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Your solution correctly solves the problem and will pass all test cases.
The code is clean, readable, and well-structured.
You correctly translated the logic from the reference solution.
Areas for Improvement:
Incorrect Complexity Analysis: Your comments claim O(n) time and O(n) space complexity, but the actual complexities are O(n²) time and O(1) space. Always verify your complexity analysis carefully.
Missed the Follow-up Optimization: The problem explicitly asks if you can come up with an algorithm better than O(n²). Consider using a hash map (dictionary) to store previously seen numbers and their indices. This would allow you to find the complement in O(1) time, reducing the overall time complexity to O(n).
Edge Cases: Your solution handles the case where no pair is found by returning [-1, -1], which is good defensive programming.
Suggested Improvement:
Try implementing the hash map approach to achieve O(n) time complexity:
Interview Problem: 0-1 Knapsack Problem (Problem2.py)
Strengths:
Excellent space optimization using 1D DP array instead of 2D
Correct use of reverse iteration to maintain the 0-1 knapsack property
Clean, readable code with appropriate use of @staticmethod
Good documentation of time and space complexity in comments
The solution correctly handles the case where weights[i] > j by simply not updating dp[j] in that iteration
Areas for Improvement:
Variable naming could be slightly clearer. Using n for capacity and m for number of items is a bit unconventional (typically n refers to number of items). Consider capacity and num_items for better readability.
Could add a brief comment explaining why reverse iteration is used (to prevent item reuse)
Consider adding input validation or edge case handling (e.g., empty arrays)
VERDICT: PASS
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