How do you solve Maximum Points After Enemy Battles on LeetCode?

Maximum Points After Enemy Battles (LeetCode #3207) 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: Greedy strategies often yield optimal solutions in problems involving choices and resources.

What is the time complexity of Maximum Points After Enemy Battles?

The optimal solution for Maximum Points After Enemy Battles runs in O(n log n) time and uses O(1) space. The sorting step takes O(n log n), and the single pass through the array takes O(n), making this approach efficient overall.

What is the brute force solution for Maximum Points After Enemy Battles?

The brute force approach for Maximum Points After Enemy Battles is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we try every possible combination of marking and battling enemies to see how many points we can accumulate. This method is straightforward but inefficient as it explores all possibilities. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Points After Enemy Battles?

Maximum Points After Enemy Battles uses the following concepts: Array, Greedy. The recommended patterns to study are: Greedy, Sorting, Array.

What are the interview tips for Maximum Points After Enemy Battles?

Always consider edge cases, such as when currentEnergy is initially zero. Think about how you can break down the problem into smaller parts. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving Maximum Points After Enemy Battles?

Not considering the possibility of marking enemies to regain energy. Overlooking the importance of sorting the enemies before processing.

#3207

Maximum Points After Enemy Battles

Medium

ArrayGreedyGreedySortingArray

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 solution leverages a greedy approach by first sorting the enemies based on their energy values. We prioritize battling weaker enemies to gain points and then use those points to battle stronger enemies.

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Algorithm

3 steps

1Step 1: Sort the enemyEnergies array in ascending order.
2Step 2: Initialize points to 0 and iterate through the sorted array.
3Step 3: For each enemy, if currentEnergy is sufficient, battle them to gain points, otherwise, if points are available, mark them to regain energy.

solution.py13 lines

1# Full working Python code
2
3def maxPointsOptimal(enemyEnergies, currentEnergy):
4    enemyEnergies.sort()
5    points = 0
6    for energy in enemyEnergies:
7        if currentEnergy >= energy:
8            points += 1
9            currentEnergy -= energy
10        elif points > 0:
11            currentEnergy += energy
12            points -= 1
13    return points

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

1Greedy strategies often yield optimal solutions in problems involving choices and resources.
2Sorting can simplify decision-making by allowing us to handle the smallest elements first.

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