Day 60 DSA Practice: Longest Palindromic Subsequence in Java

🚀 Daily DSA Practice – Day 64 | Dynamic Programming – Matrix Chain Multiplication (Java) Continuing my DSA journey, today I explored a classic Partition Dynamic Programming problem, which is widely asked in interviews. 📌 Problem Solved: Matrix Chain Multiplication (MCM) (Classic DP Problem) 🎯 Concept: Partition DP / Interval DP 🧠 Problem Idea: Given a sequence of matrices, the goal is to determine the minimum number of multiplications needed to multiply them together. The key challenge is deciding where to split the chain for optimal cost. DP Relation dp[i][j] = min( dp[i][k] + dp[k+1][j] + cost of multiplying matrices ) Where: i → j represents current matrix range k is the partition point 🔍 What I Practiced: ✔ Understanding Partition / Interval DP pattern ✔ Breaking problems into subranges (i → j) ✔ Trying all possible partition points (k) ✔ Building 2D DP table for optimal cost calculation This problem is a foundation for many advanced problems like: • Palindrome Partitioning • Burst Balloons • Boolean Parenthesization #DSA #DynamicProgramming #Java #PartitionDP #ProblemSolving #InterviewPreparation #Consistency

🚀 Java DSA Progress Update I’ve solved 125/310 problems (~40%) as part of my structured Java DSA roadmap. ✔️ Strong in: Arrays, Strings, Linked Lists, Binary Search ⚡ Focusing next on: Heap, Graphs, Dynamic Programming, Sliding Window 📌 Approach: Pattern-based problem solving Time & space analysis Learning from mistakes Re-solving for retention 🎯 Goal: Become interview-ready in 30 days with strong problem-solving fundamentals. Consistent progress > perfection. #Java #DSA #LeetCode #ProblemSolving #SoftwareEngineering

🚀 Daily DSA Practice – Day 63 | Dynamic Programming – Longest Increasing Subsequence (Optimized) (Java) Continuing my DSA journey, today I revisited a classic problem with an optimized approach, improving time complexity significantly. 📌 Problem Solved (LeetCode): 300. Longest Increasing Subsequence (Medium) 🎯 Concept: Binary Search + Dynamic Programming Optimization 🧠 Problem Idea: Previously, I solved LIS using O(n²) DP. Today, I learned the optimized O(n log n) approach using Binary Search. Instead of storing all subsequences, we maintain a list where: Each index represents the smallest possible tail of an increasing subsequence of that length Logic If current element > last → append Else → replace using binary search 🔍 What I Practiced: ✔ Optimizing DP from O(n²) → O(n log n) ✔ Applying binary search in DP problems ✔ Understanding greedy + DP hybrid approach ✔ Improving performance for large input constraints This problem taught me how optimization techniques can make a huge difference in efficiency, which is critical in coding interviews. #DSA #LeetCode #DynamicProgramming #Java #BinarySearch #ProblemSolving #InterviewPreparation #Consistenc

Solved LeetCode 17 – Letter Combinations of a Phone Number using backtracking in Java. Approach: Mapped each digit (2–9) to its corresponding characters using a simple array for O(1) access. Then used backtracking to build combinations digit by digit. For every digit: Pick each possible character Append → explore next digit → backtrack Key idea: Treat it like a tree of choices, where each level represents a digit and branches represent possible letters. Key learnings: Backtracking = build → explore → undo StringBuilder helps avoid unnecessary string creation Problems like this are about systematic exploration of choices Time Complexity: O(4^n * n) Space Complexity: O(n) recursion stack + output Consistent DSA practice is strengthening pattern recognition day by day. #Java #DSA #Backtracking #LeetCode #CodingInterview #SoftwareEngineering

Today I implemented Array Reversal in Java using the Two-Pointer Technique! 🔄 Logic: ∙ Set left = 0 and right = arr.length - 1 ∙ Swap elements at both ends ∙ Move pointers inward until they meet ✅ Input: {1, 2, 3, 4, 5} ✅ Output: 5 4 3 2 1 💡 Why Two-Pointer? Instead of using extra space, we swap in-place — making it O(n) time and O(1) space! No extra array needed. Just two pointers doing the work. 💪 Every small concept I practice brings me one step closer to DSA mastery. Keep building. Keep learning. 🙌 #100daysofcode #dsa #java #program #array #problem #leetcode #javadeveloper #learning

🚨 This small Java mistake can give wrong comparisons I used to write this: if (price == 0.1 + 0.2) { // logic } Looks correct… but it may fail ❌ --- 👉 Why? Floating-point calculations are not always exact in Java. 👉 0.1 + 0.2 = 0.30000000000000004 So comparison with "==" can fail. --- ✅ Better way: if (Math.abs(price - (0.1 + 0.2)) < 0.0001) { // logic } --- 💡 Lesson: Never use "==" for floating-point comparison. Small detail… but critical in real applications. Have you faced this before? 👇 #Java #CoreJava #Programming #BackendDeveloper #Coding #TechLearning

💡 𝗝𝗮𝘃𝗮/𝐒𝐩𝐫𝐢𝐧𝐠 𝐁𝐨𝐨𝐭 𝗧𝗶𝗽 - 𝗦𝘄𝗶𝘁𝗰𝗵 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 💎 🕯 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘄𝗶𝘁𝗰𝗵 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁 The traditional switch statement has been part of Java since the beginning. It requires explicit break statements to prevent fall-through, which can lead to bugs if forgotten. Each case must contain statements that execute sequentially, making the code verbose and error-prone. 💡 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝘄𝗶𝘁𝗰𝗵 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 Switch expressions were introduced in Java 14 as a more concise and safe alternative. Using the -> syntax, you eliminate the need for break statements and can directly return values. Multiple cases can be grouped with commas, and the compiler enforces exhaustiveness for better safety. ✅ 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 ◾ No break statements, safer and cleaner code. ◾ Direct value assignment, treat switch as an expression. ◾ Multiple labels with comma separation. ◾ Compiler exhaustiveness checks, fewer runtime errors. 🤔 Which one do you prefer? #java #springboot #programming #softwareengineering #softwaredevelopment

Refactoring for Clarity: Array Manipulation in Java 👨💻 I’ve just finished a practical exercise on array manipulation. While the goal was simple (summing two arrays), I used it as an opportunity to apply improvements. - Method decomposition: Separated concerns into specialized methods (Read, Calculate, Display). - Input validation: Built a robust loop to handle invalid inputs and prevent crashes. - Data Formatting: Used printf to create a clear, readable results table. Small improvements in logic and organization make a huge difference in software quality. #Java #Algorithms #CleanCode #Backend #LearningToCode

🔥 𝗗𝗮𝘆 𝟵𝟴/𝟭𝟬𝟬 — 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟳𝟬. 𝗖𝗹𝗶𝗺𝗯𝗶𝗻𝗴 𝗦𝘁𝗮𝗶𝗿𝘀 | 🟢 Easy | Java The gateway problem to Dynamic Programming — and one of the most iconic problems in DSA. 🪜 🔍 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 You can climb 1 or 2 steps at a time. How many distinct ways can you reach the top of n stairs? 💡 𝗧𝗵𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 To reach step i, you either came from step i-1 (1 step) or step i-2 (2 steps). So ways(i) = ways(i-1) + ways(i-2) Sound familiar? It's literally the Fibonacci sequence. 🐇 ⚡ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 — 𝗕𝗼𝘁𝘁𝗼𝗺-𝗨𝗽 𝗗𝗣 ✅ Base cases: dp[1] = 1, dp[2] = 2 ✅ Fill dp[i] = dp[i-1] + dp[i-2] for i from 3 to n ✅ Return dp[n] No recursion. No stack overflow. No repeated subproblems. Just a clean bottom-up table. 🧠 📊 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 ⏱ Time: O(n) — single pass 📦 Space: O(n) — can be further optimised to O(1) with just two variables This problem teaches the most important DP lesson: identify overlapping subproblems, store results, build up from the base. Every hard DP problem starts with this same thinking. 𝗔𝗻𝗱 𝘆𝗲𝘀 — 𝗷𝘂𝘀𝘁 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘀𝗲 𝘀𝘁𝗮𝗶𝗿𝘀, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝘄𝗮𝘀 𝗰𝗹𝗶𝗺𝗯𝗲𝗱 𝗼𝗻𝗲 𝗱𝗮𝘆 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲. 🏔️ 📂 𝗙𝘂𝗹𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gS3DX_YW 𝗝𝘂𝘀𝘁 𝟮 𝗺𝗼𝗿𝗲 𝗱𝗮𝘆𝘀. 𝗧𝗵𝗲 𝘀𝘂𝗺𝗺𝗶𝘁 𝗮𝘄𝗮𝗶𝘁𝘀! 🏁 #LeetCode #Day98of100 #100DaysOfCode #Java #DSA #DynamicProgramming #Fibonacci #CodingChallenge #Programming

🚀 DSA Preparation 💪 Solved a simple yet important String + Sliding Window problem. Focused on checking unique characters in fixed-size substrings efficiently. Great practice for improving window-based pattern recognition 🔥 🧠 Problem 🔎 Substrings of Size Three with Distinct Characters Given a string s, return the number of good substrings of length 3. 👉 A substring is good if all characters are distinct. 👉 Count all such substrings (including duplicates if they appear multiple times). Example Input: s = "xyzzaz" Output: 1 Input: s = "aababcabc" Output: 4 Improving DSA with small patterns and clean logic 🚀 #DSA #LeetCode #Strings #SlidingWindow #Java #ProblemSolving #Consistency