Remove Duplicates from Sorted List on LeetCode using Java

Day 29 of #100DaysOfLeetCode 💻✅ Solved #22. Generate Parentheses on LeetCode using Java. Approach: • Used Backtracking to generate all valid combinations • Added "(" only if open brackets were available • Added ")" only when close brackets were greater than open • Ensured at every step that the parentheses remain balanced • Stopped recursion when both open and close counts reached zero Performance: ✓ Runtime: 3 ms (Beats 16.29% submissions) ✓ Memory: 45.18 MB (Beats 13.87% submissions) Key Learning: ✓ Strengthened understanding of Backtracking technique ✓ Learned how to maintain constraints during recursion ✓ Improved ability to build combinations using decision trees Learning one problem every single day 🚀 #Java #LeetCode #DSA #Backtracking #Recursion #ProblemSolving #CodingJourney #100DaysOfCode

Day 31 of #100DaysOfLeetCode 💻✅ Solved #94. Binary Tree Inorder Traversal on LeetCode using Java. Approach: • Used recursion to perform inorder traversal • Followed Left → Root → Right order strictly • Traversed left subtree first before processing current node • Stored node values in a list during traversal • Returned the final list after complete traversal Performance: ✓ Runtime: 0 ms (Beats 100.00% submissions) ✓ Memory: 42.76 MB (Beats 98.30% submissions) Key Learning: ✓ Strengthened understanding of tree traversal patterns ✓ Improved clarity on recursion stack behavior ✓ Reinforced difference between preorder, inorder, and postorder traversal Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinaryTree #Recursion #TreeTraversal #ProblemSolving #CodingJourney #100DaysOfCode

Day 32 of #100DaysOfLeetCode 💻✅ Solved #144. Binary Tree Preorder Traversal on LeetCode using Java. Approach: • Used recursion to perform preorder traversal • Followed Root → Left → Right order strictly • Added node value before traversing subtrees • Traversed left subtree first, then right subtree • Stored values in a list during traversal Performance: ✓ Runtime: 0 ms (Beats 100.00% submissions) ✓ Memory: 42.83 MB (Beats 96.77% submissions) Key Learning: ✓ Strengthened understanding of preorder traversal pattern ✓ Clearly differentiated preorder from inorder traversal ✓ Improved recursive tree traversal implementation skills Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinaryTree #Recursion #TreeTraversal #ProblemSolving #CodingJourney #100DaysOfCode

Day 46 of #100DaysOfLeetCode 💻✅ Solved #374. Guess Number Higher or Lower problem in Java. Approach: • Used Binary Search to efficiently guess the number. • Calculated mid using low + (high - low) / 2 to avoid overflow. • Used the guess(mid) API to check if the number is higher, lower, or correct. • Updated the search range accordingly until the number was found. Performance: ✓ Runtime: 0 ms (Beats 100% submissions) 🚀 ✓ Memory: 41.68 MB (Beats 97.84% submissions) Key Learning: ✓ Practiced implementing binary search on a real problem ✓ Learned how to safely calculate mid to prevent integer overflow ✓ Strengthened problem-solving skills with search algorithms Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinarySearch #ProblemSolving #CodingJourney #100DaysOfCode

Day 36 of #100DaysOfLeetCode 💻✅ Solved #111. Minimum Depth of Binary Tree on LeetCode using Java. Approach: • Used recursive approach to calculate minimum depth • Returned 0 when root is null (base case) • If one subtree is null, avoided taking minimum of 0 (handled edge case carefully) • Returned 1 + minimum depth of valid subtree • Ensured correct handling of skewed trees Performance: ✓ Runtime: 6 ms ✓ Memory: 82.20 MB Key Learning: ✓ Understood difference between minimum depth and maximum depth logic ✓ Learned importance of handling null subtree cases correctly ✓ Improved confidence in solving binary tree recursion problems Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinaryTree #Recursion #TreeProblems #ProblemSolving #CodingJourney #100DaysOfCode

Day 34 of #100DaysOfLeetCode 💻✅ Solved #110. Balanced Binary Tree on LeetCode using Java. Approach: • Used a bottom-up recursive approach to calculate height • Returned -1 immediately if any subtree is unbalanced • Compared left and right subtree heights at each node • Checked if the height difference is greater than 1 • Stopped early to optimize unnecessary computations Performance: ✓ Runtime: 0 ms (Beats 100.00% submissions) ✓ Memory: 45.33 MB (Beats 95.41% submissions) Key Learning: ✓ Understood how to combine height calculation with balance checking ✓ Learned early termination technique in recursion ✓ Improved problem-solving for tree-based recursive problems Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinaryTree #Recursion #TreeProblems #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 92 – #100DaysOfCode Qn. Combination Sum III Today I worked on the Combination Sum III problem using recursion and backtracking in Java. 🔹 Problem Summary: Find all possible combinations of k numbers that add up to n, using numbers 1–9 only once. 🔹 My Approach: Used recursion to explore all possible number combinations. Maintained a running sum and a temporary list. When the sum equals n and the list size equals k, the combination is added to the result. Used a HashSet to avoid duplicate combinations. 🔹 Key Learning: This problem helped me strengthen my understanding of: Backtracking Recursive decision trees Managing state during recursion (adding/removing elements) 📌 Next Goal: Optimize the solution further by avoiding the HashSet and pruning unnecessary recursive calls. #Day92 #LeetCode #DSA #Java #Recursion #Backtracking #CodingJourney

Day 30 of #100DaysOfLeetCode 💻✅ Solved #108. Convert Sorted Array to Binary Search Tree on LeetCode using Java. Approach: • Used Divide and Conquer strategy • Selected the middle element as the root to maintain balance • Recursively built the left subtree using left half of array • Recursively built the right subtree using right half of array • Ensured the tree remains height-balanced at every step Performance: ✓ Runtime: 0 ms (Beats 100.00% submissions) ✓ Memory: 45.18 MB (Beats 44.10% submissions) Key Learning: ✓ Understood how sorted arrays can directly map to balanced BST ✓ Strengthened recursion fundamentals in tree construction ✓ Improved understanding of height-balanced binary trees Learning one problem every single day 🚀 #Java #LeetCode #DSA #BinarySearchTree #Recursion #DivideAndConquer #ProblemSolving #CodingJourney #100DaysOfCode

Day 44 of #100DaysOfLeetCode 💻✅ Solved Very Simple #169. Majority Element problem in Java. Approach: • Sorted the given array using Arrays.sort() • Observed that the majority element appears more than n/2 times • After sorting, the majority element will always be present at index n/2 • Returned the element at nums[n/2] Performance: ✓ Runtime: 7 ms (Beats 42.86% submissions) ✓ Memory: 55.96 MB (Beats 16.21% submissions) Key Learning: ✓ Understood how sorting can help identify the majority element ✓ Learned the property that the majority element always occupies the middle position after sorting ✓ Practiced array manipulation and problem solving in Java Learning one problem every single day 🚀 #Java #LeetCode #DSA #Arrays #ProblemSolving #CodingJourney #100DaysOfCode

Day 38 of #100DaysOfLeetCode 💻✅ Solved #222. Count Complete Tree Nodes problem in Java. Approach: • Used recursion to traverse the binary tree • Counted the current node and recursively counted nodes in left and right subtrees • Added the counts to get the total number of nodes • Returned 0 when the node is null to stop recursion Performance: ✓ Runtime: 0 ms (Beats 100% submissions) ✓ Memory: 44 MB Key Learning: ✓ Practiced recursion with binary trees ✓ Understood how tree traversal helps count nodes in a tree ✓ Improved recursive thinking while solving tree problems Learning one problem every single day 🚀 #Java #LeetCode #DSA #ProblemSolving #CodingJourney #100DaysOfCode