You are given an integer array nums, an integer k, and an integer multiplier.
You need to perform k operations on nums. In each operation:
x in nums. If there are multiple occurrences of the minimum value, select the one that appears first.x with x * multiplier.Return an integer array denoting the final state of nums after performing all k operations.
Example 1:
Input: nums = [2,1,3,5,6], k = 5, multiplier = 2
Output: [8,4,6,5,6]
Explanation:
| Operation | Result |
|---|---|
| After operation 1 | [2, 2, 3, 5, 6] |
| After operation 2 | [4, 2, 3, 5, 6] |
| After operation 3 | [4, 4, 3, 5, 6] |
| After operation 4 | [4, 4, 6, 5, 6] |
| After operation 5 | [8, 4, 6, 5, 6] |
Example 2:
Input: nums = [1,2], k = 3, multiplier = 4
Output: [16,8]
Explanation:
| Operation | Result |
|---|---|
| After operation 1 | [4, 2] |
| After operation 2 | [4, 8] |
| After operation 3 | [16, 8] |
Constraints:
1 <= nums.length <= 1001 <= nums[i] <= 1001 <= k <= 101 <= multiplier <= 5In #3264 Final Array State After K Multiplication Operations I, the goal is to simulate k operations where the smallest value in the array is multiplied by a given multiplier. A direct simulation that scans the entire array each time works, but it is not the most efficient for larger inputs.
A more optimal strategy uses a min-heap (priority queue). Store each element along with its index in the heap so the smallest value can be accessed quickly. For every operation, remove the minimum element, multiply it by the given multiplier, and push the updated value back into the heap. This efficiently maintains the order of elements as values change. After completing k operations, reconstruct the final array state.
This approach ensures efficient repeated minimum lookups and updates. The heap operations dominate the runtime, giving a complexity of O((n + k) log n), while the additional space required for the heap is O(n).
| Approach | Time Complexity | Space Complexity |
|---|---|---|
| Min-Heap (Priority Queue) Simulation | O((n + k) log n) | O(n) |
NeetCodeIO
Use these hints if you're stuck. Try solving on your own first.
Maintain sorted pairs <code>(nums[index], index)</code> in a priority queue.
Simulate the operation <code>k</code> times.
Watch expert explanations and walkthroughs
Jot down your thoughts, approach, and key learnings
Yes, you can simulate the process by scanning the array to find the smallest element in each of the k operations. However, this results in O(n * k) time complexity, which is less efficient than the heap-based solution.
While the exact problem may not always appear, similar heap and simulation problems are common in FAANG-style interviews. Understanding how to use priority queues for repeated minimum or maximum updates is an important interview skill.
A priority queue implemented as a min-heap is the best choice. It allows quick retrieval and updates of the smallest element after each multiplication operation, keeping the simulation efficient.
The optimal approach uses a min-heap (priority queue) to repeatedly access the smallest element efficiently. Each operation removes the smallest value, multiplies it, and pushes it back into the heap. This avoids scanning the entire array every time.