completed array 2

[Sanket-S-Kale]

No description provided.

[super30admin]

Disappeared Numbers (Problem1.py)

Strengths:

• Correctly implements the in-place marking algorithm with O(n) time complexity
• Uses abs() to handle already-negated values, preventing double negation issues
• Restores the input array to its original state, which is good practice
• Clear comments explaining the algorithm logic
• Well-structured with appropriate variable names

Areas for Improvement:

• The comment about "auxiliary space" could be more precise - technically we're modifying the input array, which may not be allowed in some contexts
• Could add a brief explanation of why we use abs(n) - 1 as the index mapping

Comparison to Reference:

• Student's solution: O(n) time, O(1) auxiliary space ✓
• Reference solution: O(n²) time, O(1) space ✗

The student's solution is significantly more efficient than the reference brute-force approach, meeting the follow-up requirements.

VERDICT: PASS

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max and min (Problem2.py)

Strengths:

• Excellent use of the divide-and-conquer algorithm design technique
• Comprehensive documentation explaining the time complexity analysis
• Clean, readable code with good variable naming
• Correctly handles all edge cases (single element, two elements)
• Achieves better than 2*(N-2) comparisons (approximately 3n/2 - 2)

Areas for Improvement:

• The recursive approach uses O(log n) stack space, while an iterative solution could achieve O(1) space
• Consider making the solution iterative to match the reference solution's space complexity
• The function signature findMinMax returns a list, but the problem doesn't specify return format - ensure consistency with expected output

Minor Note:
The problem mentions "less than 2*(N-2) comparisons" - your solution makes approximately 3n/2 - 2 comparisons, which for n > 2 is indeed less than 2*(n-2). For n=3: 31.5-2=2.5 comparisons vs 2 comparisons needed. For n=4: 32-2=4 comparisons vs 4 comparisons needed. For n=5: 3*2.5-2=5.5 comparisons vs 6 comparisons needed. So your solution meets the requirement for n > 4.

VERDICT: PASS

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Life Game (Problem3.py)

Strengths:

1. Excellent understanding of the in-place solution approach using intermediate state markers
2. Comprehensive comments explaining the algorithm and time/space complexity
3. Clean, readable code structure with good variable naming
4. Correctly handles all four Game of Life rules
5. Proper boundary checking for neighbor validation

Areas for Improvement:

1. The boundary check using in range() is slightly less efficient than direct comparison. Consider using 0 <= nr < rows and 0 <= nc < cols instead.
2. The directions list could be defined once as a class/module constant rather than recreated on each call, though this is a minor optimization.

Overall, this is a solid solution that demonstrates good problem-solving skills and understanding of in-place algorithms.

VERDICT: PASS