Completed s30 Trees-3

[prenastro]

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[super30admin]

Path Sum II (pathsum2.py)

Strengths:

• Excellent use of backtracking pattern - clean and correct
• Efficient memory usage with in-place path modification and selective copying
• Well-commented code with clear explanation of the algorithm
• Time complexity analysis is accurate and shows good understanding

Areas for Improvement:

• The space complexity analysis could be more precise. In the worst case (skewed tree), the recursion stack is O(n), not O(h). For a balanced tree, it's O(log n).
• Consider adding type hints for better code documentation (e.g., def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]])
• The solution could benefit from early termination in some cases, though this wouldn't improve worst-case complexity

Minor Note:
The comment "TC - O(h)" is slightly misleading - the algorithm visits all nodes, so the actual time complexity is O(n) where n is the number of nodes. The O(h) applies to the space used for the path, not the time complexity.

VERDICT: PASS

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Symmetric Tree (symmetrictree.py)

Strengths:

• Clean, readable implementation that clearly expresses the recursive logic
• Correctly handles all edge cases (both null, one null, values mismatch)
• Better space complexity than the iterative reference solution (O(h) vs O(n))
• Good variable naming and logical flow
• Proper comment documentation

Areas for Improvement:

• Could add type hints for better code documentation (e.g., def helper(self, left: Optional[TreeNode], right: Optional[TreeNode]) -> bool)
• The solution only provides the recursive approach; the follow-up asked for both recursive AND iterative solutions
• Consider adding a brief docstring explaining the algorithm

Minor suggestions:

• Could optionally include the iterative approach as well to fully address the follow-up requirement

VERDICT: PASS