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

Maximum Total Reward Using Operations I (LeetCode #3180) 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 optimal selection.

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

The brute force approach for Maximum Total Reward Using Operations I is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute-force approach, we explore all possible combinations of indices to mark and calculate the total reward for each combination. This method is straightforward but inefficient as it checks every possibility. 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 I?

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

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

Explain your thought process clearly during the interview. Discuss both brute-force and optimal approaches to show depth of understanding. Practice coding the solution to avoid syntax errors during interviews.

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

Not sorting the array before processing. Failing to check if the current reward is greater than the total reward.

#3180

Maximum Total Reward Using Operations I

Medium

ArrayDynamic ProgrammingGreedySorting

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 rewardValues array first. By processing the rewards in ascending order, we can ensure that we always add the largest possible rewards that exceed the current total reward.

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Algorithm

3 steps

1Step 1: Sort the rewardValues array in ascending order.
2Step 2: Initialize total reward x to 0.
3Step 3: Iterate through the sorted array and add each reward to x if it is greater than x.

solution.py9 lines

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

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step, which is the most time-consuming operation here. The subsequent iteration through the array is linear, O(n).

1Sorting the rewards allows for optimal selection.
2Always add rewards in increasing order to maximize total.

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