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This approach iteratively distributes candies to people in a sequence, considering one cycle at a time until all candies are allocated.
Time Complexity: O(sqrt(2 * candies)). This is because the linear sum of the sequence continues until all candies are exhausted, which resembles the behavior of an arithmetic series.
Space Complexity: O(num_people), since we maintain an array to store the number of candies for each person.
1def distribute_candies(candies, num_people):
2 result = [0] * num_people
3 i = 0
4 amount = 1
5 while candies > 0:
6 result[i % num_people] += min(candies, amount)
7 candies -= amount
8 amount += 1
9 i += 1
10 return result
11
12# Example usage:
13candies = 7
14num_people = 4
15print(distribute_candies(candies, num_people))This Python code allocates candies using a list to hold each person's share. The use of % num_people guarantees round-robin allocation. The min function ensures the distributed quantity never exceeds the available candies.
Using a mathematical approach, calculate full rounds of distribution first to optimize the solution as opposed to simulating step-by-step distribution.
Time Complexity: O(sqrt(2 * candies)) due to converging arithmetic series.
Space Complexity: O(num_people).
1#
Although this solution follows a similar process to the iterative one, it stops calculating after wrapping through all the necessary candies in full cycles using arithmetic logic for each complete set of distributions.