Reversing an Array in Java with Two Pointers

🎯 Day 62 of #100DaysOfLeetCode Today I solved the “Merge Two Sorted Lists” problem using Java This problem is a great exercise in understanding how to work with Linked Lists and pointer manipulation. The goal is to merge two sorted linked lists into one sorted list — without using extra space for another list. Key Concepts Practiced: Linked List traversal Dummy node technique Efficient merging using pointers Handling null edge cases Approach Summary: Create a dummy node to simplify edge cases. Use two pointers to traverse both lists. Compare values and link nodes in sorted order. Attach any remaining nodes at the end. #100DaysOfCode #LeetCode #Java #CodingChallenge #DataStructures #LinkedList #ProblemSolving #SoftwareEngineering

📌 Day 16/100 - Reverse String (LeetCode 344) 🔹 Problem: Reverse a given string in-place — meaning you must modify the original array of characters without using extra space. 🔹 Approach: Used the two-pointer technique — one starting at the beginning and one at the end of the array. Swap characters at both pointers, then move them closer until they meet. Efficient, clean, and runs in linear time without additional memory allocation. 🔹 Key Learnings: In-place algorithms optimize space complexity significantly. The two-pointer pattern is a versatile tool for many array and string problems. Understanding mutable vs immutable structures in Java is crucial for memory efficiency. Sometimes, the simplest logic beats the most complex one. 🧠 “True efficiency lies in simplicity, not complexity.” #100DaysOfCode #LeetCode #Java #ProblemSolving #DSA #CodingJourney #TwoPointers

#Day45 of #50DaysOfCoding Divisible Sum Pairs Problem (Java) Today, I tackled the “Divisible Sum Pairs” problem from HackerRank using Java. The objective was to identify all pairs of numbers in a list whose sum is divisible by a specified integer k. Concepts Used: - Nested loops to check every unique pair (i, j) where i < j. - Modulo operation to verify divisibility: (ar[i] + ar[j]) % k == 0. - Basic iteration and condition checking logic. Working: - Read input values n, k, and the array ar. - Iterate through all pairs. - Count how many pairs have sums divisible by k. - Print the total count. Example: If n = 6, k = 3, and ar = [1, 3, 2, 6, 1, 2], the valid pairs are (1,2), (3,6), (2,4), etc. Output: 5 Complexity: - Time Complexity: O(n²) - Space Complexity: O(1) This exercise enhanced my understanding of modulo operations, loops, and pair iteration logic — essential concepts for many coding interviews. #Java #ProblemSolving #CodingJourney #Programming #LearningEveryday #LogicBuilding #leetcode #DSA #CodingChallenge #LearnJava #CodeNewbie #Algorithms #DataStructures #TechJourney #javaProgramming #LearningInPublic #PentagonSpace

💻 Day 45 of #LeetCode Journey 🚀 ✅ Problem: Count and Say 📘 Language: Java 🔹 Status: Accepted (30/30 test cases passed) 🔹 Runtime: 4 ms | Beats 55.38% 🔹 Memory: 41.86 MB 🧠 Concept: This problem is about generating the n-th term in the “count and say” sequence. Each term is built by describing the previous term — count the number of digits and say them in order. 🧩 Approach: Start with "1". For each iteration, use a StringBuilder to construct the next sequence. Track consecutive digits using a counter. Append the count and digit when the sequence changes. 💡 Efficient string manipulation and iteration give optimal performance. 🔥 Every solved problem builds confidence. One step closer to mastering patterns in strings! #Day45 #LeetCode #Java #CodingChallenge #ProblemSolving #CountAndSay #50DaysOfCode

📌 Day 20/100 – Add Binary (LeetCode 67) Today’s challenge was about adding two binary strings and returning the result in binary format — just like simulating manual binary addition with carry logic. 🔹 Problem: Given two binary strings a and b, return their sum as a binary string without converting them into decimal. 🔹 Approach: Start from the end of both strings (like column-wise addition). Use a carry variable to handle overflow (0 or 1). Keep appending the result (sum % 2) to a StringBuilder. Reverse the string at the end since we append from LSB to MSB. 🔹 Key Learnings: StringBuilder is more efficient than string concatenation in Java. Handling indices carefully avoids edge-case bugs. Binary addition logic is similar to decimal addition — just base changes, logic stays. #100DaysOfCode #Day20 #LeetCode #Java #BinaryMath #DSA #ProblemSolving #CodingJourney #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-68) LeetCode #3228 – Maximum Number of Operations to Move Ones to the End (Java Edition) Tackled this neat string problem using a greedy approach in Java. The challenge? Move '1's to the end of the string under specific movement rules, maximizing the number of valid operations. 🧠 Core Idea: Track how many '0's we've seen and how many '1's we've already moved. Only move a '1' if there's enough '0's to justify it and the next character is also '1'. 💻 Java Strategy: Loop through the string Use counters to manage '0's and '1's Let me know how you'd tweak this or if you see a more optimal path! #Java #LeetCode #GreedyAlgorithm #StringProblems #DSA #CodingChallenge #LinkedInTech #ProblemSolving