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Solving Leetcode Interviews in Seconds with AI: Rearrange Array to Maximize Prefix Score

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2 min read

Introduction

In this blog post, we will explore how to solve the LeetCode problem "2587" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like Chatmagic, we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

Problem Statement

You are given a 0-indexed integer array nums. You can rearrange the elements of nums to any order (including the given order). Let prefix be the array containing the prefix sums of nums after rearranging it. In other words, prefix[i] is the sum of the elements from 0 to i in nums after rearranging it. The score of nums is the number of positive integers in the array prefix. Return the maximum score you can achieve. Example 1: Input: nums = [2,-1,0,1,-3,3,-3] Output: 6 Explanation: We can rearrange the array into nums = [2,3,1,-1,-3,0,-3]. prefix = [2,5,6,5,2,2,-1], so the score is 6. It can be shown that 6 is the maximum score we can obtain. Example 2: Input: nums = [-2,-3,0] Output: 0 Explanation: Any rearrangement of the array will result in a score of 0. Constraints: 1 <= nums.length <= 105 -106 <= nums[i] <= 106

Explanation

Here's the breakdown of the solution:

  • Prioritize Positive Numbers: The core idea is to maximize the score by placing positive numbers at the beginning of the rearranged array. This ensures that the prefix sums are more likely to remain positive.
  • Greedy Approach with Sorting: Sort the input array in descending order based on value. Iterate through the sorted array, accumulating the prefix sum. If the prefix sum is positive, increment the score. This greedy approach ensures the maximum number of positive prefix sums.

  • Runtime Complexity: O(n log n) due to sorting. Storage Complexity: O(n) in the worst-case scenario for sorting algorithms (e.g., merge sort), but could be O(1) if an in-place sorting algorithm like heapsort is used, modifying the original array.

Code

    def max_score(nums):
    """
    Calculates the maximum score achievable by rearranging the elements of nums.

    Args:
        nums: A list of integers.

    Returns:
        The maximum score.
    """

    nums.sort(reverse=True)  # Sort in descending order
    score = 0
    prefix_sum = 0
    for num in nums:
        prefix_sum += num
        if prefix_sum > 0:
            score += 1
        else:
            break  # No point in continuing if prefix sum becomes non-positive
    return score

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