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This approach uses a heap to dynamically maintain workers while iterating over them sorted by wage-to-quality ratio. The goal is to keep the sum of qualities of the selected workers minimal while ensuring all conditions are satisfied. We sort all workers by their wage-to-quality ratio because for each worker, to satisfy both their minimum wage and relative payment constraints, each selected worker must be paid at least this ratio times their quality.
Time Complexity: O(n log n) due to sorting, and O(n log k) for heap operations.
Space Complexity: O(n) for the sorted list of workers or the heap.
The Java solution maintains k workers with minimal quality by utilizing a max-heap. Each worker is evaluated based on a sorted order of their wage-to-quality ratio. The quality of each worker is considered, updating the heap appropriately, and calculating the smallest payment necessary when there are exactly k workers in the heap.
This approach focuses on sorting workers by the ratio of their wage expectation to quality. By calculating this ratio, we can use it as a reference point to determine the required payment for every possible group of k workers. Selecting the right workers and calculating the minimum payment by understanding proportional wages can be done by iterating over sorted worker lists using two indices, capturing the required details and performing the necessary calculations.
Time Complexity: O(n log n) due to sorting, and O(n log n) due to managing heap.
Space Complexity: O(n) for worker list and heap storage.
1class Solution:
2 def mincostToHireWorkers(self, quality, wage, k):
3 workers = sorted((w / q, q) for w, q in zip(wage, quality))
4 ans = float('inf')
5 sumq = 0
6 pool = []
7
8 for r, q in workers:
9 heapq.heappush(pool, -q)
10 sumq += q
11
12 if len(pool) > k:
13 sumq += heapq.heappop(pool)
14
15 if len(pool) == k:
16 ans = min(ans, r * sumq)
17
18 return ansHere, the Python code sorts workers by their ratio of wage to quality. A max-heap manages the quality selection, and when a potential solution with k workers is found, the resultant cost is calculated. The approach makes efficient use of sorted ratios and quality management to derive the answer.