Completed PreCourse-2

[gurneetk186]

No description provided.

[super30admin]

Let's evaluate each exercise one by one:

Exercise_1.java (Binary Search):

• Correctness: The implementation follows the standard binary search algorithm and should work correctly. However, there's a bug in the mid calculation (int mid = l +(r-1)/2 should be int mid = l + (r-l)/2).
• Time Complexity: Correctly identified as O(log n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: The code is generally clean but has some misplaced braces and comments. The "Write your code here" comment should be removed.
• Efficiency: The algorithm is optimal for binary search.

Exercise_2.java (QuickSort):

• Correctness: The implementation correctly follows the QuickSort algorithm with proper partitioning.
• Time Complexity: Correctly identified as O(n log n) average case.
• Space Complexity: Should be O(log n) due to recursion stack, not O(1).
• Code Quality: Good overall, but some comments are misplaced (e.g., "Write code here for Partition and Swap").
• Efficiency: The implementation is standard and efficient.

Exercise_3.java (Linked List Middle):

• Correctness: Correctly implements the two-pointer technique to find the middle.
• Time Complexity: Correctly identified as O(n).
• Space Complexity: Correctly identified as O(1).
• Code Quality: Good, but the error message "The last is empty" should be more clear ("The list is empty").
• Efficiency: Optimal solution for this problem.

Exercise_4.java (MergeSort):

• Correctness: Correct implementation of MergeSort.
• Time Complexity: Correctly identified as O(n log n).
• Space Complexity: Correctly identified as O(n).
• Code Quality: Good, but some comments are misplaced ("Your code here").
• Efficiency: Standard implementation, no major optimizations needed.

Exercise_5.java (Iterative QuickSort):

• Correctness: Correct implementation of iterative QuickSort using a stack.
• Time Complexity: Correctly identified (O(n log n) average, O(n^2) worst).
• Space Complexity: Correctly identified as O(log n).
• Code Quality: Good, but the swap implementation using arithmetic could lead to overflow (better to use standard temp variable swap).
• Efficiency: The stack-based approach is a good optimization over recursive QuickSort.

General Observations:
Strengths:

1. All implementations follow standard algorithms correctly.
2. Time and space complexity analysis is mostly accurate.
3. Code is generally well-structured.

Areas for Improvement:

1. Fix the mid calculation in binary search.
2. Improve some error messages for clarity.
3. Remove placeholder comments like "Write your code here".
4. Consider edge cases more carefully (empty inputs, already sorted inputs, etc.).
5. For swap operations, prefer the standard temp variable approach to avoid potential overflow.