(n log n) average and O(n^2) worst case.
• Space Complexity: Correctly identified as O(log n) average and O(n) worst case.
• Code Quality: Good structure and variable naming. Could add more comments.
• Efficiency: Good implementation, though choosing a random pivot could improve worst-case performance.

General Strengths:

• Good understanding of algorithms and their complexities
• Proper variable naming and code structure
• Mostly correct implementations

Areas for Improvement:

• Need to handle edge cases better in binary search
• Could add more comments explaining the logic
• Could mention space complexity in QuickSort (Exercise_2)
• Could consider random pivot selection for QuickSort variants