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2530. Maximal Score After Applying K Operations
Description
You are given a 0-indexed integer array nums
and an integer k
. You have a starting score of 0
.
In one operation:
- choose an index
i
such that0 <= i < nums.length
, - increase your score by
nums[i]
, and - replace
nums[i]
withceil(nums[i] / 3)
.
Return the maximum possible score you can attain after applying exactly k
operations.
The ceiling function ceil(val)
is the least integer greater than or equal to val
.
Example 1:
Input: nums = [10,10,10,10,10], k = 5 Output: 50 Explanation: Apply the operation to each array element exactly once. The final score is 10 + 10 + 10 + 10 + 10 = 50.
Example 2:
Input: nums = [1,10,3,3,3], k = 3 Output: 17 Explanation: You can do the following operations: Operation 1: Select i = 1, so nums becomes [1,4,3,3,3]. Your score increases by 10. Operation 2: Select i = 1, so nums becomes [1,2,3,3,3]. Your score increases by 4. Operation 3: Select i = 2, so nums becomes [1,1,1,3,3]. Your score increases by 3. The final score is 10 + 4 + 3 = 17.
Constraints:
1 <= nums.length, k <= 105
1 <= nums[i] <= 109
Solutions
Solution 1: Priority Queue (Max Heap)
To maximize the sum of scores, we need to select the element with the maximum value at each step. Therefore, we can use a priority queue (max heap) to maintain the element with the maximum value.
At each step, we take out the element with the maximum value $v$ from the priority queue, add $v$ to the answer, and replace $v$ with $\lceil \frac{v}{3} \rceil$, and then add it to the priority queue. After repeating this process $k$ times, we return the answer.
The time complexity is $O(n + k \times \log n)$, and the space complexity is $O(n)$ or $O(1)$. Here, $n$ is the length of the array $nums$.
-
class Solution { public long maxKelements(int[] nums, int k) { PriorityQueue<Integer> pq = new PriorityQueue<>((a, b) -> b - a); for (int v : nums) { pq.offer(v); } long ans = 0; while (k-- > 0) { int v = pq.poll(); ans += v; pq.offer((v + 2) / 3); } return ans; } }
-
class Solution { public: long long maxKelements(vector<int>& nums, int k) { priority_queue<int> pq(nums.begin(), nums.end()); long long ans = 0; while (k--) { int v = pq.top(); pq.pop(); ans += v; pq.push((v + 2) / 3); } return ans; } };
-
class Solution: def maxKelements(self, nums: List[int], k: int) -> int: h = [-v for v in nums] heapify(h) ans = 0 for _ in range(k): v = -heappop(h) ans += v heappush(h, -(ceil(v / 3))) return ans
-
func maxKelements(nums []int, k int) (ans int64) { h := &hp{nums} heap.Init(h) for ; k > 0; k-- { v := h.pop() ans += int64(v) h.push((v + 2) / 3) } return } type hp struct{ sort.IntSlice } func (h hp) Less(i, j int) bool { return h.IntSlice[i] > h.IntSlice[j] } func (h *hp) Push(v any) { h.IntSlice = append(h.IntSlice, v.(int)) } func (h *hp) Pop() any { a := h.IntSlice v := a[len(a)-1] h.IntSlice = a[:len(a)-1] return v } func (h *hp) push(v int) { heap.Push(h, v) } func (h *hp) pop() int { return heap.Pop(h).(int) }
-
function maxKelements(nums: number[], k: number): number { const pq = new MaxPriorityQueue(); nums.forEach(num => pq.enqueue(num)); let ans = 0; while (k > 0) { const v = pq.dequeue()!.element; ans += v; pq.enqueue(Math.floor((v + 2) / 3)); k--; } return ans; }
-
use std::collections::BinaryHeap; impl Solution { pub fn max_kelements(nums: Vec<i32>, k: i32) -> i64 { let mut pq = BinaryHeap::from(nums); let mut ans = 0; let mut k = k; while k > 0 { if let Some(v) = pq.pop() { ans += v as i64; pq.push((v + 2) / 3); k -= 1; } } ans } }