Leetcode #2530: Maximal Score After Applying K Operations
In this guide, we solve Leetcode #2530 Maximal Score After Applying K Operations in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given a 0-indexed integer array nums and an integer k. You have a starting score of 0.
Quick Facts
- Difficulty: Medium
- Premium: No
- Tags: Greedy, Array, Heap (Priority Queue)
Intuition
A locally optimal choice leads to a globally optimal result for this structure.
That means we can commit to decisions as we scan without backtracking.
Approach
Sort or preprocess if needed, then repeatedly take the best available local choice.
Maintain the minimal state necessary to validate the greedy decision.
Steps:
- Sort or preprocess as needed.
- Iterate and pick the best local option.
- Track the current solution.
Example
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.
Python Solution
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
Complexity
The time complexity is , and the space complexity is or . The space complexity is or .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.