LeetCode Challenge Day 22: Matrix Rotation and Simulation

🚀 Day 21 of 100 Days LeetCode Challenge Problem: Flip Square Submatrix Vertically Day 21 brings a clean matrix manipulation problem—simple logic, but important for fundamentals 🔥 💡 Key Insight: We are given: Top-left corner (x, y) Size k 👉 We need to flip the k x k submatrix vertically → Meaning: reverse the order of rows inside that square 🔍 Core Approach: 1️⃣ Identify Submatrix Boundaries Rows: x → x + k - 1 Columns: y → y + k - 1 2️⃣ Swap Rows Vertically Swap: Row x ↔ Row x + k - 1 Row x + 1 ↔ Row x + k - 2 Continue until middle 👉 Only swap elements within column range [y, y + k - 1] 💡 Visualization: Top row ⬇️ becomes bottom Bottom row ⬆️ becomes top 🔥 What I Learned Today: Matrix problems often come down to index manipulation Clear boundary definition avoids mistakes Simple operations can still test precision 📈 Challenge Progress: Day 21/100 ✅ 3 Weeks Consistency Strong! 💪 LeetCode, Matrix Manipulation, Arrays, Simulation, Indexing, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Matrix #Arrays #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

🚀 Day 214 of #300DaysOfCoding Today I solved a Hard-level problem on LeetCode: 👉 Minimum Total Distance Traveled This problem really tested my understanding of Dynamic Programming + Greedy Thinking. 💡 Key Learnings: Sorting plays a crucial role in optimizing 1D distance problems Converting real-world constraints into DP states makes complex problems manageable Handling capacity constraints (factories limit) using iterative assignment 🧠 Approach: Sorted robots and factories based on position Used DP + Memoization to assign robots to factories efficiently Tried multiple assignments per factory within capacity limits ⚡ Why this problem is interesting? It combines: Greedy intuition (nearest assignment) Dynamic Programming (optimal substructure) Real-world modeling (capacity constraints) 📊 Complexity: Time: O(n × m × limit) Space: O(n × m) 🔥 Problems like this improve problem-solving depth and prepare you for top product-based companies. #LeetCode #DSA #DynamicProgramming #CodingJourney #ProblemSolving #SoftwareEngineering #MCA #GATEPreparation

🚀 Day 27 of 100 Days LeetCode Challenge Problem: Matrix Similarity After Cyclic Shifts Day 27 brings a neat simulation + modular arithmetic problem 🔥 💡 Key Insight: Shifting happens k times, but instead of simulating k steps: 👉 We can reduce it using modulo Effective shifts = k % n (where n = number of columns) 🔍 Core Approach: 1️⃣ Understand Row Behavior Even rows → shift left Odd rows → shift right 2️⃣ Apply Optimized Shift For each row: Calculate effective shift using modulo Perform one optimized shift instead of k times 3️⃣ Compare with Original Matrix If all rows match → ✅ true Else → ❌ false 💡 Why Modulo Works: After n shifts, row returns to original state → avoids unnecessary computation 🚀 🔥 What I Learned Today: Repeated operations → think modulo optimization Simulation problems can often be simplified Understanding patterns saves time 📈 Challenge Progress: Day 27/100 ✅ Closing in on 30! LeetCode, Matrix, Simulation, Cyclic Shift, Modular Arithmetic, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Matrix #Simulation #ModularArithmetic #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

🚀 Day 17 of 100 Days LeetCode Challenge Problem: Largest Submatrix With Rearrangements Today’s problem is a powerful mix of matrix + greedy + sorting optimization 🔥 💡 Key Insight: We are allowed to rearrange columns, which changes everything! 👉 Instead of fixed positions: Focus on heights of consecutive 1’s in each column 🔍 Core Approach: 1️⃣ Build Heights Matrix For each cell: If 1 → increase height from previous row If 0 → reset to 0 👉 This converts the problem into a histogram per row 2️⃣ Sort Each Row (Greedy Move) Sort heights in descending order Why? → To maximize width for larger heights 3️⃣ Calculate Max Area For each position j: Area = height[j] × (j + 1) Track maximum across all rows 🔥 What I Learned Today: Rearrangement problems → think flexibility optimization Converting matrix → histogram simplifies logic Sorting can unlock hidden maximums 📈 Challenge Progress: Day 17/100 ✅ Strong consistency streak! LeetCode, Matrix, Greedy Algorithm, Sorting, Histogram, Optimization, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Matrix #GreedyAlgorithm #Sorting #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

𝐃𝐚𝐲 𝟕𝟖/𝟑𝟔𝟓 🚀 📌 𝐋𝐞𝐞𝐭𝐂𝐨𝐝𝐞 𝐏𝐎𝐓𝐃: 𝐖𝐚𝐥𝐤𝐢𝐧𝐠 𝐑𝐨𝐛𝐨𝐭 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 Continuing my 𝟑𝟔𝟓 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐂𝐨𝐝𝐞 journey with a focus on 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠, 𝐃𝐒𝐀, 𝐚𝐧𝐝 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲. 💪 🔎 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Track robot movement using (x, y) coordinates. Maintain direction using an index (North, East, South, West). Store obstacles in a HashSet for fast lookup. Move step-by-step and stop if an obstacle is encountered. Track maximum Euclidean distance squared from origin. 🔍 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐮𝐬𝐞𝐝: Simulation + HashSet for obstacle detection. ⏱ 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: 𝐎(𝐧 × 𝐦) (worst case step simulation) 🧠 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: 𝐎(𝐤) (for storing obstacles) 📈 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Using HashSet for obstacle detection converts costly lookups into O(1), making simulation efficient. #LeetCode #LeetCodeDaily #365DaysOfCode #DSA #Java #Simulation #Hashing #ProblemSolving #LearningInPublic 👨💻 🔗 Problem link in comments 👇

🔥 Day 554 of #750DaysofCode 🔥 🚀 Problem Solved: Walking Robot Simulation Today’s problem tested my simulation + hashing skills in a really interesting way! 🤖 What I implemented: I simulated the robot’s movement step-by-step on an infinite grid while handling obstacles efficiently. 💡 Instead of using complex structures, I used: 👉 HashSet to store blocked positions 👉 Direction array to manage movement (N, E, S, W) 🧠 Core Idea: Maintain current direction using an index Rotate: Right → (dir + 1) % 4 Left → (dir + 3) % 4 Move one step at a time (very important 🚨) Before every step: Check if next position is blocked If yes → stop moving for that command ⚡ Key Insight: 👉 You cannot jump directly k steps 👉 You MUST move step-by-step to correctly detect obstacles 💻 My Approach: ✔️ Stored obstacles as "x,y" in HashSet ✔️ Used direction vector: North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) ✔️ Tracked max distance using: 👉 x² + y² 📈 Complexity: Time: O(N + total steps) Space: O(M) for obstacles 🎯 What I learned: Simulation problems require careful step execution Hashing makes obstacle lookup super fast Small implementation details can make or break the solution 💬 Honestly, this problem looks easy at first, but handling directions + obstacles correctly makes it a great practice problem! #Day554 #750DaysOfCode #LeetCode #Java #DSA #CodingJourney #ProblemSolving #Developers #Tech

🚀 Solving Queue Simulation Smartly (LeetCode – Count Students Unable to Eat Lunch) In this problem, I implemented an optimized approach instead of brute-force simulation. 🔹 First, I counted students based on their sandwich preference (0 and 1). 🔹 Then, I simulated the queue: 1.If the front student matches the sandwich → both are removed 2.Otherwise, the student moves to the back 🔹 Key Insight: If no student prefers the current sandwich type, we can stop early — no need to continue unnecessary rotations. ✅ This reduces redundant operations and makes the solution more efficient. 💡 Takeaway: Sometimes counting + simulation together can simplify problems and avoid infinite loops or TLE. #LeetCode #ProblemSolving #DataStructures #Queue #CompetitiveProgramming #CodingJourney

🚀 Day 16/100 – DSA Journey Today’s problem was a great exercise in pointer manipulation and recursion thinking 🔗 🔹 Problem Solved: 1. Swap Nodes in Pairs 💡 Key Learnings: 👉 Approach 1: Iterative (Pointer Manipulation) Use a dummy node to simplify edge cases Swap nodes in pairs by adjusting pointers Move pointers step by step 👉 Key Insight: Careful pointer updates are crucial — one wrong link breaks the list 👉 Approach 2: Recursive Swap first two nodes Recursively solve for the rest of the list 👉 Core Idea: Break problem into smaller subproblems ✅ O(n) Time ✅ O(1) Space (Iterative) ⚠️ Recursive uses call stack → O(n) space 🔥 What I learned today: Same problem, two different mindsets: Iterative → control everything step-by-step Recursive → trust the function to handle smaller parts Both are powerful — knowing when to use which is key 👀 Day 16 done ✅ Consistency continues! 💬 Quick question: Which approach do you prefer for Linked Lists — Iterative or Recursive? #100DaysOfDSA #buildinpublic #codinginpublic #leetcodejourney #softwareengineerlife #dailylearning #codingpractice #devcommunity #programminglife #techcareers #jobready #growthmindset

Day 79 on LeetCode Smallest Index With Digit Sum Equal to Index 🔢✅ Keeping the streak going with a simple yet interesting digit manipulation + simulation problem 💯 🔹 Approach Used in My Solution The goal was to find the smallest index i such that the sum of digits of nums[i] equals i. Key idea: • Traverse the array from left to right • For each element, calculate the digit sum • Compare it with the current index • Return the first index where the condition satisfies • If none found → return -1 A clean and direct implementation without overcomplication. ⚡ Complexity: • Time Complexity: O(n * d) (d = number of digits) • Space Complexity: O(1) 💡 Key Takeaways: • Practiced digit extraction using modulo and division • Reinforced writing simple simulation-based solutions • Reminder: not every problem needs optimization — clarity matters 🔥 Consistency maintained, step by step progress continues. #LeetCode #DSA #Algorithms #DataStructures #Arrays #Math #Simulation #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #Consistency #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

🚀 Day 96 of DSA Problem Solving Today’s problem: Walking Robot Simulation 🤖 At first glance, this looked like a simple simulation problem… but it tested something deeper — attention to detail and step-by-step thinking. 💡 Problem Idea: Simulate robot movement on a 2D plane with directions and obstacles, while tracking the maximum distance from origin. 🧠 Key Learning: Always simulate step-by-step, not in jumps Use HashSet for O(1) obstacle lookup Direction handling using arrays makes code clean and scalable ⚙️ Concepts Practiced: Simulation Hashing Direction vectors Edge case handling ⏱ Time Complexity: O(N + M) 🔥 The Real Journey Behind This Solution: Here’s one honest truth from today… I did watch the video solution to understand the approach. But even after that, I realized something important — 👉 I am still not fully confident in explaining this problem on my own. And that’s okay. Because learning is not about copying solutions… It’s about struggling, revisiting, and trying again. So instead of stopping there, I pushed myself to: Rewrite the solution Understand each step Try to explain it in my own words I may not be perfect yet… But I’m trying my best to truly learn the approach, not just solve the problem. 💬 Growth doesn’t come from knowing everything… It comes from being honest about what you don’t know — and still showing up. And I realize this is a Hard question but has Medium Tag, so this is hard or just I feel this hard. #Day96 #DSAJourney #KeepLearning #ProblemSolving #Java #CodingLife 🚀