Complete Precourse 2

[Srijha-Kalyan]

No description provided.

[super30admin]

The student has provided solutions for five different exercises, each implementing a common algorithm or data structure. Here's a detailed evaluation for each:

1. Exercise_1.py (Binary Search):

   • Correctness: The implementation correctly follows the binary search algorithm.
   • Time Complexity: Correctly identified as O(log n).
   • Space Complexity: Correctly identified as O(1).
   • Code Quality: Well-structured and readable. The parameters l and r in the function signature are redundant since they're immediately overwritten.
   • Efficiency: No optimizations needed.

2. Exercise_2.py (QuickSort - Recursive):

   • Correctness: Correct implementation of QuickSort with proper partitioning.
   • Time Complexity: Correctly identified as O(n log n) average and O(n²) worst case.
   • Space Complexity: Correctly identified as O(log n) due to recursion stack.
   • Code Quality: Well-structured with good comments explaining the partition function.
   • Efficiency: Standard implementation, no optimizations needed.

3. Exercise_3.py (Linked List Middle Element):

   • Correctness: Correct implementation using slow/fast pointer technique.
   • Time Complexity: O(n) (though not explicitly mentioned).
   • Space Complexity: O(1) (though not explicitly mentioned).
   • Code Quality: Good structure and comments. Could mention time/space complexity in docstring.
   • Efficiency: Optimal solution for this problem.

4. 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).
   • Code Quality: Well-structured with good variable naming (L, R for left/right).
   • Efficiency: Standard implementation, no optimizations needed.

5. Exercise_5.py (QuickSort - Iterative):

   • Correctness: Correct implementation using a stack to simulate recursion.
   • Time Complexity: Correctly identified as O(n log n) average and O(n²) worst case.
   • Space Complexity: Correctly identified as O(log n) to O(n).
   • Code Quality: Good structure and comments. Could explain the stack usage more clearly.
   • Efficiency: Standard implementation, no optimizations needed.

General Strengths:

• Consistent style and good documentation
• Correct time/space complexity analysis
• Proper implementation of standard algorithms
• Good variable naming

Areas for Improvement:

• Some redundant parameters (like l, r in binarySearch)
• Could add more detailed docstrings explaining time/space complexity
• For linked list problem, could explicitly state time/space complexity
• Could add more edge case testing (empty arrays, single element, etc.)