You are given a list of jobs, each with a start time, end time, and profit. You want to schedule jobs to maximize total profit without overlapping. Which algorithmic pattern best fits this problem?

easy🔍 Pattern Recognition Q1 of Q15

Dynamic Programming: Knapsack - Maximum Profit in Job Scheduling

AGreedy interval scheduling based on earliest finish time

BDynamic programming with sorting and binary search for compatible jobs

CDepth-first search exploring all subsets without memoization

DBreadth-first search on job graph with topological sorting

Step-by-Step Solution

Solution:

Step 1: Identify problem constraints
The problem requires maximizing profit with no overlapping jobs, which is a classic weighted interval scheduling problem.
Step 2: Recognize suitable algorithmic pattern
Greedy alone fails due to weights; brute force is exponential; DP with sorting by end time and binary search for compatible jobs efficiently solves it.
Final Answer:
Option B -> Option B
Quick Check:
Weighted interval scheduling -> DP + binary search [OK]

Quick Trick: Weighted intervals -> DP with binary search [OK]

Common Mistakes:

MISTAKES

Assuming greedy always works for scheduling
Confusing unweighted with weighted interval scheduling

Trap Explanation:

PITFALL

Many candidates pick greedy because it works for unweighted intervals, but it fails with profits.

Interviewer Note:

CONTEXT

Tests candidate's ability to recognize weighted interval scheduling pattern.

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More Dynamic Programming: Knapsack Quizzes

You are given a list of jobs, each with a start time, end time, and profit. You want to schedule jobs to maximize total profit without overlapping. Which algorithmic pattern best fits this problem?

Step 1: Identify problem constraints

Step 2: Recognize suitable algorithmic pattern

Final Answer:

Quick Check:

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