How do you solve Earliest Possible Day of Full Bloom on LeetCode?

Earliest Possible Day of Full Bloom (LeetCode #2136) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(n) space complexity. Key insight: Prioritize planting seeds with longer grow times first to minimize overall bloom time.

What is the time complexity of Earliest Possible Day of Full Bloom?

The optimal solution for Earliest Possible Day of Full Bloom runs in O(n log n) time and uses O(n) space. The time complexity is O(n log n) due to the sorting step, while the space complexity is O(n) for storing the seeds in a list.

What is the brute force solution for Earliest Possible Day of Full Bloom?

The brute force approach for Earliest Possible Day of Full Bloom is Brute Force, with O(n!) time complexity and O(n) space complexity. The brute force approach involves trying all possible orders of planting the seeds and calculating the day each flower blooms. This method is straightforward but inefficient as it explores all permutations. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Earliest Possible Day of Full Bloom?

Earliest Possible Day of Full Bloom uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting.

What are the interview tips for Earliest Possible Day of Full Bloom?

Always consider sorting when dealing with tasks that have different priorities. Explain your thought process clearly, especially when deciding on the order of operations. Be prepared to discuss the time and space complexities of your solution.

What are common mistakes when solving Earliest Possible Day of Full Bloom?

Failing to recognize the importance of the order of planting seeds. Overcomplicating the problem by trying to simulate every possible scenario.

#2136

Earliest Possible Day of Full Bloom

Hard

ArrayGreedySortingGreedySorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n!)	O(n log n)
Space	O(n)	O(n)

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Intuition

Time O(n log n)Space O(n)

The optimal solution involves sorting the seeds based on their grow time in descending order. This way, we prioritize planting seeds that take longer to grow first, minimizing the overall bloom time.

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Algorithm

5 steps

1Step 1: Create a list of tuples containing plant and grow times.
2Step 2: Sort this list by grow time in descending order.
3Step 3: Initialize variables to track the current day and the latest bloom day.
4Step 4: Iterate through the sorted list, updating the current day and calculating the bloom day for each seed.
5Step 5: Return the maximum bloom day.

solution.py8 lines

1def earliestBloomDay(plantTime, growTime):
2    seeds = sorted(zip(plantTime, growTime), key=lambda x: -x[1])
3    current_day = 0
4    max_bloom_day = 0
5    for p, g in seeds:
6        current_day += p
7        max_bloom_day = max(max_bloom_day, current_day + g)
8    return max_bloom_day

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Complexity note: The time complexity is O(n log n) due to the sorting step, while the space complexity is O(n) for storing the seeds in a list.

1Prioritize planting seeds with longer grow times first to minimize overall bloom time.
2Sorting based on grow time allows for an efficient planting strategy.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.