Java DSA: Efficient Array Problem Solving with Math

🚀 DSA Learning Journey | Day 13 | Java Solved “20. Valid Parentheses.” 💡 Key Idea: Used a Stack to track opening brackets and match them with corresponding closing brackets. ⚙ Implementation • Language: Java • Time Complexity: O(n) • Space Complexity: O(n) 📚 Learning: How stack data structure helps in solving matching and validation problems efficiently. #JavaDeveloper #DSA #LeetCode #ProblemSolving #Stack #Algorithms #CodingJourney #100DaysOfCode #DevelopersOfLinkedIn

🚀 DSA Learning Journey | Day 4 | Java Solved “Kth Largest Element in an Array.” 💡 Key Idea: Used the Heap / Priority Queue approach to efficiently find the Kth largest element without fully sorting the array. ⚙ Implementation • Language: Java • Time Complexity: O(n log k) • Space Complexity: O(k) 📚 Learning how heaps help optimize selection problems in arrays. #Java #DSA #LeetCode #ProblemSolving #JavaDeveloper #Algorithms

📚 New article just published on SYUTHD! 🔖 Java's AI Frontier: Integrating Generative AI & LLMs with Spring AI for Modern Apps 🏷️ Category: Java Programming 📖 Full article → https://lnkd.in/gsWqaJDx 👉 Follow our page for more tech tutorials: https://lnkd.in/gsJDptPM 💬 Telegram: https://t.me/nisethtechno 👍 Facebook: https://lnkd.in/gsKv3Dyn #JavaProgramming #Tech #Tutorial #Programming #TechBlog #2026

Binary Search is a powerful algorithm that efficiently finds an element in a sorted array by repeatedly dividing the search space in half. This approach significantly reduces time complexity compared to linear search. ✅ Problem: Binary Search 💻 Language: Java ⚡ Time Complexity: O(log n) Consistent practice is the key to mastering Data Structures & Algorithms. #LeetCode #BinarySearch #DSA #Java #Coding #ProblemSolving #SoftwareDevelopment

Shifting from Sorting to Searching in Java DSA! After learning sorting algorithm from basics to complex like recursion of Quick and Merge sort, today I started searching algorithm like Linear Search. It is as straightforward as it gets: 1. Start at the beginning of the array. 2.Check every single element one by one. 3. Stop when you find the target. With a time complexity of O(n), it isn't the most efficient algorithm for massive datasets. However, it doesn't require the array to be sorted first, which makes it a great reminder that sometimes a simple, brute-force approach is exactly what you need for small, unsorted data. #DSA #Java #LinearSearch

🚀 Day 12 of My DSA Journey Today I solved the “Contains Duplicate” problem on LeetCode using Java. 💡 Problem Statement Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. Example: Input: [1,2,3,1] Output: true Because the value 1 appears more than once. ⚙️ Approach I Used I solved this problem using sorting. Steps: 1️⃣ First, I sorted the array using Arrays.sort() 2️⃣ Then I traversed the array once 3️⃣ Compared each element with the next element 4️⃣ If nums[i] == nums[i+1], it means a duplicate exists → return true 5️⃣ If no duplicates are found, return false 📊 Complexity Analysis Time Complexity: O(n log n) → due to sorting Space Complexity: O(1) (ignoring sorting internal space) 📚 Key Learning Even simple problems can be solved in multiple ways. Sorting helped reduce the problem to a simple adjacent comparison. In future, I’ll also explore a HashSet approach which can solve this in O(n) time. Another day of improving problem-solving skills and algorithmic thinking. On to Day 13 tomorrow. 💪 #DSA #LeetCode #100DaysOfCode #Java #ProblemSolving #CodingJourney #Algorithms #LearningInPublic #Consistency

🚀 DSA Learning Journey | Day 5 | Java Solved “Maximum Product Subarray.” 💡 Key Idea: Tracked both maximum and minimum products at each step since a negative number can flip the sign and turn a minimum into a maximum. ⚙ Implementation • Language: Java • Time Complexity: O(n) • Space Complexity: O(1) 📚 Learning how handling negative numbers and dynamic updates is important in product-based subarray problems. #Java #DSA #LeetCode #ProblemSolving #JavaDeveloper #Algorithms

Day 4 of Java with DSA Journey 🚀 📌 Topic: Search Insert Position (LeetCode 35) 💬 Quote: "Binary Search is about more than finding a needle in a haystack; it's about knowing exactly where to put a new needle." ✨ What I Learned: 🔹 Power of the low Pointer: If the target isn’t found, low directly gives the correct insertion index. 🔹 Finding Position Even When Missing: Binary Search doesn’t just search — it tells you where the element belongs. 🔹 Efficient Gap Detection: Even for missing values, we maintain O(log n) efficiency. 🔹 Complexity: ⏱ Time: O(log n) 📦 Space: O(1) 🧠 Problem Solved: ✔️ Search Insert Position 💡 Key Insight: Binary Search helps determine the rank/position of a number in a sorted array — whether it exists or not ⚡ ⚡ Interview Insight (Post-Loop Behavior): 👉 When the loop ends (low > high): low → first index greater than target high → last index smaller than target 🎯 That’s why low = insert position 🔑 Takeaway: Binary Search is not just about finding — it's about positioning. Consistency is the real key 🔑 #DSA #LeetCode #Java #CodingJourney #BinarySearch #ProblemSolving #100DaysOfCode #Algorithms #TechLearning #Day4 #Array #MCA #lnct

Java 26 is here, and it’s time to stop calling it "just for enterprise." ☕️ With 10 new JEPs, Java is aggressively positioning itself for the AI and cloud-native era. What’s actually moving the needle: Structured Concurrency is reaching the finish line (6th preview!). Vector API is gaining serious traction for high-performance math. GC Improvements are slashing latency for real-time services. The Hot Take: While everyone is distracted by the latest Python wrappers, the JVM is becoming one of the best runtimes for production AI backends. It’s 2026. If you’re still writing off Java as "boring," you’re missing the bigger picture. #Java26 #Backend #SoftwareEngineering #AI #JVM