Adding One to Array Digits with Carry Propagation

✅ Day 27 of 100 Days LeetCode Challenge Problem: 🔹 #695 – Max Area of Island 🔗 https://lnkd.in/gUCues5K Learning Journey: 🔹 Today’s problem focused on finding the largest connected group of land cells in a 2D grid. 🔹 I used Breadth-First Search (BFS) to explore each island and calculate its area. 🔹 Starting from an unvisited land cell, BFS traverses all connected land cells while counting the size of the island. 🔹 A visited set ensures each cell is processed only once, avoiding duplicate work. Concepts Used: 🔹 Breadth-First Search (BFS) 🔹 Graph Traversal 🔹 Matrix Traversal 🔹 Connected Components Key Insight: 🔹 Grid problems can be treated as graph traversal problems. 🔹 BFS allows efficient exploration of all connected cells in an island. 🔹 Tracking visited nodes is crucial for correctness and performance. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

🗓 Day 61 / 100 – #100DaysOfLeetCode 📌 Problem 1390: Four Divisors Today’s problem combined number theory fundamentals with careful iteration. The task was to scan through an array and compute the sum of divisors only for those numbers that have exactly four distinct divisors. 🧠 My Approach: For each number in the array: Iterated from 1 to √x to find divisor pairs efficiently. Whenever i divides x, added both i and x / i to a set to ensure uniqueness. Accumulated the sum of these divisors while tracking how many unique divisors were found. After processing a number, checked: If it had exactly 4 divisors, added its divisor sum to the final answer. Otherwise, ignored it. Using the square-root optimization keeps the solution efficient even for large values. 💡 Key Learning: This problem reinforced: ✔ efficient divisor enumeration using √n optimization ✔ using sets to avoid duplicate divisors ✔ applying number theory concepts in coding problems ✔ filtering results based on strict conditions A solid reminder that understanding divisor patterns can simplify many math-based problems in DSA 🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #Math #NumberTheory #Divisors #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

🗓 Day 63 / 100 – #100DaysOfLeetCode 📌 Problem 1161: Maximum Level Sum of a Binary Tree Today’s problem focused on tree traversal and level-wise aggregation. The task was to find the level in a binary tree that has the maximum sum of node values, with levels numbered starting from 1. 🧠 My Approach: Used Breadth-First Search (BFS) to traverse the tree level by level. For each level: Calculated the sum of all node values at that level. Compared it with the maximum sum seen so far. Kept track of: the current level number the level with the maximum sum Returned the smallest level number in case of a tie, as required. This level-order traversal makes the solution both intuitive and efficient. 💡 Key Learning: This problem reinforced: ✔ how BFS naturally fits level-based tree problems ✔ careful tracking of level indices during traversal ✔ handling ties correctly based on problem constraints Tree problems often become much simpler once the right traversal strategy is chosen. Another solid step forward in mastering binary tree patterns 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #BFS #TreeTraversal #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

Solving the Bitwise OR Challenge 🔢 The problem: Find the smallest 'x' such that x OR (x + 1) equals a given prime number 'p'. The Logic: 💡 The only prime number that cannot satisfy this is 2. For all other primes (which are odd), the binary representation ends in at least one '1'. 💡 The operation x OR (x + 1) essentially fills the rightmost '0' bit of 'x' with a '1'. 💡 To minimize 'x' while satisfying the condition, we look for the rightmost sequence of consecutive 1s in the prime 'p'. 💡 By flipping the highest bit of that specific trailing sequence from 1 to 0, we create our answer. Example Walkthrough: 👉 For p = 13 (1101): The trailing 1 is at position 0. Flipping it gives 1100 (12). 12 OR 13 = 13. 👉 For p = 31 (11111): The sequence of 1s is long. Flipping the bit at the "boundary" gives 01111 (15). 15 OR 16 = 31. Code Implementation: https://htmlify.me/r/u0q7 Key Takeaway: When dealing with x OR (x + 1), you are looking at how a carry bit propagates through trailing ones in binary addition! #BitManipulation #Programming #Algorithms #Python #CompetitiveProgramming #TechTips

🗓 Day 60 / 100 – #100DaysOfLeetCode 📌 Problem 1411: Number of Ways to Paint N × 3 Grid Today’s problem was a really good exercise in dynamic programming and pattern observation. The goal was to count the number of valid ways to paint an n × 3 grid using three colors, such that no two adjacent cells (horizontal or vertical) share the same color. 🧠 My Approach: Instead of tracking every possible coloring explicitly, I categorized each row into two patterns: 1️⃣ same – Rows where the 1st and 3rd cells have the same color (e.g., A B A) 2️⃣ diff – Rows where all three cells have different colors (e.g., A B C) For the first row: same = 6 diff = 6 Then for each subsequent row: A same pattern can be formed from: previous same rows in 3 ways previous diff rows in 2 ways A diff pattern can be formed from: previous same rows in 2 ways previous diff rows in 2 ways Using these transitions, I iterated row by row and applied modulo 10^9 + 7 to handle large values efficiently. 💡 Key Learning: This problem reinforced: ✔ breaking complex constraints into manageable states ✔ recognizing repeating patterns to optimize DP solutions ✔ reducing a large state space into just a few variables ✔ how mathematical transitions can drastically simplify implementation A perfect example of how thinking in patterns turns a hard-looking problem into a clean and elegant solution 🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #DynamicProgramming #DP #Combinatorics #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

🚀 LeetCode Daily Challenge – Day 3 Problem #1411: Number of Ways to Paint N × 3 Grid (Hard) Today’s problem was a good example of how dynamic programming + pattern recognition can turn a complex-looking problem into an efficient solution. 💡 Approach & Thought Process: Instead of thinking about the entire grid at once, I focused on how one row affects the next row. For a single row of 3 cells: There are two valid coloring patterns: Type A: All three colors are different Type B: The first and third cells have the same color By counting how many ways each pattern can transition to the next row: We can track just two values instead of the full grid Each new row updates these values using fixed transition formulas This avoids brute force and keeps the solution efficient even for large n This reduces the problem to a simple iterative DP update. ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) This problem reinforced an important lesson: 👉 When constraints are large, look for repeating patterns instead of brute force. 📌 Stay tuned for more daily problem-solving insights and DSA practice updates! #LeetCode #DailyCoding #DataStructures #Algorithms #DynamicProgramming #ProblemSolving #Python #DSA #LearningInPublic #SoftwareEngineering

🗓 Day 66 / 100 – #100DaysOfLeetCode 📌 Problem 865: Smallest Subtree with All the Deepest Nodes Today’s problem focused on binary tree depth analysis. The goal was to find the smallest subtree that contains all the deepest nodes in the tree. 🧠 My Approach: Used Depth-First Search (DFS) to compute information bottom-up. For each node, tracked: the maximum depth reachable from that node, and the candidate subtree root that contains all deepest nodes. Compared depths of left and right subtrees: If both sides have the same depth → the current node is the answer. Otherwise, propagated the deeper side upward. Returned the node that satisfies the condition for all deepest nodes. This approach cleanly combines depth calculation with subtree selection in a single traversal. 💡 Key Learning: This problem reinforced: ✔ how returning multiple values from DFS simplifies logic ✔ thinking bottom-up for tree optimization problems ✔ identifying the exact node where depths converge Tree problems often become elegant once the right recursive structure is identified 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #DFS #TreeTraversal #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

hi connections I just tackled LeetCode 70: Climbing Stairs. It’s a classic Dynamic Programming (DP) problem that teaches you how to break a big challenge into smaller, manageable steps. The Problem: You can climb 1 or 2 steps at a time. How many ways can you reach the top? The Logic: To get to step n, you must have come from either step n-1 or n-2. So: totalWays(n) = ways(n-1) + ways(n-2). The Optimization: Instead of a recursive approach that repeats work, or a DP array that eats up memory, I used two variables to track only the previous two steps. ✅ Time: O(n) ✅ Space: O(1) — Maximum efficiency! Sometimes the most complex-looking problems have the simplest mathematical patterns. 💡 #DynamicProgramming #LeetCode #SoftwareEngineering #Python #Algorithms #Optimization

Day 57 of #100DaysOfLeetCode Today was about revisiting a fundamental problem that looks simple on the surface but reinforces how important edge-case thinking is. 🔢 Plus One (LeetCode 66) The task was to increment a large number represented as an array of digits, without converting it directly into an integer. Sounds easy — until you hit cases like: trailing 9s all digits being 9 carry propagation across the entire array 🧠 My Approach Traverse digits from right to left If the digit is less than 9, increment and return If the digit is 9, set it to 0 and carry forward If all digits turn into 0, prepend 1 A clean and optimal carry-handling simulation. 💡 What I Learned Simple problems sharpen precision Edge cases define correctness Fundamentals matter just as much as advanced topics 📊 Complexity Analysis Time Complexity: O(n) Space Complexity: O(1) ✅ Day 57 Summary Not every day is about hard problems. Some days are about getting the basics absolutely right. Consistency > Difficulty. On to Day 58. Still moving forward. 🚀 #100DaysOfLeetCode #Day57 #LeetCode #EasyProblem #Arrays #Python #DSA #ProblemSolving #CodingJourney #Consistency #AdityaCodes

✨ Day 70 of #100DaysOfDSA 📌 LeetCode 1390: Four Divisors A great problem that blends number theory with efficient iteration. 🧩 Problem Understanding: Given an array of integers Identify numbers that have exactly four divisors Add the sum of those divisors to the final answer 🧠 Approach & How It Works: For each number, iterate only up to √n to find divisors Use a set to store unique divisors (pairing i and num // i) Early break if divisor count exceeds 4 → saves computation If a number ends with exactly 4 divisors, add their sum to the total ⚙️ Performance: 🚀 Runtime: 387 ms 📦 Memory: 12.86 MB (beats 92%) ✅ Testcases Passed: 18 / 18 📘 What I Learned: ✔ Efficient divisor counting using square root optimization ✔ Importance of early termination for performance ✔ Applying sets to avoid duplicate divisors ✔ Reinforced fundamentals of number theory Every problem sharpens logic and discipline 💡 Onward with consistency! 💪 #LeetCode #DSA #FourDivision #100DaysOfDSA #100DaysOfCode #Python #NumberTheory #ProblemSolving #CodingJourney