How do you solve Maximum Score Of Spliced Array on LeetCode?

Maximum Score Of Spliced Array (LeetCode #2321) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Swapping elements can lead to significant changes in the sums of the arrays.

What is the brute force solution for Maximum Score Of Spliced Array?

The brute force approach for Maximum Score Of Spliced Array is Brute Force, with O(n²) time complexity and O(1) space complexity. We can try every possible subarray swap between nums1 and nums2. For each swap, we calculate the sums of both arrays and determine the maximum score. This approach is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximum Score Of Spliced Array?

Maximum Score Of Spliced Array uses the following concepts: Array, Dynamic Programming. The recommended patterns to study are: Dynamic Programming, Array Manipulation.

What are the interview tips for Maximum Score Of Spliced Array?

Always start with a brute-force approach to understand the problem better. Look for patterns in the problem that can simplify your solution. Consider edge cases, such as when arrays are of length 1 or when they contain identical elements.

What are common mistakes when solving Maximum Score Of Spliced Array?

Not considering the case where no swaps are made. Overcomplicating the problem by trying to brute-force all subarrays without recognizing patterns.

#2321

Maximum Score Of Spliced Array

Hard

ArrayDynamic ProgrammingDynamic ProgrammingArray Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(1)

Instead of checking all possible swaps, we can calculate the potential gain from swapping elements and use that to determine the maximum score efficiently. This approach focuses on the difference between the two arrays.

⚙️

Algorithm

5 steps

1Step 1: Calculate the initial sums of nums1 and nums2.
2Step 2: Initialize a variable to track the maximum score.
3Step 3: For each index, calculate the gain from swapping nums1[i] with nums2[i].
4Step 4: Determine the maximum score by considering the initial sums and the maximum gain.
5Step 5: Return the maximum score.

solution.py11 lines

1def maximumScore(nums1, nums2):
2    initial_sum1 = sum(nums1)
3    initial_sum2 = sum(nums2)
4    max_score = max(initial_sum1, initial_sum2)
5    n = len(nums1)
6    for i in range(n):
7        gain = nums2[i] - nums1[i]
8        new_sum1 = initial_sum1 + gain
9        new_sum2 = initial_sum2 - gain
10        max_score = max(max_score, new_sum1, new_sum2)
11    return max_score

ℹ

Complexity note: This complexity is linear because we only iterate through the arrays once to calculate the potential gains from swapping, making it much more efficient than the brute-force approach.

1Swapping elements can lead to significant changes in the sums of the arrays.
2The maximum score can be achieved by considering the potential gains from each swap.

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