PreCourse-2 Complete

[mgpadshala]

PreCourse-2 Complete

[super30admin]

The student's solution demonstrates a good understanding of fundamental algorithms and data structures. Here's a detailed evaluation for each exercise:

1. Exercise_1.py (Binary Search):

   • Correctness: The implementation correctly follows the iterative binary search approach.
   • 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. The variable names are appropriate.
   • Efficiency: No optimizations needed.

2. Exercise_2.py (QuickSort):

   • Correctness: The implementation correctly follows the QuickSort algorithm with proper partitioning.
   • Time Complexity: O(n log n) average case, O(n²) worst case (though unlikely with good pivot selection).
   • Space Complexity: O(log n) due to recursion stack.
   • Code Quality: The code is well-structured. The partition function is correctly implemented.
   • Efficiency: The recursive calls in quickSort should use low instead of 0 in the first recursive call to avoid unnecessary sorting of already sorted parts.

3. Exercise_3.py (Linked List Middle):

   • Correctness: The implementation correctly finds the middle element using the slow-fast pointer technique.
   • Time Complexity: O(n), which is optimal for this problem.
   • Space Complexity: O(1), using constant extra space.
   • Code Quality: The code is clean and well-structured. The edge case for empty list is handled.
   • Efficiency: No optimizations needed.

4. Exercise_4.py (MergeSort):

   • Correctness: The implementation correctly follows the MergeSort algorithm.
   • Time Complexity: O(n log n) in all cases.
   • Space Complexity: O(n) due to auxiliary arrays used in merging.
   • Code Quality: The code is well-structured but could benefit from more descriptive variable names (e.g., leftSortedArray could be left_half).
   • Efficiency: The merge function creates new arrays for left and right halves, which could be optimized by using a single auxiliary array.

5. Exercise_5.py (Iterative QuickSort):

   • Correctness: The implementation correctly follows the iterative QuickSort approach using a stack.
   • Time Complexity: O(n log n) average case, O(n²) worst case.
   • Space Complexity: O(n) in worst case due to stack usage.
   • Code Quality: The code is well-structured but the indentation in the if condition inside the while loop is incorrect, which could lead to logical errors.
   • Efficiency: The stack implementation could be optimized by pushing the larger partition first to limit stack size.

Overall, the student has demonstrated a strong understanding of these algorithms. The main areas for improvement are:

1. Fixing the indentation issue in Exercise_5.py
2. Using more descriptive variable names in some places
3. Optimizing the merge function in Exercise_4.py to reduce space usage
4. Correcting the recursive call in Exercise_2.py to use low instead of 0