Day 37 LeetCode Challenge: Walking Robot Simulation with Hashing

🚀 Day 57/100 — Navigating Infinite Planes with Obstacles! Today’s Problem: 874. Walking Robot Simulation 🔹 The Goal: Today was about simulating a robot navigating an infinite XY-plane. Given a series of commands to turn or move forward, the robot must avoid specific obstacle coordinates. The objective: find the maximum squared Euclidean distance the robot reaches during its entire journey. 🔹 The Insight: This problem is a masterclass in Search Space Optimization. While a naive approach might check every step against an array of obstacles—leading to O(K \cdot O) complexity—the real way to win is using a HashSet. By hashing obstacle coordinates into strings like "x,y", we turn an expensive search into a lightning-fast O(1) lookup. 🔹 The Logic: Modular Vector Rotation: I used a 4-direction array and the modulo operator to handle 90-degree turns seamlessly without complex if-else chains. Incremental Verification: Instead of "jumping" to the final position, the robot moves one unit at a time. This ensures we detect an obstacle the very moment the robot hits it. Global Peak Tracking: Monitored the squared distance (x^2 + y^2) at every single valid step to ensure the absolute maximum displacement was recorded. ✨ Achievement: Day 57! Moving past the halfway mark by tackling high-performance simulations. It’s a great reminder that even simple movements require rigorous logic and optimized data structures to scale effectively. 🔍 Steps followed: ✔ Set-Based Lookups: Converted the obstacle grid into a HashSet for constant-time collision detection. ✔ Directional Mapping: Managed 2D movement using index-based vector arrays. ✔ Precision Simulation: Implemented step-by-step movement to respect boundary constraints. 🔧 The Stats: Time Complexity: O(N + K) (where N is commands and K is total steps) Space Complexity: O(O) (to store O obstacles) 57 days down, 43 to go. The logic is getting sharper every single day. Let's keep the momentum! 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #Simulation #GraphTheory #Algorithms #Optimization #Day57

🚀 Day 38 of 100 Days LeetCode Challenge Problem: Walking Robot Simulation II Day 38 upgrades the previous problem into a design + simulation + cycle optimization challenge 🔥 💡 Key Insight: The robot moves along the boundary of the grid in a cycle. 👉 Instead of simulating every step: Compute the perimeter cycle length Use modulo to optimize large movements 📌 Cycle length: 2 * (width + height) - 4 🔍 Core Approach: 1️⃣ Understand Movement Pattern Robot moves along edges: East → North → West → South (counterclockwise turns) 2️⃣ Optimize Steps Reduce steps: num = num % cycle_length 👉 Avoid unnecessary full loops 3️⃣ Simulate Remaining Steps Move step-by-step only for reduced num Handle boundary → turn 90° counterclockwise 4️⃣ Track State Maintain: Current position (x, y) Current direction 🔥 What Makes It Interesting: Large inputs → require cycle detection Not just simulation → needs optimization 🔥 What I Learned Today: Repeating patterns → think cycles + modulo Design problems require state management Optimization is key for large constraints 📈 Challenge Progress: Day 38/100 ✅ Still going strong! LeetCode, Simulation, Design Problem, Cyclic Movement, Modular Arithmetic, Arrays, DSA Practice, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #DesignProblem #ModularArithmetic #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

#Day30 of DSA Journey | LeetCode 874 - Walking Robot Simulation Today’s problem was all about simulation + direction handling + hashing — a very practical and fun one! Problem Summary: A robot starts at (0,0) facing North and follows a sequence of commands: -2 → Turn Left -1 → Turn Right 1 to 9 → Move forward step-by-step There are obstacles on the grid, and the robot must stop if it encounters one. Goal: Find the maximum squared distance from the origin reached at any point. Key Learnings: Use direction vectors to simplify movement North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) Store obstacles in a HashSet for O(1) lookup Convert (x, y) → string "x,y" or use a pair hash Move step-by-step (not directly k steps!) to handle obstacles correctly Track max distance using: maxDist = max(maxDist, x*x + y*y) Approach: Initialize direction = North Process each command Rotate left/right using modular arithmetic Move step-by-step and stop if obstacle found Update max distance at each step Time Complexity: O(N * K) → N commands, each moving up to K steps Space Complexity: O(M) → for storing obstacles Takeaway: This problem strengthens your understanding of: Simulation problems Direction handling Efficient lookup using hashing #DSA #LeetCode #CodingJourney #JavaScript #ProblemSolving #100DaysOfCode

🚀 Day 36 of 100 Days LeetCode Challenge Problem: Robot Return to Origin Day 36 is a simple but important simulation + counting problem 🔥 💡 Key Insight: To return to origin (0,0): Total moves Right = Left Total moves Up = Down 👉 If both conditions satisfy → ✅ robot returns to origin 🔍 Core Approach: 1️⃣ Initialize Counters x = 0, y = 0 2️⃣ Process Each Move 'R' → x++ 'L' → x-- 'U' → y++ 'D' → y-- 3️⃣ Final Check If (x == 0 && y == 0) → true Else → false 💡 Alternative Approach: Count occurrences: count('R') == count('L') count('U') == count('D') 🔥 What I Learned Today: Simple problems test basic logic clarity Simulation problems are about correct tracking Don’t overcomplicate—keep it clean 📈 Challenge Progress: Day 36/100 ✅ Consistency still going strong! LeetCode, Simulation, Strings, Counting, Coordinates, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #Strings #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

LeetCode Daily | Day 71 🔥 LeetCode POTD – 2069. Walking Robot Simulation II (Medium) ✨ 📌 Problem Insight Given a robot on a bounded grid: ✔ Starts at (0, 0), facing East ✔ Moves along grid edges ✔ Turns counter-clockwise when hitting boundary ✔ Need to track position + direction after steps 🔍 Initial Thinking – Simulation ❌ 💡 Idea: ✔ Move step-by-step ✔ Handle boundary checks ✔ Update direction dynamically ⏱ Problem: ❌ Can be inefficient for large steps ❌ Too many edge cases 💡 Optimized Approach – Boundary Precomputation 🔥 👉 Key Idea: ✔ Robot always moves on rectangle boundary ✔ Precompute full path once ✔ Store: • All boundary positions • Direction at each step ✔ Then movement becomes: • idx = (idx + steps) % cycle ⚡ Why this is powerful ✔ Converts simulation → array indexing ✔ No step-by-step movement needed ✔ Handles edge cases automatically ⏱ Complexity: ✔ Precompute: O(perimeter) (~400 max) ✔ step(): O(1) ✅ ✔ getPos / getDir: O(1) 🧠 Key Learning ✔ Identify cycle / pattern in problems ✔ Precomputation can eliminate simulation ✔ Mapping states → indices simplifies logic ✔ Small trick (dir[0] = South) handles full-cycle edge case 🚀 Takeaway A great example of turning a simulation problem into a math/indexing problem — very useful for interviews ⚡ #LeetCode #DSA #Algorithms #CPlusPlus #ProblemSolving #CodingJourney #DataStructures

🚀 LeetCode Day Problem Solving 🚀 Day-39 📌 Problem: A robot starts at position (0, 0) on a 2D plane. You are given a string moves representing its movement sequence. Each character in the string denotes a move: ➡ 'R' → Right ⬅ 'L' → Left ⬆ 'U' → Up ⬇ 'D' → Down 👉 Your task is to determine whether the robot returns to the origin (0, 0) after completing all moves. Return true if it returns to origin, otherwise false. 🧠 Example 1: Input: moves = "UD" ✅ Output: true 📖 Explanation: Move Up → (0, 1) Move Down → (0, 0) ✔ Robot returns to origin 🧠 Example 2: Input: moves = "LL" ❌ Output: false 📖 Explanation: Move Left → (-1, 0) Move Left → (-2, 0) ❌ Robot does not return to origin 💡 Key Insight: ✔ Count movements: Number of 'U' should equal 'D' Number of 'L' should equal 'R' ✔ OR simulate coordinates: Track (x, y) position 📊 Complexity Analysis: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) 🧠 What I Learned: ✔ Simple simulation problems can be solved efficiently ✔ Importance of balancing movements ✔ Good practice for coordinate tracking ✅ Day 39 Completed 🚀 Keep going strong with DSA & consistency 💪 #Leetcode #DSA #ProblemSolving #BitManipulation #CodingJourney #InterviewPreparation #Consistency #MilanSahoo 🚀

LeetCode Daily | Day 70 🔥 LeetCode POTD – 874. Walking Robot Simulation (Medium) ✨ 📌 Problem Insight Given a robot on an infinite grid: ✔ Starts at (0, 0), facing North ✔ Commands include: • -2 → turn left • -1 → turn right • k → move forward k steps ✔ Obstacles block movement ✔ Find max distance² from origin reached 🔍 Initial Thinking – Simulation + Directions ✔ 💡 Idea: ✔ Maintain current position (x, y) ✔ Track direction using index (N, E, S, W) ✔ Use dx, dy arrays for movement ✔ Store obstacles in a set for fast lookup ✔ Move step-by-step (important!) • Before each step → check obstacle • If blocked → stop current command ⏱ Complexity: O(n + steps), Space: O(n) ✔ Efficient due to hashing ✔ Each move handled once 💡 Key Optimization – Hashing Obstacles 🔥 👉 Use set / unordered_set ✔ O(1) obstacle check ✔ Encode (x, y) → faster lookup possible 🧠 Key Learning ✔ Simulation problems need careful handling of edge cases ✔ Direction control using modulo is very useful ✔ Step-by-step movement avoids logical bugs ✔ Hashing turns brute-force into efficient solution A great mix of simulation + hashing — classic interview pattern ⚡ #LeetCode #DSA #Algorithms #CPlusPlus #ProblemSolving #CodingJourney #DataStructures

🔥 Day 549 of #750DaysOfCode 🔥 🚀 Solved: Robot Collisions (Hard) Today’s problem was a really interesting mix of sorting + stack + simulation — exactly the kind of question that tests real problem-solving skills 💡 🧠 Problem Insight We are given robots with: Positions 📍 Health ❤️ Directions (L / R) When two robots collide: The one with lower health ❌ gets destroyed The stronger one loses 1 health and continues If equal → both destroyed ⚡ Key Idea 👉 Collisions only happen when: A robot moving Right (R) meets one moving Left (L) So the approach becomes: Sort robots by position Use a stack to track right-moving robots Simulate collisions when a left-moving robot appears 🛠️ Approach ✔ Sort indices based on positions ✔ Use stack for R robots ✔ Resolve collisions with L robots ✔ Track alive robots and update health dynamically ⏱️ Complexity Time: O(n log n) (sorting) Space: O(n) 🎯 Key Learnings ✅ Stack is powerful for collision-type problems ✅ Sorting helps simulate real-world order ✅ Only R vs L interactions matter ✅ Clean handling of edge cases is crucial This problem really improved my understanding of simulation problems in DSA 💪 On to Day 550 🚀 #LeetCode #DSA #Java #CodingJourney #ProblemSolving #SoftwareEngineering #Stack #Algorithms #750DaysOfCode

🚀 #LeetCode Day Problem Solving 🚀 Day-20 📌 Problem: A robot starts at position (0, 0) on a 2D plane. You are given a string moves representing its movement sequence. Each character in the string denotes a move: ➡ 'R' → Right ⬅ 'L' → Left ⬆ 'U' → Up ⬇ 'D' → Down 👉 Your task is to determine whether the robot returns to the origin (0, 0) after completing all moves. Return true if it returns to origin, otherwise false. 🧠 Example 1: Input: moves = "UD" ✅ Output: true 📖 Explanation: Move Up → (0, 1) Move Down → (0, 0) ✔ Robot returns to origin 🧠 Example 2: Input: moves = "LL" ❌ Output: false 📖 Explanation: Move Left → (-1, 0) Move Left → (-2, 0) ❌ Robot does not return to origin 💡 Key Insight: ✔ Count movements: Number of 'U' should equal 'D' Number of 'L' should equal 'R' ✔ OR simulate coordinates: Track (x, y) position 📊 Complexity Analysis: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) 🧠 What I Learned: ✔ Simple simulation problems can be solved efficiently ✔ Importance of balancing movements ✔ Good practice for coordinate tracking ✅ Day 20 Completed 🚀 Keep going strong with DSA & consistency 💪 #Leetcode #DSA #ProblemSolving #BitManipulation #CodingJourney #InterviewPreparation #Consistency

🚀 My Coding Journey — Only 90 Days to Go! Today’s problem was a really interesting one involving robots, walls, and constrained shooting ranges — and it turned out to be a great exercise in greedy thinking + binary search. 💡 Key Learning: At first glance, it feels like a simulation problem. But the real trick is: Each robot can shoot either left or right (not both) Its range is limited not just by distance, but also by neighboring robots blocking the shot This naturally forms independent intervals 🔍 Approach I Used: Sort robots by position For each robot: Compute valid shooting range (considering blocking robots) Count walls on left and right using binary search Choose the direction that destroys more walls ⚡ Big Insight: Because robots block each other, their decisions become independent, which allows a greedy solution instead of complex DP. ⏱️ Time Complexity: O((n + m) log m) 🎯 Takeaway: Sometimes problems that look like simulation can be reduced to: 👉 interval problems 👉 greedy decisions 👉 efficient counting (binary search) 📉 Challenges like this remind me how important it is to: Break constraints carefully Translate real-world rules into clean intervals 🔥 90 days remaining in this journey. Consistency > Motivation. Let’s keep going. #DSA #CodingJourney #LeetCode #Greedy #BinarySearch #ProblemSolving #100DaysOfCode