Solving "Find Pivot Index" problem on LeetCode with Java

🔹 Day 38 – LeetCode Practice Problem: Missing Number (LeetCode #268) 📌 Problem Statement: You are given an array nums containing n distinct numbers taken from the range [0, n]. Find the one number that is missing from the array. ✅ My Approach (Java): Calculated the expected sum using the formula total = \frac{n \times (n + 1)}{2} The missing number = total - actual sum. This method avoids sorting or extra space, keeping it optimal. 📊 Complexity: Time Complexity: O(n) Space Complexity: O(1) ⚡ Submission Results: Accepted ✅ Runtime: 0 ms (Beats 100%) 🚀 Memory: 45.51 MB (Beats 32.44%) 💡 Reflection: A great reminder that sometimes the most efficient solutions come from simple mathematical reasoning. Clean, elegant, and lightning-fast! #LeetCode #ProblemSolving #Java #DSA #CodingPractice #Learning

🔥 #100DaysOfDSA — Day 30/100 Topic: Reverse an Array in Java 🔁 💡 What I Did Today: Today, I explored how to reverse an array manually using the two-pointer approach. Instead of relying on built-in methods, I learned how to swap elements from both ends until the array is completely reversed. 🧠 Logic Used: Initialize two pointers: start = 0 (beginning of array) end = numbers.length - 1 (end of array) While start < end: Swap numbers[start] and numbers[end] Move pointers inward → start++ and end-- 📊 Example: Input → {2, 4, 6, 8, 10} Output → {10, 8, 6, 4, 2} ⚙️ Time Complexity: O(n) — each element is swapped once. O(1) space — done in-place without using extra arrays. ✨ Takeaway: Learning to reverse an array manually helps strengthen the concept of pointers, loops, and in-place operations. Sometimes the simplest logic gives the biggest "aha!" moment 💡 #100DaysOfCode #Day30 #Java #DSA #Arrays #ProblemSolving #CodingJourney #LearnInPublic #DeveloperLife #CodeNewbie #LogicBuilding

Day 50 of #75DaysDSAChallenge Problem: 7. Reverse Integer Difficulty: 🟠 Medium Platform: LeetCode 🧩 Problem Statement Given a signed 32-bit integer x, return x with its digits reversed. If reversing x causes it to go outside the signed 32-bit integer range [-2³¹, 2³¹ - 1], return 0. Example: Input: x = 123 Output: 321 💡 Approach 1️⃣ Extract the last digit using x % 10. 2️⃣ Remove the last digit using x / 10. 3️⃣ Add the digit to the reversed number. 4️⃣ Before updating, check for overflow conditions. 5️⃣ Continue until all digits are processed. #LeetCode #Java #DSA #CodingChallenge #75DaysDSAChallenge #ProblemSolving #TechLearning #CodingJourney

DSA Practice – Day 68 🚀 Problem: Subsets II (LeetCode 90) ✨ Brute Force Approach 1. Generate all possible subsets using recursion (like Subsets I). 2. Use a Set or HashSet to store unique subsets and avoid duplicates. 3. Convert the set back to a list before returning the result. Time Complexity: O(2ⁿ × n) (to generate all subsets) Space Complexity: O(2ⁿ × n) (storing all subsets) ✨ Optimal Approach (Backtracking with Duplicate Handling) We use backtracking, but with a twist — handle duplicates smartly. Steps: 1. Sort the array so duplicates come together. 2. Use a loop with a check if(i != ind && nums[i] == nums[i - 1]) continue; to skip duplicates. 3. Recursively generate subsets and backtrack after each recursive call. Time Complexity: O(2ⁿ × n) Space Complexity: O(n) 🌟 Key Idea Sorting + Skipping duplicates ensures we only generate unique subsets efficiently. #Day68 #Backtracking #DSA #Java #LeetCode #Placements

🚀Day40/100 - Problem of the day:- Maximum Number of Operations to Move Ones to the End. 🎯 Goal: Solved a LeetCode problem to find the maximum number of operations possible from a binary string using a simple one-pass logic in Java. 💡 Core Idea: The main idea is to iterate through the string once, keep a running count of '1's, and increment the result whenever the current character '0' is smaller than the previous one '1'. This approach ensures efficient comparison and counting without extra space. 📘 Key Takeaway: Sometimes, the most efficient solutions come from observing pattern transitions (like 1 → 0) rather than using complex data structures. Optimizing logic within a single loop can significantly improve runtime performance. 🧠 Time Complexity: O(n) — only one traversal of the string. 💾 Space Complexity: O(1) — uses only a few integer variables. #100DaysChallenge #Java #DSA #Day40 #ProblemSolving #CodingJourney #Leetcode

🐇🐢 Delete Middle Node of a Linked List (Optimized Way) For this, I used the Rabbit–Tortoise (Slow–Fast Pointer) method, which is much more optimized than the normal approach. The slow pointer moves one step at a time, while the fast pointer moves two steps — when the fast pointer reaches the end, the slow pointer points to the middle node, which I then deleted. 📘 Interesting Observation: In Java, when you remove a node, the garbage collector automatically frees up memory. But in C++, you need to manually delete the node using the delete keyword — otherwise, it can lead to memory leaks. That’s one of the biggest differences between languages that helps us understand how memory management works under the hood! ⏱️ Time Complexity: O(n/2) ≈ O(n) 💾 Space Complexity: O(1) ✨ Lesson learned: Every concept seems tough in the beginning, but once you dive deeper, it becomes easier and more enjoyable. Keep practicing — you’ll be amazed by your own growth! 🚀 #DSA #LinkedList #CPlusPlus #Java #MemoryManagement #ProblemSolving #SlowFastPointer #KeepLearning

🚀 Day 55 of #100DaysOfCode Challenge Problem: LeetCode #219 – Contains Duplicate II Language: Java ☕ Today I solved an interesting array problem that checks whether a duplicate element exists within a given distance k in the array. 💡 Logic: Use a HashMap to remember the last index of each number. If the same number appears again, check if the difference between indices ≤ k. If yes → return true, else keep checking. 💻 Code: import java.util.HashMap; public class Solution { public boolean containsNearbyDuplicate(int[] nums, int k) { HashMap<Integer, Integer> map = new HashMap<>(); for (int i = 0; i < nums.length; i++) { if (map.containsKey(nums[i]) && i - map.get(nums[i]) <= k) { return true; } map.put(nums[i], i); } return false; } } 🧠 Example: Input: [1,2,3,1], k = 3 Output: true ✅ (same number 1 appears within distance 3) ⚙️ Key Concepts: HashMap for quick lookup Difference check using indices Time Complexity → O(n) Space Complexity → O(n) 💬 Every day’s problem teaches me something new — today it was about using maps smartly to track elements efficiently. On to the next challenge! 💪 #Day55 #LeetCode #Java #100DaysOfCode #CodingJourney #ProblemSolving #HashMap #DSA

🚀 Day 55 of #100DaysOfCode Challenge Problem: LeetCode #219 – Contains Duplicate II Language: Java ☕ Today I solved an interesting array problem that checks whether a duplicate element exists within a given distance k in the array. 💡 Logic: Use a HashMap to remember the last index of each number. If the same number appears again, check if the difference between indices ≤ k. If yes → return true, else keep checking. 💻 Code: import java.util.HashMap; public class Solution { public boolean containsNearbyDuplicate(int[] nums, int k) { HashMap<Integer, Integer> map = new HashMap<>(); for (int i = 0; i < nums.length; i++) { if (map.containsKey(nums[i]) && i - map.get(nums[i]) <= k) { return true; } map.put(nums[i], i); } return false; } } 🧠 Example: Input: [1,2,3,1], k = 3 Output: true ✅ (same number 1 appears within distance 3) ⚙️ Key Concepts: HashMap for quick lookup Difference check using indices Time Complexity → O(n) Space Complexity → O(n) 💬 Every day’s problem teaches me something new — today it was about using maps smartly to track elements efficiently. On to the next challenge! 💪 #Day55 #LeetCode #Java #100DaysOfCode #CodingJourney #ProblemSolving #HashMap #DSA

🚀 Day 55 of #100DaysOfCode Challenge Problem: LeetCode #219 – Contains Duplicate II Language: Java ☕ Today I solved an interesting array problem that checks whether a duplicate element exists within a given distance k in the array. 💡 Logic: Use a HashMap to remember the last index of each number. If the same number appears again, check if the difference between indices ≤ k. If yes → return true, else keep checking. 💻 Code: import java.util.HashMap; public class Solution { public boolean containsNearbyDuplicate(int[] nums, int k) { HashMap<Integer, Integer> map = new HashMap<>(); for (int i = 0; i < nums.length; i++) { if (map.containsKey(nums[i]) && i - map.get(nums[i]) <= k) { return true; } map.put(nums[i], i); } return false; } } 🧠 Example: Input: [1,2,3,1], k = 3 Output: true ✅ (same number 1 appears within distance 3) ⚙️ Key Concepts: HashMap for quick lookup Difference check using indices Time Complexity → O(n) Space Complexity → O(n) 💬 Every day’s problem teaches me something new — today it was about using maps smartly to track elements efficiently. On to the next challenge! 💪 #Day55 #LeetCode #Java #100DaysOfCode #CodingJourney #ProblemSolving #HashMap #DSA

🚀Day 99/100 #100DaysOfLeetCode 🔍Problem: Sum of Two Integers✅ 💻Language: Java 💡Approach: Instead of using the ‘+’ or ‘–’ operators, this problem leverages bit manipulation to perform addition. 🔸Use XOR (^) to calculate the sum without carry. 🔸Use AND (&) followed by a left shift (<< 1) to calculate the carry. 🔸Repeat until no carry remains. 📚Key Takeaways: 🔹Reinforced understanding of bitwise operations. 🔹Learned how addition can be simulated using logical operations. 🔹Improved understanding of low-level arithmetic computation. ⚡Performance: ⏱️Runtime: 0 ms (Beats 100.00%) 💾Memory: 40.88 MB (Beats 10.05%) #100DaysOfLeetCode #Java #BitManipulation #CodingChallenge #ProblemSolving #DSA #LeetCode #CodingJourney