You have k bags. You are given a 0-indexed integer array weights where weights[i] is the weight of the ith marble. You are also given the integer k.
Divide the marbles into the k bags according to the following rules:
The score after distributing the marbles is the sum of the costs of all the k bags.
Return the difference between the maximum and minimum scores among marble distributions.
Examples
Example 1
Input: weights = [1,3,5,1], k = 2
Output: 4
Explanation:
The distribution [1],[3,5,1] results in the minimal score of (1+1) + (3+1) = 6.
The distribution [1,3],[5,1], results in the maximal score of (1+3) + (5+1) = 10.
Thus, we return their difference 10 - 6 = 4.
Example 2
Input: weights = [1, 3], k = 2
Output: 0
Explanation: The only distribution possible is [1],[3].
Since both the maximal and minimal score are the same, we return 0.
Constraints
1 <= k <= weights.length <= 105
1 <= weights[i] <= 109
2551. Put Marbles in Bags
Hard
50 Points
Array
Greedy
Sorting
Heap (Priority Queue)
You have k bags. You are given a 0-indexed integer array weights where weights[i] is the weight of the ith marble. You are also given the integer k.
Divide the marbles into the k bags according to the following rules:
The score after distributing the marbles is the sum of the costs of all the k bags.
Return the difference between the maximum and minimum scores among marble distributions.
Examples
Example 1
Input: weights = [1,3,5,1], k = 2
Output: 4
Explanation:
The distribution [1],[3,5,1] results in the minimal score of (1+1) + (3+1) = 6.
The distribution [1,3],[5,1], results in the maximal score of (1+3) + (5+1) = 10.
Thus, we return their difference 10 - 6 = 4.
Example 2
Input: weights = [1, 3], k = 2
Output: 0
Explanation: The only distribution possible is [1],[3].
Since both the maximal and minimal score are the same, we return 0.