Completed PreCourse-2

[Vinod-Christwin]

No description provided.

[super30admin]

Exercise_1.py (Binary Search):

• Correctness: The implementation correctly follows the binary search algorithm. It handles the search correctly by adjusting the left and right pointers based on the comparison with the mid value.
• Time Complexity: Correctly identified as O(log n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: The code is clean and well-structured. The comments are helpful. One minor improvement could be to use (r - l) // 2 instead of (r - 1) // 2 in the mid calculation to be more precise.
• Efficiency: The implementation is efficient. No major optimizations needed.

Exercise_2.py (QuickSort - Recursive):

• Correctness: The partition and quicksort functions are correctly implemented following the standard approach.
• Time Complexity: Correctly identified as O(n log n) average case.
• Space Complexity: Correctly identified as O(log n) due to recursive calls.
• Code Quality: Good structure and comments. The variable names could be more descriptive (e.g., 'part' could be 'partition_index').
• Efficiency: The implementation is standard and efficient. Using the last element as pivot is fine but could be optimized by choosing a better pivot (e.g., median of three).

Exercise_3.py (Linked List Middle Element):

• Correctness: Correctly implements the two-pointer technique to find the middle element.
• Time Complexity: Correctly identified as O(n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: Well-structured and readable. The comments explain the approach clearly.
• Efficiency: The implementation is optimal for this problem.

Exercise_4.py (MergeSort):

• Correctness: Correctly implements the merge sort algorithm with proper merging of subarrays.
• Time Complexity: Correctly identified as O(n log n).
• Space Complexity: Correctly identified as O(n).
• Code Quality: Good structure, but could benefit from more descriptive variable names (e.g., 'l' and 'r' could be 'left' and 'right').
• Efficiency: The implementation is standard. One optimization could be to avoid creating new subarrays by using indices.

Exercise_5.py (QuickSort - Iterative):

• Correctness: Correctly implements the iterative version of quicksort using a stack.
• Time Complexity: Correctly identified as O(n log n).
• Space Complexity: Correctly identified as O(n).
• Code Quality: Well-structured but could use more comments to explain the stack operations.
• Efficiency: The implementation is correct but could be optimized by choosing a better pivot strategy.

General Feedback:

• The student has demonstrated a strong understanding of algorithms and data structures.
• All implementations are correct and efficient.
• The code quality is generally good, with minor improvements possible in variable naming and comments.
• The time and space complexity analyses are accurate.
• The student follows best practices and the solutions are well-structured.