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In this approach, we prioritize seeds with shorter planting times first. The idea is that we can start planting these seeds earlier, and consequently, they will finish blooming earlier. This might minimize the delay caused by longer-growing seeds.
Time Complexity: O(n log n) due to sorting. Space Complexity: O(n) for storing seeds in an array.
1def earliestFullBloom(plantTime, growTime):
2 seeds = sorted(zip(plantTime, growTime), key=lambda x: x[0])
3 current_day = 0
4 last_bloom_day = 0
5
6 for pt, gt in seeds:
7 current_day += pt
8 last_bloom_day = max(last_bloom_day, current_day + gt)
9
10 return last_bloom_day
11
12
13plantTime = [1, 4, 3]
14growTime = [2, 3, 1]
15result = earliestFullBloom(plantTime, growTime)
16print(f"Earliest Full Bloom Day: {result}")
17The Python solution utilizes zip and sorted to manage the sorting and ordering of planting and growing operations, keeping track of cumulative days for bloom calculations.
This strategy focuses on seeds with longer grow times first. By doing so, we minimize delays caused by excessively long growing periods by ensuring they finish growing as soon as possible.
Time Complexity: O(n log n) because of sorting operations. Space Complexity: O(n) for storing seed data.
1
JavaScript’s strategy involves mapping and sorting seeds according to their grow times. The implementation calculates cumulative days for blooming in an ordered manner.