Leetcode #1723: Find Minimum Time to Finish All Jobs
In this guide, we solve Leetcode #1723 Find Minimum Time to Finish All Jobs 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 an integer array jobs, where jobs[i] is the amount of time it takes to complete the ith job. There are k workers that you can assign jobs to.
Quick Facts
- Difficulty: Hard
- Premium: No
- Tags: Bit Manipulation, Array, Dynamic Programming, Backtracking, Bitmask
Intuition
The problem breaks into overlapping subproblems, so caching results prevents exponential repetition.
A carefully chosen DP state captures exactly what we need to build the final answer.
Approach
Define the DP state and recurrence, then compute states in the correct order.
Optionally compress space once the recurrence is clear.
Steps:
- Choose a DP state definition.
- Write the recurrence and base cases.
- Compute states in the correct order.
Example
Input: jobs = [3,2,3], k = 3
Output: 3
Explanation: By assigning each person one job, the maximum time is 3.
Python Solution
class Solution:
def minimumTimeRequired(self, jobs: List[int], k: int) -> int:
def dfs(i):
nonlocal ans
if i == len(jobs):
ans = min(ans, max(cnt))
return
for j in range(k):
if cnt[j] + jobs[i] >= ans:
continue
cnt[j] += jobs[i]
dfs(i + 1)
cnt[j] -= jobs[i]
if cnt[j] == 0:
break
cnt = [0] * k
jobs.sort(reverse=True)
ans = inf
dfs(0)
return ans
Complexity
The time complexity is O(n·m) (typical). The space complexity is O(n·m) or optimized.
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.