Master Mo's Algorithm for Efficient Range Queries

😱 Most beginners solve this problem in O(N²) or O(N³) But experienced programmers solve it in O(N). The trick? Sliding Window Technique. Let’s understand it with a simple example. Array : 1 2 3 4 5 Window size = 3 Instead of recalculating every subarray like this: [1 2 3] [2 3 4] [3 4 5] We simply slide the window. Old window : [1 2 3] Slide → [2 3 4] Instead of recomputing everything we just : • Remove 1 • Add 4 That’s it. Time Complexity improves from O(N²) → O(N). Key Idea New Window = Previous Window - element leaving + element entering I wrote a detailed explanation with code and visuals here : https://lnkd.in/gKRuKHy8 If you're learning DSA or Competitive Programming, this technique is extremely useful. Follow me for more simple explanations of algorithms  #DSA #CompetitiveProgramming #Algorithms #CodingInterview #SlidingWindow #LeetCode #Programming

Day 60/365 – Longest Increasing Subsequence (LIS) 📈 Given an array, find the length of the longest strictly increasing subsequence. 💡 Key Idea: Dynamic Programming 🧠 Approach • Use dp[i] = length of LIS ending at index i • Compare current number with all previous numbers • If nums[j] < nums[i], update subsequence length Formula: dp[i] = max(dp[i], dp[j] + 1) ⏱ Time Complexity: O(n²) 📦 Space Complexity: O(n) ✨ Takeaway: LIS is a classic Dynamic Programming problem and often appears in coding interviews. #Day60 #365DaysOfCode #LeetCode #DynamicProgramming #Algorithms #DSA #InterviewPrep #ProblemSolving #Consistency

🚀 Solved: Missing & Repeating Number Problem (DSA) Today I solved a classic and highly asked interview problem on arrays — finding the missing and repeating number in O(n) time. 💡 Key Learnings: There are multiple approaches: 🧠 Math Formula (Sum & Square Sum) ⚡ XOR Method (Best for avoiding overflow) 📊 Hashing (Simple but extra space) The trick is converting the problem into equations: Difference → (Repeating - Missing) Sum → (Repeating + Missing) ⚠️ Big Mistake I Faced: Integer overflow while calculating formulas like: n(n+1)/2 👉 Fixed by casting before multiplication → (long)n * (n+1) ✅ Finally got: 1111 / 1111 test cases passed 100% accuracy 📈 What I realized: It’s not just about solving problems — it’s about understanding why your solution fails and fixing edge cases. 🔥 Next Goal: Master XOR approach Solve more pattern-based DSA problems daily 💬 If you're learning DSA: Consistency + Debugging = Growth #DSA #Coding #Java #ProblemSolving #Learning #GeeksforGeeks #100DaysOfCode #Programming #InterviewPrep

🚀 Day 75 – DSA Challenge Today I solved the “Larry’s Array” problem, which focuses on permutations, rotations, and inversion counting. 📌 Problem Insight: We are allowed to rotate any 3 consecutive elements in the array. The goal is to determine whether the array can be sorted using this operation. 💡 Key Learning: Instead of simulating rotations, the trick is to count the number of inversions in the array. If the number of inversions is even → the array can be sorted (YES) If the number of inversions is odd → the array cannot be sorted (NO) 🔎 Concept Used: • Permutations • Inversion counting • Parity of permutations • Problem-solving optimization ⚡ What I Learned Today Understanding the mathematical properties of operations can help us avoid brute force simulations and solve problems more efficiently. 💻 Language Used: C / JavaScript 🧠 Focus: Data Structures & Algorithms Practice Consistency is the key. One problem every day makes a huge difference over time. #Day75 #DSA #ProblemSolving #CodingChallenge #Algorithms #Programming #DeveloperJourney #LearningInPublic

DSA Starts Making Sense When You Learn It Pattern-Wise Many developers approach DSA by jumping between random problems. The result? Progress often feels slow and inconsistent. Effective DSA preparation comes from recognizing that most problems are built around repeating patterns. Once you understand the pattern behind a problem, solving similar questions becomes significantly easier. A Practical Preparation Strategy- • Identify core DSA patterns • Focus on mastering one pattern at a time • Solve multiple problems that follow the same logic • Move to the next pattern only after gaining confidence Fundamental DSA Patterns Every Learner Should Know 1) Sliding Window 2) Two Pointers 3) Hashing & Frequency Map 4) Prefix Sum 5) Binary Search (on array & answer) 6) Recursion & Backtracking 7) Linked List Patterns 8) Stack & Monotonic Stack 9) Heap / Priority Queue 10) Greedy 11) Dynamic Programming 12) Graph Traversal (BFS / DFS) 13) Tree Traversal 14) Bit Manipulation When you prepare DSA pattern-wise: ✔ Problem recognition becomes faster. ✔ Solutions become more structured and confident. ✔ Learning shifts from memorization to understanding. Strong DSA skills are rarely about volume. They come from mastering foundational patterns. 📌 Save this as a structured DSA preparation roadmap. hashtag #DSA #CodingInterview #Python #PlacementPreparation #LeetCode #PatternBasedDSA #PythonDeveloper #SoftwareEngineering

DSA Starts Making Sense When You Learn It Pattern-Wise Many developers approach DSA by jumping between random problems. The result? Progress often feels slow and inconsistent. Effective DSA preparation comes from recognizing that most problems are built around repeating patterns. Once you understand the pattern behind a problem, solving similar questions becomes significantly easier. A Practical Preparation Strategy- • Identify core DSA patterns • Focus on mastering one pattern at a time • Solve multiple problems that follow the same logic • Move to the next pattern only after gaining confidence Fundamental DSA Patterns Every Learner Should Know 1) Sliding Window 2) Two Pointers 3) Hashing & Frequency Map 4) Prefix Sum 5) Binary Search (on array & answer) 6) Recursion & Backtracking 7) Linked List Patterns 8) Stack & Monotonic Stack 9) Heap / Priority Queue 10) Greedy 11) Dynamic Programming 12) Graph Traversal (BFS / DFS) 13) Tree Traversal 14) Bit Manipulation When you prepare DSA pattern-wise: ✔ Problem recognition becomes faster. ✔ Solutions become more structured and confident. ✔ Learning shifts from memorization to understanding. Strong DSA skills are rarely about volume. They come from mastering foundational patterns. 📌 Save this as a structured DSA preparation roadmap. #DSA #CodingInterview #Python #PlacementPreparation #LeetCode #PatternBasedDSA #PythonDeveloper #SoftwareEngineering

🚀 Strengthening my foundation in Data Structures & Algorithms Explored key concepts like Sorting & Searching Algorithms, Dynamic Programming, Graph Algorithms, and more. From Binary Search & Merge Sort to Dijkstra’s Algorithm & DFS/BFS, this journey is all about improving problem-solving skills and writing efficient code. 💡 Consistency + Practice = Growth All credit goes to the original creater of the material. Feel free to Repost and Follow Himansh S. for more Helpful resources. DM me for more resources. #DSA #CodingJourney #ProblemSolving #Programming #SoftwareDevelopment #Learning #TechSkills

DSA Cheat Sheet for Developers Master Data Structures & Algorithms Fast Boost your problem-solving skills with this complete DSA Cheat Sheet designed for developers! Covering essential data structures, algorithms, time complexity, sorting, searching, recursion, trees, graphs, and dynamic programming — this quick reference helps you revise faster and code smarter. Perfect for coding interviews, competitive programming, and strengthening your programming fundamentals. #DSA #DataStructures #Algorithms #DSACheatSheet #CodingInterview #ProblemSolving #Programming #Developers #TechLearning #SoftwareEngineering #CompetitiveProgramming #InterviewPreparation #Coding #LearnToCode

🚀 DSA Deep Dive – Day 30 Solved Longest Increasing Subsequence today. And I realized… Dynamic Programming is not about continuous subarrays. It’s about controlled choices over time. 🤯 The problem sounds simple: Given an array, find the length of the longest strictly increasing subsequence. Not subarray. Subsequence. That one word changes everything. 👀 You’re allowed to skip elements. But you must maintain order. Here’s what I learned 👇 • Define dp[i] = length of LIS ending at index i • Every element can start a subsequence (minimum length = 1) • For every i, check all j < i • If nums[j] < nums[i] → extend subsequence • Take the maximum possible extension This is classic DP state building. At every index, you ask: 👉 Can I extend a previous increasing chain? 👉 Or do I start fresh from here? Brute force recursion: Exponential 💀 Optimized DP (nested loops): O(n²) ⚡ Even better approach exists: O(n log n) with binary search 🚀 But the mindset is what matters. The powerful shift? Instead of asking “What is the longest sequence overall?” Ask “What is the longest sequence ending HERE?” That’s dynamic programming thinking. Local decisions → Global answer. Lesson: DP is not about memorizing patterns. It’s about defining the right state. Once the state is clear, the transition becomes obvious. 30 days in. Graphs ✅ 1-D DP almost done 🚀 Consistency compounds. Follow for more DSA breakdowns 🔥 #LeetCode #DSA #DynamicProgramming #InterviewPrep #CodingJourney #ProblemSolving #100DaysOfCode #SoftwareEngineering #TechGrowth

It's interesting how many things could be considered as improvements, but won't affect the time complexity of the solutions. In this case, we should avoid them if the score is based solely on overall time complexity (it's better to ask the interviewer). My personal pet peeve is bit arrays and bit masking. If we work with a limited set of data, it doesn't matter whether we represent it as a single bit or as a string in a dictionary. Often, such solutions come from people with competitive programming experience and only confuse developers who want to learn an algorithm. The post: https://lnkd.in/dyvM4DkA The problem: https://lnkd.in/d3U3PQSw #LeetCode #DSATips #DSA #ThinkDSA #ProblemSolving