2551. Put Marbles in Bags
Description
You have k
bags. You are given a 0indexed integer array weights
where weights[i]
is the weight of the i^{th}
marble. You are also given the integer k.
Divide the marbles into the k
bags according to the following rules:
 No bag is empty.
 If the
i^{th}
marble andj^{th}
marble are in a bag, then all marbles with an index between thei^{th}
andj^{th}
indices should also be in that same bag.  If a bag consists of all the marbles with an index from
i
toj
inclusively, then the cost of the bag isweights[i] + weights[j]
.
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.
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 <= 10^{5}
1 <= weights[i] <= 10^{9}
Solutions
Solution 1: Problem Transformation + Sorting
We can transform the problem into: dividing the array weights
into $k$ consecutive subarrays, that is, we need to find $k1$ splitting points, each splitting point’s cost is the sum of the elements on the left and right of the splitting point. The difference between the sum of the costs of the largest $k1$ splitting points and the smallest $k1$ splitting points is the answer.
Therefore, we can process the array weights
and transform it into an array arr
of length $n1$, where arr[i] = weights[i] + weights[i+1]
. Then we sort the array arr
, and finally calculate the difference between the sum of the costs of the largest $k1$ splitting points and the smallest $k1$ splitting points.
The time complexity is $O(n \times \log n)$, and the space complexity is $O(n)$. Where $n$ is the length of the array weights
.









