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Problem solution · Python

Final Array State After K Multiplication Operations II

Final Array State After K Multiplication Operations II: a Python solution using heap or priority queue. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Heap or priority queue
Source
walkccc LeetCode Solutions
Length
49 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Heap or priority queue

For Final Array State After K Multiplication Operations II, the implementation repeatedly takes the currently best candidate from a heap while inserting newly available choices.

  1. Define the priority key and whether the smallest or largest item should lead.
  2. Push each candidate when it becomes eligible.
  3. Discard stale entries when necessary and process the best live candidate.

Code notes

  • 49 lines of Python from the credited upstream file 3266.py.
  • The implementation visibly relies on sequence storage, work queue.
  • No explicit loop blocks detected.

Complexity

Count heap pushes and pops; each normally contributes a logarithmic factor in the heap size.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeFinal Array State After K Multiplication Operations II · PythonPython
Use this to learn the idea, then write your own version.
class Solution:  def getFinalState(      self,      nums: list[int],      k: int,      multiplier: int  ) -> list[int]:    if multiplier == 1:      return nums     MOD = 1_000_000_007    n = len(nums)    maxNum = max(nums)    ans = [0] * n    minHeap = [(num, i) for i, num in enumerate(nums)]     heapq.heapify(minHeap)     # Keep multiplying the minimum number as close as possible to the maximum    # number in the array. After that, stop multiplying the minimum number    # because it will be greater than the maximum number in the array and break    # the circularity.    while k > 0 and minHeap[0][0] * multiplier <= maxNum:      num, i = heapq.heappop(minHeap)      heapq.heappush(minHeap, (num * multiplier, i))      k -= 1     sortedIndexedNums = sorted(minHeap)    multipliesPerNum, remainingK = divmod(k, n)     # Evenly distribute the remaining multiplications to each number by using    # fast exponentiation.    for index, (num, i) in enumerate(sortedIndexedNums):      sortedIndexedNums[index] = (          sortedIndexedNums[index][0] *          pow(multiplier, multipliesPerNum, MOD) % MOD, i)     # Distribute the remaining multiplications to the minimum `remainingK`    # numbers.    for index in range(remainingK):      sortedIndexedNums[index] = (          sortedIndexedNums[index][0] * multiplier % MOD,          sortedIndexedNums[index][1])     for num, i in sortedIndexedNums:      ans[i] = num     return ans

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