Completed Competitive Coding-1

[RitikaC1]

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[super30admin]

Interview Problem: Find Missing Number in a sorted array (Problem1.py)

Your solution demonstrates a solid understanding of the binary search algorithm applied to finding a missing number in a sorted array. The approach of comparing arr[mid] with mid+1 is intuitive and effective. The code is clean and well-commented. Consider adding input validation to handle edge cases like empty arrays or single-element arrays, which would make the solution more robust. Overall, this is a good implementation that matches the reference solution's complexity and correctness.

VERDICT: PASS

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Interview Problem: Design Min Heap (Problem2.py)

EVALUATION

1. Correctness

The student's solution correctly implements a Min Heap with all three required operations:

• insert(): Correctly appends the value and uses _heapify_up to maintain the heap property
• get_min(): Returns the root element (minimum) in O(1) time
• extract_min(): Correctly removes the minimum, replaces root with last element, and uses _heapify_down to restore heap property

The solution handles edge cases (empty heap, single element) appropriately. The logic for both heapify operations is sound and follows standard Min Heap algorithms.

2. Time Complexity

• insert(): O(log N) - Correctly identified
• get_min(): O(1) - Correctly identified
• extract_min(): O(log N) - Correctly identified

The implementation achieves the same time complexity as the reference solution.

3. Space Complexity

The student claims O(1) space complexity, which is incorrect. The heap array grows with the number of elements inserted, so the space complexity is O(N) where N is the number of elements stored. This matches the reference solution's O(n) space complexity.

4. Code Quality

Strengths:

• Clean, well-indented code with consistent formatting
• Good use of descriptive method names (_heapify_up, _heapify_down)
• Private methods appropriately prefixed with underscore
• Clear main block demonstrating usage
• Proper documentation of time complexity

Areas for Improvement:

• Space complexity is mislabeled as O(1) - should be O(N)
• Could add type hints for better code documentation
• The _heapify_down method could benefit from more explicit comments explaining the logic
• No bounds checking in _heapify_down for edge cases (though current logic handles them)

5. Efficiency

The implementation is efficient and follows standard Min Heap patterns:

• No unnecessary operations
• Proper use of iterative rather than recursive approaches (avoids stack overflow for large heaps)
• Early termination in _heapify_up when no swap is needed

FEEDBACK

Strengths:

• Excellent implementation of core Min Heap operations
• Clean, readable code structure that matches the reference solution in correctness
• Good handling of edge cases (empty heap, single element)
• Proper use of heap property maintenance algorithms

Areas for Improvement:

1. Space Complexity Error: The space complexity is O(N), not O(1). The heap list grows proportionally with the number of elements inserted.
2. Minor Optimization: In _heapify_down, you could cache length to avoid repeated len() calls (though this is a very minor optimization).

Overall, this is a solid implementation that correctly solves the problem. The only significant issue is the incorrect space complexity claim.

The solution correctly implements all Min Heap operations with appropriate time complexity. The code is well-structured and readable. While the space complexity documentation is incorrect (should be O(N) not O(1)), the implementation itself is sound and efficient.

VERDICT: PASS