1 problem completed

[BharathVuppala96]

No description provided.

[super30admin]

Your solution correctly implements the recursive backtracking approach and produces the right results. However, there are some areas for improvement to make it more efficient and idiomatic in Python:

1. Avoid Unnecessary List Copies: Instead of creating a new list copy at every recursive call (which is O(n) in the length of the list), you should use a single list and backtrack by popping the last element after the recursive call. This will save memory and time.

   For example, you can change the helper function to:

   def helper(self, candidates, target, i, path):
        if target < 0:
            return
        if target == 0:
            self.result.append(path[:])  # append a copy of the current path
            return
        if i == len(candidates):
            return
   
        # Skip the current candidate
        self.helper(candidates, target, i+1, path)
   
        # Take the current candidate
        path.append(candidates[i])
        self.helper(candidates, target - candidates[i], i, path)
        path.pop()  # backtrack by removing the last element

   This way, you avoid creating multiple copies and only create a copy when you add to the result.

2. Prune Early by Sorting: Although not necessary due to the constraints, you could sort the candidates first and then break early if the current candidate exceeds the remaining target. This can speed up the solution.

3. Use Default Arguments Carefully: Be cautious when using mutable default arguments (like [] in the function definition). In your case, you are passing a new list in the initial call, so it's safe. But it's good practice to avoid mutable defaults.

4. Naming Conventions: The variable names are clear, but you could use more descriptive names like index instead of i and current instead of path.

5. Recursion Depth: Given the constraints, recursion is acceptable, but iterative solutions or using a stack might be considered for very deep recursion. However, for this problem, recursion is fine.

By making these changes, your solution will be more efficient and follow Python best practices.