Reverse Integer Solution without Built-in Functions

#Day561 of #600DaysOfDSA Topics and Learnings: #Arrays, #Sorting Problems Re-revised: 181. #Leetcode #15 : 3um Approach: 1. Sort the array 2. Use 3 pointers to find the required pair Complexity Analysis: TC: O(N^2) SC: O(N^2) #600DaysLeetCodeChallenge #600DaysLeetCode #DataStructures #Algorithms #ProblemSolving #DSA #LeetCode #TakeUForward #TUF+ #SoftwareDevelopment #ContinuousLearning #ProfessionalGrowth #Coding

DAY->26 🚀 LeetCode 1002 — Find Common Characters | Frequency Optimization 🔥 Solved this problem using a frequency array approach and achieved 100% runtime (0 ms) ⚡ 🔍 The goal is to find characters that appear in all strings, including duplicates. 💡 Approach: Use a frequency array of size 26 Initialize with a large value (INT_MAX) For each word: Count character frequency Update global frequency using minimum values 🧠 Key Insight: We take the minimum frequency because a character must exist in every string. ⚡ Complexity: Time → O(n × k) Space → O(1) This problem helped me understand how frequency comparison can efficiently find common elements 🚀 #DSA #LeetCode #Cpp #Coding #ProblemSolving #Arrays #LearningJourney

#Day565 of #600DaysOfDSA Topics and Learnings: #Arrays, #LinearTraversal Problems Re-revised: 185. #Leetcode #2149 : Rearrange Array Elements by Sign Approach: Used Linear Traversal to solve this Complexity Analysis: TC: O(N) SC: O(N) #600DaysLeetCodeChallenge #600DaysLeetCode #DataStructures #Algorithms #ProblemSolving #DSA #LeetCode #TakeUForward #TUF+ #SoftwareDevelopment #ContinuousLearning #ProfessionalGrowth #Coding

🔥 From logic to trees — solved LeetCode #95 (Medium) 💻 Built all unique Binary Search Trees using recursion + memoization. 🔍 Key concepts: 1. Divide & Conquer 2. Recursive tree construction 3. Dynamic Programming ⚙️ Result: ✔️ Accepted ✔️ Optimized approach ✔️ Deeper understanding of problem structuring 💡 Takeaway: Strong solutions come from breaking problems into smaller, reusable pieces. #LeetCode #Algorithms #DataStructures #ProblemSolving #CodingJourney

#Day550 of #600DaysOfDSA Topics and Learnings: #BinarySearch Problems Re-revised: 170. #Leetcode #1283 : Find the Smallest Divisor Given a Threshold Approach: Used Binary Search to solve this Complexity Analysis: TC: O(log(max)*N) SC: O(1) #600DaysLeetCodeChallenge #600DaysLeetCode #DataStructures #Algorithms #ProblemSolving #DSA #LeetCode #TakeUForward #TUF+ #SoftwareDevelopment #ContinuousLearning #ProfessionalGrowth #Coding

#Day552 of #600DaysOfDSA Topics and Learnings: #BinarySearch Problems Re-revised: 172. #Leetcode #162 : Find Peak Element Approach: Used Binary Search to solve this Complexity Analysis: TC: O(logN) SC: O(1) #600DaysLeetCodeChallenge #600DaysLeetCode #DataStructures #Algorithms #ProblemSolving #DSA #LeetCode #TakeUForward #TUF+ #SoftwareDevelopment #ContinuousLearning #ProfessionalGrowth #Coding

#100DaysLeetCode Day 40 ✅ Solved LeetCode 16: 3Sum Closest Given an array and a target value, find the sum of three integers that is closest to the target. 🚀 Approach Used Sorting + Two Pointers Steps: Sort the array Fix one element Use two pointers (left and right) to find the closest sum Logic: If current sum is closer to target than previous result → update answer If sum < target → move left Else → move right If sum becomes exactly equal to target, return immediately. ⏱ Complexity Time Complexity: O(n²) Space Complexity: O(1) (ignoring sorting space) #100DaysLeetCode #Day40 #LeetCode16 #leetcode #cpp #dsa #twopointers #arrays #problemSolving #coding #interviewprep

💡 DSA Day 216 ✅ ❓ Shortest Distance to Target String in a Circular Array 🔍 The Challenge: In a circular array of size n, distance isn’t always straight. You have to consider both directions: Direct distance: |i - start| Wrap-around distance: n - |i - start| ✨ The Insight: Take the minimum of both: min(direct, wrap-around) This keeps the solution clean and efficient: ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(1) #Coding #DataStructures #CPP #LeetCode #ProblemSolving #ShortestDistancetoTargetStringinaCircularArray

Computation is easy—the real challenge is knowing what to compute. In competitive programming, problems like counting pairs whose sum is divisible by k look straightforward, a vast pattern of questions of CodeForces and LeetCode revolve around this—but under contest pressure, they expose gaps in thinking efficiency. Brute force works… until it doesn’t. The real shift happens when you stop iterating over pairs and start reasoning in modular space since it reduces space from large n to defined m. Want to know, how I got this idea, try solving this problem - https://lnkd.in/gUrbzTV6 What most people miss is this: • It’s not about “optimizing loops” • It’s about changing the representation of the problem This is where many plateau—solving problems, but not upgrading their thinking model. Interestingly, this maps directly to real systems: • Hashing, partitioning, load balancing → all rely on similar modular reasoning • Efficiency comes from pre-computation and smart grouping, not brute force If you’re stuck in CP, it’s rarely a coding issue—it’s a thinking abstraction gap. How often do you step back and rethink the structure of a problem instead of optimizing your current approach? Let us discuss this further in the comments. Follow Vishu Kalier for more such deep dives. #CompetitiveProgramming #DSA #ProblemSolving #Algorithms #Coding #SystemDesign #cfbr #learninginpublic #roadtograndmaster #eternal #zomato

#Day560 of #600DaysOfDSA Topics and Learnings: #Arrays, #Sorting Problems Re-revised: 180. #Leetcode #18 : 4Sum Approach: 1. Sort the array 2. Used 4 pointers with triple nested loop to find the required quadruplets Complexity Analysis: TC: O(N^3) SC: O(N^2) #600DaysLeetCodeChallenge #600DaysLeetCode #DataStructures #Algorithms #ProblemSolving #DSA #LeetCode #TakeUForward #TUF+ #SoftwareDevelopment #ContinuousLearning #ProfessionalGrowth #Coding