Sweep Line Technique Part 2: Optimize Event Processing & Interval Queries

Mo’s Algorithm  Optimize Complex Range Queries Like a Pro I’ve just published a detailed guide on Mo’s Algorithm, one of the most powerful techniques for handling range queries efficiently in competitive programming. In this blog, I focused on building intuition step by step :-  -What Mo’s Algorithm really is  -When to use it (and when NOT to)  -Why we sort queries using √N blocks  -Why sorting by R inside blocks matters  -Optimized sorting trick (odd-even block optimization)  -Full C++ implementation for distinct elements problem  -Time Complexity breakdown → O((N + Q) √N) Key Insight :  Instead of solving each query from scratch, Mo’s Algorithm reuses previous computations by minimizing pointer movement — turning expensive problems into efficient ones. If you're preparing for :   -Coding Interviews  -Competitive Programming  -DSA Concepts This technique is a must-know Read the full blog here : https://lnkd.in/ggprikKn Would love your feedback and suggestions! #DSA #Algorithms #CompetitiveProgramming #MoAlgorithm #CPP #CodingJourney #TechBlog #LearningInPublic #Programming

🚀 DSA Deep Dive – Day 24 Solved my 5th 1-D Dynamic Programming problem today. 🚀 And I realized most developers misunderstand Palindrome problems. The question never said “use DP table.” 🤯 It simply asked to return the longest palindromic substring. Here’s what I learned 👇 • Palindromes expand from a center 🎯 • Every index can be a potential center • Handle both odd & even length cases • Expand while characters match • Track the longest window Brute force: O(n³) ❌ Better check: O(n²) Optimized (Expand Around Center): O(n²) with constant space ⚡ The interesting part? This problem is tagged under Dynamic Programming. But you don’t actually need a DP table to solve it efficiently. Lesson: DP isn’t about building tables. It’s about recognizing structure and eliminating unnecessary work. Smart thinking > Category memorization. Follow for more DSA insights 🚀 #LeetCode #DSA #DynamicProgramming #CodingJourney #SoftwareEngineering #InterviewPrep #ProblemSolving #100DaysOfCode #Developers #TechCareers

Is Dynamic Programming Scary?? 😱 Let's understand it.... But the core idea is simple: if the same smaller problem appears again and again, solve it once, store it, and reuse it. That is why DP is powerful. It is not about writing complex code first. It is about learning to spot repetition, define the right state, and build bigger answers from smaller ones. In this visual, I explained: what Dynamic Programming really means, why repeated subproblems matter, and how memoization and tabulation differ. Memoization = top-down Tabulation = bottom-up Once that difference becomes clear, DP starts feeling much more practical and much less scary. The goal is not to memorize solutions. The goal is to understand: what repeats, what to store, and how the final answer is built. Which DP problem helped you understand the concept for the first time? #DynamicProgramming #Algorithms #DataStructures #Programming #SoftwareEngineering #CodingInterview #ProblemSolving #DeveloperLearning #ComputerScience #TechEducation

Most people underestimate this. Solving 1 DSA problem today doesn’t feel like much. Solving 1 tomorrow doesn’t either. But 1 problem daily for a year? That’s 365 structured thinking exercises. Here’s what I’ve noticed from consistent practice: Problems start looking familiar Edge cases become easier to predict Debugging time reduces Intuition improves dramatically You stop panicking during contests The biggest shift isn’t in coding speed. It’s in how you approach a problem. Earlier: “I don’t know how to solve this.” Now: “This looks like sliding window + hashmap.” “Feels like binary search on answer.” “Maybe DP with state compression.” That pattern recognition only comes from repetition. Competitive Programming is not about talent. It’s about exposure + reflection + consistency. You don’t need 10 hours a day. You need 1 focused hour. Small daily effort → Big long-term advantage. #DSA #CompetitiveProgramming #Consistency #ProblemSolving #LeetCode #CodingJourney

Highlights of Dynamic Programming CSES Problemset Episodes 6–12 🚀 The special thing about this series is the focus on building intuition for when to use DP, how to identify states, and understanding the concept in the easiest possible way. If you’re learning Data Structures and Algorithms, Competitive Programming, I’d recommend watching just 2 minutes of the video. If you feel it helps you understand the concept better, your like and support would mean a lot. #DynamicProgramming #DSA #CompetitiveProgramming #ProblemSolving #Learning

𝑪𝒐𝒅𝒊𝒏𝒈 𝑷𝒓𝒐𝒃𝒍𝒆𝒎 – 𝑭𝒊𝒓𝒔𝒕 𝑵𝒐𝒏-𝑹𝒆𝒑𝒆𝒂𝒕𝒊𝒏𝒈 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓 Given a string S, determine the first character that appears only once in the string. 𝑬𝒙𝒂𝒎𝒑𝒍𝒆: 𝑰𝒏𝒑𝒖𝒕: aabbcdd 𝑶𝒖𝒕𝒑𝒖𝒕: c 𝑬𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒊𝒐𝒏 Character frequencies in the string: a → 2 b → 2 c → 1 d → 2 The first character with frequency 1 is 'c', so the output is 𝒄. Problems like this help improve string manipulation and frequency counting skills, which are commonly asked in coding interviews and online assessments. 𝑻𝒓𝒚 𝒔𝒐𝒍𝒗𝒊𝒏𝒈 𝒊𝒕 𝒚𝒐𝒖𝒓𝒔𝒆𝒍𝒇! 𝑪𝒐𝒎𝒎𝒆𝒏𝒕 𝒚𝒐𝒖𝒓 𝒂𝒏𝒔𝒘𝒆𝒓 𝒃𝒆𝒍𝒐𝒘 👇 #Coding #Programming #DSA #InterviewPreparation #SavecodeS

Most DP problems look different. But today I learned something powerful 👇 Subset Sum Partition Equal Subset Sum Partition With Minimum Difference Three problems. One core idea. If you understand Subset Sum, the other two are just smart variations of it. 💡 Dynamic Programming isn’t about memorizing patterns. It’s about recognizing them. Today’s takeaway: Define the state clearly → Everything becomes simpler. Small progress. Stronger foundations. #DSA #DynamicProgramming #CodingJourney #ProblemSolving

The DSA Grind | Streak 11/50 | Solved LC 34 Find First and Last Position of Element in Sorted Array At first glance the problem looked like a simple search problem. The most straightforward approach would be to scan the array and track the first and last occurrence of the target element. That approach works, but it results in O(n) time complexity. However, the problem specifically requires a solution with O(log n) time complexity, which immediately suggests using Binary Search since the array is already sorted. The idea was to perform two binary searches: • The first binary search finds the first occurrence of the target by continuing the search toward the left side even after finding the target. • The second binary search finds the last occurrence by continuing the search toward the right side after encountering the target. Each search runs in O(log n) time, giving an overall complexity of O(log n). This problem reinforced an important lesson: sometimes the challenge isn’t finding the element, but modifying a known algorithm to extract more information from the data. 🎯 Current Goal: Reach a 50-day solving streak 🏁 Focus: Consistency in problem solving and participating in regular contests. #DSA #DataStructures #Algorithms #CodingChallenge #LeetCode #Programming #Developer #ConsistencyIsKey

🚀 Dynamic Programming (DP) is hard… until it suddenly isn’t. A few weeks ago, I used to get stuck whenever a problem involved grids, paths, or “minimum/maximum ways.” Now I’m starting to see a pattern everywhere — and that’s the beauty of Dynamic Programming. DP isn’t just a coding topic. It’s a mindset: Break a big problem → solve smaller subproblems → store results → avoid repeating work. Once this clicks, problems that look impossible start feeling manageable. 💡 What helped me understand DP better: • Start with recursion (understand the brute force) • Add memoization (store answers) • Convert to tabulation (bottom-up thinking) • Optimize space (write cleaner solutions) 📌 Problems I practiced recently: Unique Paths (grid problems) Unique Paths with Obstacles Climbing Stairs Fibonacci pattern questions Each problem looked different, but the underlying pattern was the same. One thing I’ve learned: You don’t master DP by reading solutions. You master it by struggling, debugging, and recognizing patterns. Still learning, still improving — but definitely enjoying the process. If you’re learning DSA right now, keep going. DP feels tough at first, but once it clicks, it becomes one of the most satisfying topics. Let’s keep building 💻🔥 #DynamicProgramming #DSA #CodingJourney #LeetCode #Programming #TechStudents #SoftwareEngineering #ProblemSolving #LearnInPublic #PlacementPreparation

While revising number theory basics, I realized how important a few core algorithms are — so I compiled them into an easy-to-understand blog. The article focuses on three fundamental concepts that are extremely useful in Competitive Programming and DSA preparation : • Binary Exponentiation – calculating powers efficiently in O(log n) time instead of O(n). •  Primality Testing – understanding both brute force and the optimized Square Root Method. •  Euclidean Algorithm – a clean and powerful way to compute GCD/HCF using recursion and modulus. I’ve tried to keep the explanations simple with step-by-step logic, examples, and C++ snippets so that beginners can follow along easily while also serving as a quick revision guide for others. If you’re starting your journey in programming or preparing for coding contests, mastering these basics can make a huge difference. Blog Link : https://lnkd.in/g8S6x_HH #DSA #CompetitiveProgramming #NumberTheory #Algorithms #CPlusPlus #LearningInPublic #CodingJourney