Maximum Profit in Job Scheduling | DP + Binary Search | LeetCode | DSA Journey | Day 92

Welcome to Day 92 of my DSA Journey! Today, I’m solving Maximum Profit in Job Scheduling — a classic Dynamic Programming + Binary Search problem where we need to select non-overlapping jobs while maximizing total profit. 🧩 Problem Overview You are given several jobs, where each job has: 🕐 startTime 🕐 endTime 💰 profit The goal is to choose a set of non-overlapping jobs that gives the maximum total profit. For example, if one job ends before another job starts, both jobs can be selected. 💡 Key Idea This problem can be solved using Weighted Interval Scheduling. First, we sort the jobs by their start time. Then we use Dynamic Programming: dp[i] = maximum profit we can earn starting from job i For every job, we have two choices: 1️⃣ Take the current job Add its profit and find the next job whose start time is at least the current job's end time. 2️⃣ Skip the current job Move to the next job. So we take: dp[i] = max(take, skip) ⚡ Where Binary Search Helps After sorting the jobs, we can use Binary Search to efficiently find the next compatible job instead of scanning through all remaining jobs. This reduces the overall time complexity significantly. ⏱️ Complexity Sorting: O(n log n) DP + Binary Search: O(n log n) Overall Time: O(n log n) Space: O(n) 📚 Resources 📖 ChaiCode: https://dsa.chaicode.com/signup?ref=N... One problem every day. One step closer to mastering DSA! 🚀 Day 92 ✅ #DSA #JobScheduling #DynamicProgramming #BinarySearch #LeetCode #Coding #Programming #DSAJourney #100DaysOfCode #ChaiCode