Longest Prefix Suffix Problem with KMP Algorithm

Most people struggle in coding interviews because they solve random DSA problems. Top engineers do it differently. They focus on patterns. Because once you understand the logic behind a pattern, solving unseen problems becomes much easier. Here are some of the most important DSA patterns you should master: 🔹 Sliding Window 🔹 Two Pointers 🔹 Fast & Slow Pointers 🔹 Binary Search 🔹 DFS / BFS 🔹 Backtracking 🔹 Dynamic Programming 🔹 Greedy 🔹 Union Find (DSU) 🔹 Topological Sort 🔹 Trie And more importantly — here are the must-solve problems for each. Instead of trying to solve 500 random questions: ✅ Pick one pattern ✅ Understand when to apply it ✅ Solve 3–5 quality problems ✅ Learn the intuition behind it That’s how you build real problem-solving skills. Not by memorizing solutions. 📌 Save this cheat sheet for your DSA prep. It might save you weeks of confusion. #DSA #CodingInterview #SoftwareEngineering #LeetCode #TechCareer #ProblemSolving

🚀 My Interview Preparation Journey Most people *memorize* star patterns. Today, I tried to **understand the logic behind them.** Here’s what I learned 👇 ✨ Golden Rules: • Rows decide the height (i controls lines) • Columns depend on pattern type → Fixed: j ≤ n → Increasing: j ≤ i → Decreasing: j ≤ n - i ✨ Pyramid Pattern Logic: • Spaces = n - i • Stars = 2*i - 1 ✨ Real Skill (Important 🔥) Whenever you see a pattern, ask: 1. How many rows? 2. What changes per row? 3. How many spaces? 4. Are stars increasing or decreasing? This completely changed how I approach problems. 📌 Sharing my handwritten notes for better understanding. Consistency > Perfection. Let’s grow step by step. 🚀 #learninginpublic #dsa #javascript #python #coding #programming #interviewpreparation #100daysofcode

🚀 Day 61 of #100DaysOfCode Today, I solved LeetCode 23 – Merge k Sorted Lists, a classic problem that frequently appears in coding interviews. 💡 Problem Overview: Given k sorted linked lists, the goal is to merge them into a single sorted linked list efficiently. 🧠 Approach: To solve this problem, I used: ✔️ Min Heap (Priority Queue) to always access the smallest element ✔️ Inserted the head of each list into the heap ✔️ Continuously extracted the minimum node and added the next node from that list This ensures that the merged list remains sorted at every step. ⚡ Key Takeaways: Heap is extremely useful when dealing with multiple sorted data sources Optimized merging avoids unnecessary comparisons Classic problems strengthen core interview concepts 📊 Complexity Analysis: Time Complexity: O(N log k) Space Complexity: O(k) Consistency + Strong Fundamentals = Long-Term Growth 🚀 #LeetCode #100DaysOfCode #DSA #LinkedList #Heap #ProblemSolving #CodingJourney #SoftwareDevelopment #InterviewPrep

Day15 #100 days of leetcode One of the most asked string problems in interviews — and a great way to sharpen your problem-solving skills. 🔍 Problem Given a string, find the longest substring that reads the same forward and backward. 💡 Approach Used: Expand Around Center Instead of checking every substring (which is expensive), we treat each character as a center and expand outward. #DataStructures #Algorithms #Java #CodingInterview #LeetCode #ProblemSolving #TechCareers

Most people overcomplicate this problem. At first glance, it feels like we need extra space or another array… But the trick is simple: Since the array is sorted, duplicates are always next to each other. Once you notice that, you can solve it in-place using a Two Pointer approach — no extra space needed. Small observation → big optimization. If you’re preparing for coding interviews, this pattern shows up more often than you think. Have you used this approach before? #DSA #TwoPointers #Java #CodingInterview #ProblemSolving #LeetCode #SoftwareEngineering

😳 𝐒𝐭𝐨𝐩 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐒𝐭𝐫𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐫𝐚𝐧𝐝𝐨𝐦𝐥𝐲… You don’t need to do everything. You need to solve the RIGHT problems. --- 💡 𝐑𝐞𝐚𝐥 𝐭𝐫𝐮𝐭: Most people keep solving random questions and feel “productive” 😓 But in interviews… they get stuck. --- 🔥 𝐈𝐟 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞𝐬𝐞, 𝐲𝐨𝐮’𝐫𝐞 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐑𝐄𝐀𝐃𝐘: • Longest Substring Without Repeating Characters • Valid Anagram • Minimum Window Substring • Longest Palindromic Substring • String Compression 👉 These cover almost ALL important patterns. --- 🧠 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧: • Sliding Window • Two Pointers • Hashing • Pattern Recognition --- 📌 𝐒𝐭𝐨𝐩 𝐝𝐨𝐢𝐧𝐠 𝐭𝐡𝐢𝐬: ❌ Solving random questions ❌ No pattern understanding ❌ Just watching solutions --- ✅ 𝐃𝐨 𝐭𝐡𝐢𝐬 𝐢𝐧𝐬𝐭𝐞𝐚𝐝: Pick a pattern → Solve 4–5 GOOD problems → Revise → Repeat 🔁 --- 💬 𝐋𝐞𝐞𝐭𝐂𝐨𝐝𝐞 𝐒𝐭𝐫𝐢𝐧𝐠 𝐥𝐢𝐧𝐤 𝐢𝐧 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 👇 --- If this helps: 🔖 Save it 🔁 Share with a friend ♻️ Repost Follow for more 🚀 --- #DSA #Strings #LeetCode #Coding #PlacementPrep

Most array interview questions come down to just 5 techniques. 🧩 I've seen hundreds of developers struggle on LeetCode - not because they lack intelligence, but because no one taught them the patterns. Here are the 5 array techniques that show up in 80% of interviews: 1️⃣ HashMap / Frequency Count → Turn O(n²) brute force into O(n) with a dictionary → Use for: duplicates, anagrams, top-K elements 2️⃣ Two Pointers → Left & right pointers moving inward on a sorted array → Use for: pair sums, palindromes, reversing in-place 3️⃣ Sliding Window → Maintain a subarray and slide it across → Use for: max/min subarray of size k, longest subarray with condition 4️⃣ Prefix Sum → Precompute cumulative sums for O(1) range queries → Use for: multiple range sum queries, subarray sum = target 5️⃣ Binary Search → Halve the search space each step - O(log n) → Use for: sorted/rotated arrays and search on answer problems The secret? You don't need to memorize 500 LeetCode problems. You need to recognize which pattern fits. At AlgoNur, we teach these patterns with step-by-step animated visualizations so you actually SEE what's happening, not just memorize code. 🔗 Free. No login required. Try it at https://lnkd.in/dF-zfi4r Swipe through the carousel above to see each technique with code examples. ↑ ♻️ Repost if this helps someone you know preparing for interviews. #DSA #LeetCode #CodingInterview #DataStructures #Algorithms #SoftwareEngineering #Programming #AlgoNur

 Crack Top Tech Interviews with Smart Strategy Struggling with coding interviews? Stop memorizing problems — start mastering patterns. 👉 Check full guide here: https://lnkd.in/gwhW33bU 📌 Why Coding Patterns Matter? • Solve problems faster with structured thinking • Recognize patterns like Sliding Window, Two Pointers, DFS/BFS, DP • Crack 70%+ interview questions with the right approach 🔥 Top Patterns You Must Know: • Sliding Window & Two Pointers • Fast & Slow Pointers • Binary Search & Top K Elements • DFS / BFS (Trees & Graphs) • Dynamic Programming (Knapsack) 👉 Instead of solving 100s of random questions, focus on these patterns — most problems are just variations of them 🔗 Comment #EPTJOBS to get the Apply Link in your Dm If You Want updates, guidance & direct support for applying? 🔗 Join our #Telegram now and stay ahead. https://lnkd.in/ggh2zgSf 🔗 Join our YouTube now and stay ahead: https://lnkd.in/gUrJWnM7 #CodingInterview #DSA #SoftwareEngineering #TechJobs #LeetCode #Programming #CareerGrowth #InterviewPrep

🚀 Mastering Two Sum, Three Sum & Four Sum — The Real Game is Time Complexity If you’ve ever prepared for coding interviews, you’ve definitely come across Two Sum, Three Sum, and Four Sum problems. At first glance, they look similar — but the real challenge lies in optimizing time complexity. Let’s break it down 👇 🔹 1. Two Sum — O(n) is the goal The brute force approach (nested loops) gives O(n²), but that’s not acceptable in interviews. Using a HashMap, we can reduce it to O(n) by storing complements while traversing the array. 👉 Key Idea: Trade space for time. --- 🔹 2. Three Sum — Sorting + Two Pointers = O(n²) Here, HashMap becomes messy due to duplicate handling. Instead: - Sort the array → O(n log n) - Fix one element - Use two pointers to find remaining pairs 👉 Result: Clean solution with O(n²) time and no duplicates. --- 🔹 3. Four Sum — Extending the pattern to O(n³) Now we: - Fix two elements - Apply two-pointer technique on the remaining array 👉 Time complexity becomes O(n³) — and that’s acceptable given the constraints. --- 💡 Big Learning: Pattern Recognition > Memorization - 2 Sum → Hashing - 3 Sum → Sorting + Two Pointers - 4 Sum → Nested loops + Two Pointers Each step builds on the previous one. Once you understand the pattern, you can even extend this to K-Sum problems. --- 🔥 Pro Tip for Interviews: Always start with brute force, then optimize step-by-step while explaining your thought process. Interviewers care more about how you think than just the final answer. --- #Java #DataStructures #Algorithms #CodingInterview #TimeComplexity #SoftwareEngineering #SDET #Learning #ProblemSolving

Starting something new 🚀 Continuing my weekly posts to stay consistent and revise what I study. 📌 Week 2: DSA (Arrays, Strings, Sliding Window, 2 Pointers) This week I focused on core DSA patterns. Sharing this more like my own revision notes: 💡 If planning to crack top MNCs, I feel these 15 questions/patterns are a must (this alone can cover around 80% of DSA coding rounds) 1. Two Sum 2. Maximum Subarray (Kadane’s) 3. Best Time to Buy & Sell Stock 4. Move Zeros to end/start 5. Longest Substring Without Repeating Characters 6. Longest Subarray with Sum K 7. Minimum Window Substring 8. Sliding Window Maximum 9. Container With Most Water 10. Trapping Rainwater 11. 3-Sum (sounds like something spicy 😆 … but it actually gives a strong flavor of the two-pointer technique and how to optimize array problems) 12. Valid Anagram 13. Group Anagrams 14. Longest Palindromic Substring 15. Merge Intervals 👉 What I’m noticing while practicing: 1. Same patterns repeat again and again 2. Sliding Window helps a lot in substring/subarray problems 3. Two pointers simplify many complex-looking problems 4. Hashing is a game changer for optimization 🧠 How I’m approaching it now: 1. Solving with intention, not just for the sake of solving 2. While solving, I try to think from interviewer’s perspective → what cross questions can be asked? 3. Always start with brute force approach, then optimize time & space complexity 4. Explaining my approach out loud (this really helps in interviews) 5. Revisiting mistakes and improving approach ⚠️ One thing I realized: 1. Don’t rush directly into graphs and trees 2. For 3–5 years' experience, interviewers expect optimized solutions (best time & space complexity) 3. Strong basics in arrays, strings, and patterns make everything easier later Still learning and improving consistency. Will keep sharing every week 🙂 #learning #dsa #java #interviewprep #coding #consistency