How do you solve Maximum Total Reward Using Operations II on LeetCode?

Maximum Total Reward Using Operations II (LeetCode #3181) 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 the rewards allows for a more efficient selection process.

What is the brute force solution for Maximum Total Reward Using Operations II?

The brute force approach for Maximum Total Reward Using Operations II is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves trying every possible combination of indices to see which ones can be marked to maximize the total reward. This method is straightforward but inefficient as it checks all combinations. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Total Reward Using Operations II?

Maximum Total Reward Using Operations II uses the following concepts: Array, Dynamic Programming, Bit Manipulation. The recommended patterns to study are: Greedy Algorithms, Sorting.

What are the interview tips for Maximum Total Reward Using Operations II?

Explain your thought process clearly while coding. Mention the importance of sorting in your approach. Be prepared to discuss time and space complexity.

What are common mistakes when solving Maximum Total Reward Using Operations II?

Not sorting the array before processing. Failing to check if a reward can be added based on the current total.

#3181

Maximum Total Reward Using Operations II

Hard

ArrayDynamic ProgrammingBit ManipulationGreedy AlgorithmsSorting

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 involves sorting the reward values first and then iterating through the sorted list. This way, we can efficiently determine which rewards can be added to the total based on the current value of x.

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Algorithm

3 steps

1Step 1: Sort the rewardValues array in ascending order.
2Step 2: Initialize total reward x to 0 and max reward to 0.
3Step 3: Iterate through the sorted array, adding values to x if they are greater than the current x, and update the max reward.

solution.py11 lines

1# Full working Python code
2
3def maxReward(rewardValues):
4    rewardValues.sort()
5    total = 0
6    max_reward = 0
7    for value in rewardValues:
8        if value > total:
9            total += value
10            max_reward = total
11    return max_reward

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step, which is the most time-consuming operation. The space complexity is O(1) as we are only using a few variables for tracking totals.

1Sorting the rewards allows for a more efficient selection process.
2Always check if the current reward can be added based on the current total reward.

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