Construct Binary Tree from Inorder and Postorder Traversal on LeetCode

🚀 Day 64 of #100DaysOfCode Solved 222. Count Complete Tree Nodes on LeetCode 🔗 🧠 Key Insight: In a complete binary tree, all levels are fully filled except possibly the last, and nodes are as left as possible. 👉 This property helps us optimize beyond simple traversal ⚙️ Approach (Simple DFS - Your Solution): 1️⃣ If root is null → return 0 2️⃣ Recursively count: 🔹 left = countNodes(root.left) 🔹 right = countNodes(root.right) 3️⃣ Total nodes: 👉 1 + left + right ⏱️ Time Complexity: Current → O(n) Optimized → O(log² n) 📦 Space Complexity: O(h) #100DaysOfCode #LeetCode #DSA #BinaryTree #Recursion #DivideAndConquer #Java #InterviewPrep #CodingJourney

Day 9/30 — DSA Challenge 🚀 Problem: Construct Binary Tree from Preorder and Inorder Traversal Topic: Tree + Recursion + Hashing Difficulty: Medium Approach: Used preorder to identify root node Used inorder to divide left and right subtrees Stored inorder indices in a HashMap for O(1) lookup Recursively built left and right subtrees Mistake / Challenge: Initially struggled to understand how preorder and inorder work together Got confused in managing indices for subtree boundaries Fix: Used a pointer for preorder traversal Used HashMap to quickly find root index in inorder Carefully handled left and right subtree ranges Key Learning: Preorder → gives root Inorder → gives structure (left/right split) Combining both helps reconstruct the tree Time Taken: 1 hour Consistency check ✅ See you on Day 10. GitHub Repo: https://lnkd.in/gHW9vKUf #DSA #LeetCode #Java #Trees #Recursion #LearningInPublic

🚀 Day 75 of #100DaysOfCode Solved 103. Binary Tree Zigzag Level Order Traversal on LeetCode 🔗 🧠 Key Insight: This is a variation of level order traversal where: 👉 Levels alternate between left → right and right → left ⚙️ Approach (BFS + Direction Toggle): 1️⃣ Use a queue for level order traversal 2️⃣ Maintain a flag (leftToRight or sign) 🔹 true → left → right 🔹 false → right → left 3️⃣ For each level: 🔹 Create a list 🔹 Traverse all nodes in that level 4️⃣ Insert values based on direction: 🔹 If left → right → addLast(val) 🔹 Else → addFirst(val) 5️⃣ Add level list to result 6️⃣ Flip direction: 👉 sign *= -1 or toggle boolean ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(n) #100DaysOfCode #LeetCode #DSA #BinaryTree #BFS #Queue #Java #InterviewPrep #CodingJourney

Day 76/100 Completed ✅ 🚀 Solved LeetCode – Search a 2D Matrix (Java) ⚡ Implemented an optimized binary search approach by treating the 2D matrix as a flattened sorted array. Converted 1D index into 2D coordinates (row = mid / m, col = mid % m) to efficiently locate the target in O(log(m × n)) time. 🧠 Key Learnings: • Applying binary search on a 2D matrix • Converting 1D index to 2D (row & column mapping) • Reducing time complexity from O(m × n) → O(log(m × n)) • Importance of problem observation (matrix behaves like sorted array) 💯 This problem strengthened my understanding of binary search variations and how to apply it beyond simple 1D arrays. 🔗 Profile: https://lnkd.in/gaJmKdrA #leetcode #datastructures #algorithms #java #matrix #binarysearch #arrays #optimization #problemSolving #100DaysOfCode 🚀

Day 95 of #365DaysOfLeetCode Challenge Today’s problem: **Path Sum III (LeetCode 437)** This one looks like a typical tree problem… but the optimal solution introduces a powerful concept: **Prefix Sum + HashMap in Trees** 💡 **Core Idea:** Instead of checking every possible path (which would be slow), we track **running sums** as we traverse the tree. 👉 If at any point: `currentSum - targetSum` exists in our map → we’ve found a valid path! 📌 **Approach:** * Use DFS to traverse the tree * Maintain a running `currSum` * Store prefix sums in a HashMap * Check how many times `(currSum - targetSum)` has appeared * Backtrack to maintain correct state ⚡ **Time Complexity:** O(n) ⚡ **Space Complexity:** O(n) **What I learned today:** Prefix Sum isn’t just for arrays — it can be **beautifully extended to trees**. This problem completely changed how I look at tree path problems: 👉 From brute-force traversal → to optimized prefix tracking 💭 **Key takeaway:** When a problem involves “subarray/paths with a given sum,” think: ➡️ Prefix Sum + HashMap #LeetCode #DSA #BinaryTree #PrefixSum #CodingChallenge #ProblemSolving #Java #TechJourney #Consistency

Day 72/100 Completed ✅ 🚀 Solved LeetCode – Matrix Diagonal Sum (Java) ⚡ Implemented an efficient approach to calculate the sum of both primary and secondary diagonals of a square matrix in a single traversal. Avoided double-counting the center element by handling the odd-sized matrix case separately. 🧠 Key Learnings: • Efficient diagonal traversal in a matrix • Handling overlapping elements (center in odd n) • Writing optimal O(n) solutions instead of nested loops • Clean and concise index-based logic 💯 This problem improved my understanding of matrix patterns and how to optimize traversal by reducing unnecessary iterations. 🔗 Profile: https://lnkd.in/gaJmKdrA #leetcode #datastructures #algorithms #java #matrix #arrays #problemSolving #100DaysOfCode 🚀

Day 71/100 Completed ✅ 🚀 Solved LeetCode – Reshape the Matrix (Java) ⚡ Implemented an efficient matrix transformation approach by mapping elements from the original matrix to the reshaped matrix using a single traversal. Instead of creating intermediate structures, used index manipulation (count / c, count % c) to place elements correctly while maintaining row-wise order. 🧠 Key Learnings: • Understanding how to map 2D indices into a linear traversal • Efficiently converting between different matrix dimensions • Handling edge cases where reshape is not possible • Writing clean and optimized nested loop logic 💯 This problem strengthened my understanding of matrix traversal and index mapping techniques, which are very useful in array and grid-based problems. 🔗 Profile: https://lnkd.in/gaJmKdrA #leetcode #datastructures #algorithms #java #matrix #arrays #problemSolving #100DaysOfCode 🚀

🚀 𝐃𝐚𝐲 90/100 – 𝐖𝐨𝐫𝐝 𝐏𝐚𝐭𝐭𝐞𝐫𝐧  Today’s problem was Word Pattern — a great exercise on HashMap + Set usage and understanding bijection. 🔍 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: We need to ensure a one-to-one mapping between pattern characters and words. 💡 𝐂𝐨𝐫𝐞 𝐈𝐝𝐞𝐚: Each character maps to exactly one word Each word maps to exactly one character (no duplicates mapping)  𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬? HashMap → to store character → word mapping HashSet → to track already mapped words ensures a valid bijection. ⚡  𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Split string into words If lengths mismatch → return false Traverse pattern: If mapping exists → validate Else → check if word already used Return true if all checks pass ⏱️ 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: 𝐎(𝐧) 📦 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: 𝐎(𝐧) #Day90 #100DaysOfCode #Java #DSA #LeetCode #HashMap #CodingJourney #Consistency

🚀 Day 22/100 – DSA Challenge Today’s problem: Climbing Stairs (LeetCode | Easy | DP) The problem is simple: You can climb either 1 or 2 steps at a time. How many distinct ways are there to reach the top? 🔴 Approach 1: Recursion (Brute Force) 👉 At each step, you have 2 choices: Take 1 step Take 2 steps 📌 Java Code: class Solution { public int climbStairs(int n) { if (n <= 2) return n; return climbStairs(n - 1) + climbStairs(n - 2); } } ⚡ Complexity: Time: O(2ⁿ) ❌ (recomputes subproblems) Space: O(n) (recursion stack) 🟡 Approach 2: Memoization (Top-Down DP) 👉 Store results of subproblems to avoid recomputation 📌 Java Code: class Solution { public int climbStairs(int n) { int[] dp = new int[n + 1]; return helper(n, dp); } private int helper(int n, int[] dp) { if (n <= 2) return n; if (dp[n] != 0) return dp[n]; dp[n] = helper(n - 1, dp) + helper(n - 2, dp); return dp[n]; } } ⚡ Complexity: Time: O(n) ✅ Space: O(n) 🟢 Observation: This problem is essentially the Fibonacci sequence: f(n) = f(n-1) + f(n-2) 🎯 Key Learning: Start with recursion to understand the problem, then optimize using DP to eliminate overlapping subproblems. 📈 Day 22 done—getting better at recognizing DP patterns! #100DaysOfCode #DSA #DynamicProgramming #Java #CodingJourney

Today I solved Two Sum (LeetCode #1 - Easy) 💡 🔍 Problem: Find two indices such that their values add up to the target. 🧠 Approach I used: HashMap Instead of checking every pair (O(n²)), I used a HashMap to optimize the solution. 👉 Steps: Calculate complement = target - current element Check if complement exists in the map If yes → return indices ✅ If no → store the element in the map ⚡ Time Complexity: O(n) ⚡ Space Complexity: O(n) 💡 This problem helped me understand how using extra space can reduce time complexity. 📌 Key Learning: “Optimize brute force by using HashMap for faster lookups.” #DSA #Java #LeetCode #CodingJourney #ProblemSolving #100DaysOfCode