Introduction

In this blog post, we will explore how to solve the LeetCode problem "1402" 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

A chef has collected data on the satisfaction level of his n dishes. Chef can cook any dish in 1 unit of time. Like-time coefficient of a dish is defined as the time taken to cook that dish including previous dishes multiplied by its satisfaction level i.e. time[i] satisfaction[i]. Return the maximum sum of like-time coefficient that the chef can obtain after preparing some amount of dishes. Dishes can be prepared in any order and the chef can discard some dishes to get this maximum value. Example 1: Input: satisfaction = [-1,-8,0,5,-9] Output: 14 Explanation: After Removing the second and last dish, the maximum total like-time coefficient will be equal to (-11 + 02 + 53 = 14). Each dish is prepared in one unit of time. Example 2: Input: satisfaction = [4,3,2] Output: 20 Explanation: Dishes can be prepared in any order, (21 + 32 + 4*3 = 20) Example 3: Input: satisfaction = [-1,-4,-5] Output: 0 Explanation: People do not like the dishes. No dish is prepared. Constraints: n == satisfaction.length 1 <= n <= 500 -1000 <= satisfaction[i] <= 1000

Explanation

• Sort the array: Sorting the satisfaction array in ascending order is crucial. This allows us to consider dishes with higher satisfaction levels towards the end, maximizing their contribution to the like-time coefficient.
  • Dynamic Programming: A dynamic programming approach helps to efficiently explore all possible combinations of dishes, building the solution incrementally by considering whether to include or exclude each dish.
  • Iterative calculation: We can optimize by iteratively calculating the like-time coefficient based on prefix sums and the sorted satisfaction array.

• Runtime Complexity: O(n log n) due to sorting. Storage Complexity: O(1) (in-place sorting can be used).

Code

    def maxSatisfaction(satisfaction):
    """
    Calculates the maximum sum of like-time coefficient that the chef can obtain.

    Args:
        satisfaction: A list of integers representing the satisfaction level of each dish.

    Returns:
        The maximum sum of like-time coefficient.
    """

    satisfaction.sort()
    n = len(satisfaction)
    max_satisfaction = 0
    current_satisfaction = 0
    total_sum = 0

    for i in range(n - 1, -1, -1):
        current_satisfaction += satisfaction[i]
        if current_satisfaction > 0:
            total_sum += current_satisfaction
            max_satisfaction = max(max_satisfaction, total_sum)
        else:
            break

    return max_satisfaction