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This approach leverages binary search to efficiently minimize the penalty. The idea is to use binary search to determine the minimum possible 'penalty', where penalty is defined as the maximum number of balls allowed in any bag.
We set our binary search range from 1 to the maximum number of balls in any bag initially. For each mid value (potential penalty), we check if it's possible to achieve this penalty with the given number of operations. We do this by calculating how many operations we'd need to make every bag have at most 'mid' number of balls. If we can do that within the allowed number of operations, we adjust our search range to potentially lower penalties; otherwise, we increase our search range.
Time Complexity: O(n log(max(nums)))
Space Complexity: O(1)
1function canAchievePenalty(nums, maxOperations, penalty) {
2 let operations = 0;
3 for (let num of nums) {
4 if (num > penalty) {
5 operations += Math.floor((num - 1) / penalty);
6 }
7 if (operations > maxOperations) {
8 return false;
9 }
10 }
11 return true;
12}
13
14function minimumPenalty(nums, maxOperations) {
15 let left = 1, right = Math.max(...nums);
16 while (left < right) {
17 const mid = Math.floor((left + right) / 2);
18 if (canAchievePenalty(nums, maxOperations, mid)) {
19 right = mid;
20 } else {
21 left = mid + 1;
22 }
23 }
24 return left;
25}
26
27console.log(minimumPenalty([9], 2)); // Output: 3The JavaScript solution focuses on determining the smallest penalty value possible through a binary search mechanism. The canAchievePenalty function checks if a particular penalty value can be satisfied with the available operations, guiding the binary search process.
Another approach is to continuously divide the largest bags using a priority queue to keep track of the largest bag sizes. By always splitting the largest bag first, we aim to reduce the penalty faster. This works similarly to the binary search approach in terms of dividing the problem space but uses a different method to handle priorities.
Time Complexity: O(n log n + n log(max(nums)))
Space Complexity: O(1)
1
The greedy approach employs priority queue logic indirectly by sorting the array in descending order initially. It repeatedly considers the largest current value, using a strategy similar to the binary search solution.