Learning On the Way

1235. Maximum Profit in Job Scheduling

We have n jobs, where every job is scheduled to be done from startTime[i] to endTime[i], obtaining a profit of profit[i]. You're given the startTime, endTime and profit arrays, return the maximum profit you can take such that there are no two jobs in the subset with overlapping time range. If you choose a job that ends at time X you will be able to start another job that starts at time X.

Sort by start time

def jobScheduling(self, startTime: List[int], endTime: List[int], profit: List[int]) -> int: 
    from collections import deque 
    from bisect import bisect_left, bisect_right 
        
    events = sorted(zip(startTime, endTime, profit)) 
        
    dp = deque() 
    dp.appendleft((float('inf'), 0)) 
        
    for i in range(len(events) - 1, -1, -1): 
        start, end, profit = events[i] 
            
        idx = bisect_left(dp, (end, -float('inf'))) 
        if dp[idx][1] + profit > dp[0][1]: 
            dp.appendleft((start, dp[idx][1] + profit)) 
                            
    return dp[0][1]

Sort by end time

def jobScheduling(self, startTime, endTime, profit): 
    jobs = sorted(zip(startTime, endTime, profit), key=lambda v: v[1]) 
    dp = [[0, 0]] 
    for s, e, p in jobs: 
        i = bisect.bisect_right(dp, [s, float('inf')]) - 1 
        i = min(i, len(dp) - 1) 
        if dp[i][1] + p > dp[-1][1]: 
            dp.append([e, dp[i][1] + p]) 
    return dp[-1][1]