How do you solve Minimum Initial Energy to Finish Tasks on LeetCode?

Minimum Initial Energy to Finish Tasks (LeetCode #1665) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Sorting tasks by their minimum energy requirement allows for a more efficient greedy approach.

What is the time complexity of Minimum Initial Energy to Finish Tasks?

The optimal solution for Minimum Initial Energy to Finish Tasks runs in O(n log n) time and uses O(1) space. The sorting step takes O(n log n), and the subsequent single pass through the tasks is O(n), making this approach efficient overall.

What is the brute force solution for Minimum Initial Energy to Finish Tasks?

The brute force approach for Minimum Initial Energy to Finish Tasks is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach tries every possible initial energy level to see if it allows completing all tasks. This method is straightforward but inefficient as it checks each energy level sequentially. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Minimum Initial Energy to Finish Tasks?

Minimum Initial Energy to Finish Tasks uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting.

What are the interview tips for Minimum Initial Energy to Finish Tasks?

Always consider edge cases, such as tasks that cannot be completed with the current energy. Explain your thought process clearly while coding; it helps interviewers understand your approach. Practice explaining your solution as you code, as this simulates the interview environment.

What are common mistakes when solving Minimum Initial Energy to Finish Tasks?

Failing to sort the tasks before processing them can lead to incorrect results. Not correctly updating the current energy and required energy during the iteration.

#1665

Minimum Initial Energy to Finish Tasks

Hard

ArrayGreedySortingGreedySorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n log n)
Space	O(1)	O(1)

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Intuition

Time O(n log n)Space O(1)

The optimal approach sorts the tasks based on their minimum energy requirement and calculates the necessary initial energy using a greedy strategy. This ensures we always have enough energy to complete the tasks in the best order.

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Algorithm

4 steps

1Step 1: Sort the tasks based on their minimum energy requirement.
2Step 2: Initialize current energy and required energy to 0.
3Step 3: Iterate through the sorted tasks, updating current energy and checking if it meets the minimum requirement.
4Step 4: If current energy is less than the minimum required for a task, calculate the additional energy needed and update the required energy.

solution.py13 lines

1# Full working Python code
2
3def minimum_initial_energy(tasks):
4    tasks.sort(key=lambda x: x[1])
5    current_energy = 0
6    required_energy = 0
7    for actual, minimum in tasks:
8        required_energy = max(required_energy, minimum - current_energy)
9        current_energy += actual
10    return required_energy
11
12tasks = [[1,2],[2,4],[4,8]]
13print(minimum_initial_energy(tasks))

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Complexity note: The sorting step takes O(n log n), and the subsequent single pass through the tasks is O(n), making this approach efficient overall.

1Sorting tasks by their minimum energy requirement allows for a more efficient greedy approach.
2The relationship between actual and minimum energy spent is crucial for determining the required initial energy.

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