complete backtracking 1

[MSShravan]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of backtracking problems. Let's evaluate each file separately:

1. combination-sum.py:

   • Correctness: The solution correctly implements the combination sum problem using backtracking. It avoids duplicate combinations by using the 'start' parameter.
   • Time Complexity: The stated O(N^target) is correct for the worst case.
   • Space Complexity: O(target) is accurate considering the recursion depth.
   • Code Quality: The code is clean and well-structured. The use of a nested function for backtracking is appropriate.
   • Efficiency: The solution is optimal for this problem. No obvious improvements needed.

2. expression-add-operators.py:

   • Correctness: The solution handles the expression add operators problem correctly, including edge cases like leading zeros.
   • Time Complexity: The stated O(4^N) is correct considering the 4 choices (+, -, *, no operator) at each step.
   • Space Complexity: O(N) is accurate for the recursion depth.
   • Code Quality: The code is well-organized. The comments about leading zeros are helpful.
   • Efficiency: The solution handles all cases efficiently. The multiplication case is correctly handled by tracking the previous operand.

General strengths:

• Both solutions follow backtracking patterns correctly
• Time and space complexity analyses are accurate
• Code is readable with good variable naming
• Edge cases are considered (like leading zeros in the second problem)

Areas for improvement:

• Adding docstrings to explain the functions would improve documentation
• In the combination sum problem, sorting the candidates first could potentially help with early termination in some cases
• The second problem could benefit from a brief comment explaining the multiplication case logic