Introduction

In this blog post, we will explore how to solve the LeetCode problem "1800" 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

Given an array of positive integers nums, return the maximum possible sum of an strictly increasing subarray in nums. A subarray is defined as a contiguous sequence of numbers in an array. Example 1: Input: nums = [10,20,30,5,10,50] Output: 65 Explanation: [5,10,50] is the ascending subarray with the maximum sum of 65. Example 2: Input: nums = [10,20,30,40,50] Output: 150 Explanation: [10,20,30,40,50] is the ascending subarray with the maximum sum of 150. Example 3: Input: nums = [12,17,15,13,10,11,12] Output: 33 Explanation: [10,11,12] is the ascending subarray with the maximum sum of 33. Constraints: 1 <= nums.length <= 100 1 <= nums[i] <= 100

Explanation

Here's the solution to find the maximum sum of a strictly increasing subarray:

• Dynamic Programming Approach: Iterate through the array, maintaining a current_sum representing the sum of the current increasing subarray and a max_sum to store the maximum increasing subarray sum found so far.
• Conditional Update: If the current element is greater than the previous, extend the current increasing subarray by adding the current element to current_sum. Otherwise, start a new increasing subarray with the current element.

• Maximize Sum: In each step, update max_sum with the maximum of max_sum and current_sum.

• Runtime Complexity: O(n), Storage Complexity: O(1)

Code

    def max_ascending_sum(nums):
    """
    Finds the maximum possible sum of a strictly increasing subarray in nums.

    Args:
        nums: A list of positive integers.

    Returns:
        The maximum sum of a strictly increasing subarray.
    """
    max_sum = nums[0]
    current_sum = nums[0]

    for i in range(1, len(nums)):
        if nums[i] > nums[i - 1]:
            current_sum += nums[i]
        else:
            current_sum = nums[i]
        max_sum = max(max_sum, current_sum)

    return max_sum