Complement of Base 10 Integer LeetCode Challenge

🚀 Day 28 | 100 Days of Coding Challenge #DrGViswanathanChallenge 📘 Problem Solved: Single Number III (LeetCode) 🔧 Approach Used (Brute Force): • Iterated through each element • Counted its frequency using nested loops • Stored elements that appeared only once ⏳ Complexity: Time: O(n²) Space: O(1) (excluding output) 🧠 Key Learning: Brute force helps build understanding, but constraints push us toward better solutions. 💡 Optimal Insight: This problem can be solved in O(n) using Bit Manipulation (XOR) by separating numbers based on differing bits. 📂 Topics Covered: Arrays, Brute Force, Bit Manipulation #100DaysOfCode #DSA #CPP #LeetCode #CodingJourney #CompetitiveProgramming

🚀 Day 69 of #100DaysOfCode 🔥 Problem: Majority Element II (LeetCode 229) Today’s challenge was all about finding elements that appear more than ⌊n/3⌋ times in an array. Sounds simple, but the twist is doing it efficiently! 💡 Key Insight: Instead of counting frequencies using extra space, we can use an optimized approach based on the Boyer-Moore Voting Algorithm. ✨ Approach Highlights: • There can be at most 2 majority elements (> n/3) • Maintain 2 candidates and their counts • First pass → find potential candidates • Second pass → verify their frequency ⚡ Why this works? Because any element appearing more than n/3 times will survive the elimination process. 📈 Complexity: • Time: O(n) • Space: O(1) 🎯 Takeaway: Smart algorithms > brute force. Understanding patterns like voting algorithms can save both time and space! #DSA #LeetCode #CodingJourney #ProblemSolving #100DaysOfCode #Algorithms #Programming

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gxcUfyyz The first logic that came to my mind after seeing the question was — this is a cycle detection problem. Since the path isn't fixed, it's an undirected cycle detection problem. Just applied that and voila — solved! The constraints and multiple possible paths gave a clear hint towards DFS. The grid is just an undirected graph — each cell is a node, and two adjacent cells are connected only if they share the same character. So the problem reduces to: does any connected component of same-character cells contain a cycle? Run DFS from every unvisited cell, tracking the parent to avoid treating the edge we came from as a back edge. If we hit an already-visited cell that isn't our direct parent — cycle found. ⏱️ Time: O(m × n) | Space: O(m × n) 👉 My Solution: https://lnkd.in/gATmwcnU If you found this helpful, feel free to ⭐ the repo or connect! 🙂 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 Day 40 | 100 Days of Coding Challenge #DrGViswanathanChallenge 📘 Problem Solved: Sum of Subarray Ranges (LeetCode) 🔧 Approach Used (Brute Force): • Considered every possible subarray • Maintained current minimum and maximum while expanding subarray • Calculated range = max - min for each subarray • Added all ranges to get final answer 📌 Key Idea: For each subarray, track the largest and smallest element, and compute their difference. ⏳ Complexity: Time: O(n²) Space: O(1) 🧠 Key Learning: Even though brute force works, it helps in understanding the pattern before optimizing. 💡 Optimization Insight: This problem can be optimized using monotonic stacks by calculating contribution of each element as min and max separately. 📂 Topics Covered: Arrays, Brute Force 📊 Example: Input: [1,2,3] Output: 4 🔥 Step by step moving from brute force → optimal solutions 🚀 #100DaysOfCode #DSA #CPP #LeetCode #CodingJourney #CompetitiveProgramming

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/giAcdv2U 💡 My thought process: This solution uses 3D dynamic programming to find the maximum coins that can be collected from the top-left to the bottom-right of a grid, allowing up to 2 neutralizations for negative cells. The state dp[i][j][k] stores the maximum coins reachable at cell (i, j) using k neutralizations. For each cell, transitions are taken from the left and top cells. If the current cell is negative, two options are considered: either neutralize it (if k > 0) and carry forward the previous value without adding the negative, or do not neutralize and add the cell value. For non-negative cells, the value is simply added to the best of the previous states. The starting cell is initialized separately based on whether it is neutralized or not. The final answer is the maximum value among dp[m-1][n-1][0], dp[m-1][n-1][1], and dp[m-1][n-1][2]. 👉 My Solution: https://lnkd.in/gZWS3Kns If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

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

🚀 Day 33 of 100 Days LeetCode Challenge Problem: Maximum Amount of Money Robot Can Earn Day 33 brings a powerful Dynamic Programming + state management problem 🔥 💡 Key Insight: This is not just a normal grid DP. 👉 We also have a special power (neutralize up to 2 robbers) So the state must include: Position (i, j) Number of neutralizations used (0, 1, or 2) 🔍 Core Approach (DP): 1️⃣ Define DP State dp[i][j][k] → maximum coins at cell (i, j) using k neutralizations 2️⃣ Transitions From top (i-1, j) and left (i, j-1): If current cell ≥ 0: Add coins normally If current cell < 0: 👉 Two choices: Take loss OR use neutralization (if k < 2) 3️⃣ Take Maximum Choose best among all possibilities 4️⃣ Final Answer Max of dp[m-1][n-1][0..2] 🔥 What Makes It Interesting: Multiple states → adds complexity Decision making at each step (use power or not) 🔥 What I Learned Today: DP becomes powerful when handling extra constraints (states) Decision-based problems require exploring all valid paths State design is the most important part of DP 📈 Challenge Progress: Day 33/100 ✅ Leveling up in DP! LeetCode, Dynamic Programming, Matrix, Optimization, State Management, Algorithms, DSA Practice, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #DynamicProgramming #Matrix #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

Solved a classic Binary Tree problem: constructing a tree from preorder and inorder traversals. Interestingly, building the tree structure on paper felt almost obvious—pick the root, split left and right, and repeat. But implementing the same logic in code required careful handling of recursion and index boundaries. Used a hashmap to optimize lookups and achieve O(n) time complexity. Key takeaway: translating intuitive understanding into efficient code is where real problem-solving skills develop. #DSA #BinaryTree #Recursion #ProblemSolving #Coding

💡 Day 213 ✅ Longest Subarray with Sum = K (Quick Insight) Use Prefix Sum + HashMap to solve in O(n) ⚡ Key Points Store first occurrence of prefix sum Works with negative numbers Sliding window ❌ (not reliable here) #DSA #Coding #LeetCode #LongestSubarraywithSumequaltoK

Day 57 of solving LeetCode problems. Today’s problem: Remove Invalid Parentheses Not just about coding — this one forces you to think in terms of state exploration, pruning, and optimality. Brute force fails. Smart BFS/DFS thinking wins. Key takeaway: If your approach generates everything, you're already losing. Control the search space or it controls you. 🔺Still showing up. Still sharpening. #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #SoftwareEngineering #DSA #TechGrowth #Consistency #KeepGrinding #100DaysOfCode #DeveloperLife