Is Most Profit Assigning Work easy or hard?

Most Profit Assigning Work is considered a Medium problem. The challenge lies in recognizing that sorting plus a greedy running maximum removes the need to check every job for every worker, reducing a potential O(n*m) solution to near linear time after sorting.

Most Profit Assigning Work Python/Java solution

Typical implementations pair job difficulty and profit, sort them, and iterate with a pointer while maintaining the best profit seen so far. This approach is commonly implemented in Python, Java, C++, and JavaScript using built-in sorting and simple loops.

How to solve Most Profit Assigning Work in O(n)?

Pure O(n) is not possible for arbitrary inputs because jobs must be ordered by difficulty. However, after sorting jobs and workers, the assignment step becomes O(n + m). A prefix maximum profit array or two-pointer scan allows each worker to be processed in constant time after preprocessing.

What is the best approach for Most Profit Assigning Work?

The most common approach sorts jobs by difficulty and workers by skill, then uses a greedy sweep to track the maximum profit seen so far. As you iterate through workers, you advance through all jobs they can perform and keep the best profit available. This runs in O((n + m) log n) time due to sorting and O(1) extra space.

Is Most Profit Assigning Work asked at Google/Amazon/Meta?

Variants of this problem appear in interviews at companies like Amazon and Google because it tests greedy thinking, sorting strategies, and efficient scanning with two pointers. Interviewers use it to evaluate whether candidates can avoid brute-force comparisons.

What data structure is used in Most Profit Assigning Work?

The solution primarily uses arrays combined with sorting and greedy scanning. Some implementations also use prefix arrays for maximum profit tracking or binary search on sorted difficulties for fast lookups.

What is the time complexity of Most Profit Assigning Work?

The optimal solution runs in O((n + m) log n) time where n is the number of jobs and m is the number of workers. The sorting step dominates the runtime, while the greedy scan processes each job and worker once.

Description Solution Video 37:28

826. Most Profit Assigning Work

Medium56.4% acceptance5 companies

You have n jobs and m workers. You are given three arrays: difficulty, profit, and worker where:

difficulty[i] and profit[i] are the difficulty and the profit of the i^th job, and
worker[j] is the ability of j^th worker (i.e., the j^th worker can only complete a job with difficulty at most worker[j]).

Every worker can be assigned at most one job, but one job can be completed multiple times.

For example, if three workers attempt the same job that pays $1, then the total profit will be $3. If a worker cannot complete any job, their profit is $0.

Return the maximum profit we can achieve after assigning the workers to the jobs.

Example 1:

Input: difficulty = [2,4,6,8,10], profit = [10,20,30,40,50], worker = [4,5,6,7]
Output: 100
Explanation: Workers are assigned jobs of difficulty [4,4,6,6] and they get a profit of [20,20,30,30] separately.

Example 2:

Input: difficulty = [85,47,57], profit = [24,66,99], worker = [40,25,25]
Output: 0

Constraints:

n == difficulty.length
n == profit.length
m == worker.length
1 <= n, m <= 10⁴
1 <= difficulty[i], profit[i], worker[i] <= 10⁵

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NetEase DoorDash

Google Amazon Meta

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826. Most Profit Assigning Work

Asked at 5 companies

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