Introduction

In this blog post, we will explore how to solve the LeetCode problem "2099" 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 an integer array nums and an integer k. You want to find a subsequence of nums of length k that has the largest sum. Return any such subsequence as an integer array of length k. A subsequence is an array that can be derived from another array by deleting some or no elements without changing the order of the remaining elements. Example 1: Input: nums = [2,1,3,3], k = 2 Output: [3,3] Explanation: The subsequence has the largest sum of 3 + 3 = 6. Example 2: Input: nums = [-1,-2,3,4], k = 3 Output: [-1,3,4] Explanation: The subsequence has the largest sum of -1 + 3 + 4 = 6. Example 3: Input: nums = [3,4,3,3], k = 2 Output: [3,4] Explanation: The subsequence has the largest sum of 3 + 4 = 7. Another possible subsequence is [4, 3]. Constraints: 1 <= nums.length <= 1000 -105 <= nums[i] <= 105 1 <= k <= nums.length

Explanation

Here's a breakdown of the solution:

• Select Top k Elements: Identify the k largest elements in the input array nums. This can be done efficiently using techniques like heapq.nlargest in Python or sorting.
• Maintain Original Order: Because the problem asks for a subsequence, the relative order of elements in the original array must be preserved. We achieve this by iterating through the original nums array and including the top k elements in the correct order.

• Handle Duplicates: The solution needs to handle duplicate values correctly. A counter is used to track the remaining number of times each of the top k elements should be included in the subsequence.

• Time & Space Complexity: O(n log k) time complexity and O(n) space complexity.

Code

    import heapq
from collections import Counter

def maxSubsequence(nums, k):
    """
    Finds a subsequence of nums of length k that has the largest sum.

    Args:
        nums: An integer array.
        k: The desired length of the subsequence.

    Returns:
        An integer array representing the subsequence.
    """

    top_k_elements = heapq.nlargest(k, nums)
    counts = Counter(top_k_elements)
    result = []

    for num in nums:
        if num in counts and counts[num] > 0:
            result.append(num)
            counts[num] -= 1
            if len(result) == k:
                break

    return result