Leetcode #2323: Find Minimum Time to Finish All Jobs II
In this guide, we solve Leetcode #2323 Find Minimum Time to Finish All Jobs II in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given two 0-indexed integer arrays jobs and workers of equal length, where jobs[i] is the amount of time needed to complete the ith job, and workers[j] is the amount of time the jth worker can work each day. Each job should be assigned to exactly one worker, such that each worker completes exactly one job.
Quick Facts
- Difficulty: Medium
- Premium: Yes
- Tags: Greedy, Array, Sorting
Intuition
A locally optimal choice leads to a globally optimal result for this structure.
That means we can commit to decisions as we scan without backtracking.
Approach
Sort or preprocess if needed, then repeatedly take the best available local choice.
Maintain the minimal state necessary to validate the greedy decision.
Steps:
- Sort or preprocess as needed.
- Iterate and pick the best local option.
- Track the current solution.
Example
Input: jobs = [5,2,4], workers = [1,7,5]
Output: 2
Explanation:
- Assign the 2nd worker to the 0th job. It takes them 1 day to finish the job.
- Assign the 0th worker to the 1st job. It takes them 2 days to finish the job.
- Assign the 1st worker to the 2nd job. It takes them 1 day to finish the job.
It takes 2 days for all the jobs to be completed, so return 2.
It can be proven that 2 days is the minimum number of days needed.
Python Solution
class Solution:
def minimumTime(self, jobs: List[int], workers: List[int]) -> int:
jobs.sort()
workers.sort()
return max((a + b - 1) // b for a, b in zip(jobs, workers))
Complexity
The time complexity is , and the space complexity is . The space complexity is .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.