Completed Backtracking-2

[ashritha0806]

No description provided.

[super30admin]

Subsets (subset.py)

Strengths:

1. Excellent use of backtracking with in-place modification (path.pop()), which is more memory-efficient than creating new copies
2. Clear and informative comments explaining the decision tree approach
3. Clean code structure with descriptive variable names
4. Correct base case handling and state restoration
5. Self-contained solution with proper initialization of instance variables

Areas for Improvement:

1. The space complexity analysis in the comment could be more precise. While it says O(n), it would be worth noting that this is for the recursion stack and path, excluding the output space
2. Consider adding type hints for better code documentation (e.g., def helper(index: int) -> None:)

Minor Note:
The solution correctly handles the problem constraints (nums length 1-10, unique elements). The approach scales well within these constraints.

Overall, this is a solid implementation that demonstrates good understanding of the backtracking pattern and is more space-efficient than the reference solution.

VERDICT: PASS

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Palindrome Partitioning (palindrome.py)

Strengths:

• Clean, well-commented code with clear explanation of the backtracking approach
• Correct implementation of the core algorithm
• Proper use of list(path) to create a copy when adding to results (avoids reference issues)
• Good variable naming (pivot, path, result)

Areas for Improvement:

• The palindrome check using string reversal (s == s[::-1]) is less efficient than the two-pointer approach used in the reference solution. For longer strings, this could be optimized.
• Consider adding type hints for better code documentation (e.g., def partition(self, s: str) -> List[List[str]]:)
• The solution is missing the import statement for List from typing

VERDICT: PASS