Longest Common Subsequence Problem Solved with Dynamic Programming

🚀 Day 522 of #750DaysOfCode 🚀 Today I solved the LeetCode problem “Minimum Changes To Make Alternating Binary String.” 🔹 Problem: Given a binary string consisting of 0s and 1s, we need to make the string alternating (no two adjacent characters are the same). In one operation, we can flip any character (0 → 1 or 1 → 0). The goal is to find the minimum number of operations required. 🔹 Key Idea: An alternating string can only follow two possible patterns: 1️⃣ 010101... 2️⃣ 101010... So we: Count mismatches assuming the string starts with '0'. The other pattern mismatches will be n - mismatch. The minimum of these two values gives the answer. 🔹 Time Complexity: O(n) – we traverse the string once. 📌 Example Input: "1111" Possible alternating strings → "0101" or "1010" Minimum operations → 2 Problems like this highlight how pattern observation can simplify the solution. #leetcode #programming #coding #java #softwaredevelopment #dsa #problemSolving #750DaysOfCode

🚀 LeetCode Problem Solved: Maximum Number of Vowels in a Substring of Given Length Today I solved LeetCode 1456 – Maximum Number of Vowels in a Substring of Given Length using the Sliding Window Technique. 🔹 Problem Statement: Given a string s and an integer k, find the maximum number of vowels in any substring of length k. 🔹 Approach: Instead of checking every substring separately, I used the Sliding Window algorithm: • Count vowels in the first window of size k • Slide the window forward one character at a time • Add the new character and remove the old one from the count • Track the maximum vowels seen so far This approach optimizes the solution to O(n) time complexity. 📌 Example: Input: s = "abciiidef", k = 3 Output: 3 Explanation: The substring "iii" contains 3 vowels, which is the maximum. 💡 Key Concepts Practiced: ✔ Sliding Window Technique ✔ String Traversal ✔ Efficient Problem Solving Consistent DSA practice on LeetCode is helping me improve my algorithmic thinking and coding efficiency. #LeetCode #DSA #SlidingWindow #ProblemSolving #CodingPractice #Java #LearningInPublic #Programming

Day 98 of my 100 Days of LeetCode Challenge 🚀 Solved Longest Increasing Subsequence (LIS) — a classic Dynamic Programming + Binary Search problem. The task is to find the length of the longest strictly increasing subsequence in an array. Approach used in my solution (Optimized O(n log n)): • Maintained a list res to store the smallest possible tail value for increasing subsequences of different lengths • If the current number is greater than the last element in res, append it • Otherwise, use binary search to find the correct position and replace the element • The size of res at the end gives the length of LIS Important Insight: The list does not store the actual LIS, but it maintains optimal subsequence tails to guarantee maximum possible length. Time Complexity: O(n log n) Space Complexity: O(n) This problem is a huge milestone in understanding advanced DP optimization techniques. #100DaysOfCode #100DaysOfLeetCode #LeetCode #DynamicProgramming #BinarySearch #LIS #Java #DSA #Algorithms #CodingJourney

Day 13 -🚀Array Pairs in Programming An Array Pair is a combination of two elements selected from an array. It is commonly used in many programming problems such as finding target sum, counting pairs, or identifying relationships between elements. Array pairs are usually represented using indices (i, j) where i < j. 📌 Example Array [10, 20, 30, 40, 50] Possible pairs: (10, 20) (10, 30) (10, 40) (10, 50) (20, 30) (20, 40) (20, 50) (30, 40) (30, 50) (40, 50) 📌 Common Problems Using Array Pairs 1️⃣ Find All Possible Pairs Generate all combinations of two elements. 2️⃣ Target Sum Pair Find pairs whose sum equals a given value. Example: Target = 50 Pairs → (10,40), (20,30) 3️⃣ Unique Pairs Avoid duplicate pairs by ensuring i < j. 4️⃣ Count Pairs Calculate the total number of pairs in an array. 💻 Basic Logic (Java) for(int i = 0; i < arr.length; i++) { for(int j = i + 1; j < arr.length; j++) { System.out.println(arr[i] + " , " + arr[j]); } } ⏱ Time Complexity: O(n²) 💡 Understanding array pairs helps solve important problems like Two Sum, pair difference, and combination-based algorithms. #Java #Programming #DataStructures #Arrays #Coding #SoftwareDevelopment #TapAcademy

🚀 Day 27/60 — LeetCode Discipline Problem Solved: Length of Last Word Difficulty: Easy Today’s problem looked simple, yet it emphasized a subtle but important skill — handling edge cases cleanly. The goal was to find the length of the last word in a string, ignoring any trailing spaces. Instead of splitting the string or using extra space, the solution efficiently traverses from the end, skipping unnecessary characters and counting only what truly matters. It’s a reminder that strong coding is not always about complexity — sometimes, it’s about precision and clarity in small details. 💡 Focus Areas: • Practiced string traversal from end • Improved handling of trailing spaces • Reinforced clean and efficient logic • Avoided unnecessary extra space usage • Strengthened edge-case thinking ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance). Small problems, when approached with discipline, sharpen the instincts that solve big ones. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Strings #Algorithms #ProblemSolving #CodingJourney #SoftwareEngineering #Java #Developers #Consistency #TechJourney #LearnToCode

🚀 Day 14 of My LeetCode Journey Today I solved an interesting problem on LeetCode: “Check if Binary String Has at Most One Segment of Ones.” 🧠 Problem Statement: Given a binary string, check whether it contains at most one continuous segment of '1's. 📌 Example: Input: "111000" → ✅ True Input: "110011" → ❌ False 💡 Key Idea: If the string contains "01", it means the sequence of `1`s ended and `0`s started. If another `1` appears later, there are multiple segments of `1`s, which makes the answer false. 🎯 Learning: Many coding problems become easier when we look for patterns instead of writing complex logic. Consistency is the key to improving problem-solving and programming skills. #LeetCode #Java #CodingJourney #ProblemSolving #100DaysOfCode #SoftwareDevelopment

🚀 Day 26/60 — LeetCode Discipline Problem Solved: Remove Element (Revision) Difficulty: Easy Today’s practice focused on revisiting a fundamental array problem involving in-place modification. The task was to remove all occurrences of a given value without using extra space, while efficiently maintaining the remaining elements. The solution leverages a simple yet powerful approach of iterating through the array and overwriting unwanted elements. Problems like this reinforce the importance of space optimization and clean in-place operations, which are often crucial in real-world scenarios. 💡 Focus Areas: • Strengthened in-place array manipulation • Practiced efficient element filtering • Reinforced two-pointer style iteration • Improved understanding of space optimization • Focused on writing concise and readable code ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) on submission. Simple problems, when practiced with discipline, continue to sharpen the core of problem-solving ability. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Arrays #TwoPointers #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

Popular Article: Wait, What? Async-Await Explained, By Matthew Hess This article by Matthew Hess delves into the world of async-await programming and provides a comprehensive guide to understanding its intricacies. He explains how async-await enables efficient handling of asynchronous code, simplifying the development process for developers. Moreover, he demonstrates various use cases where async-await can be applied, making it an essential tool in any developer's toolkit. Read the full article published in CODE Magazine here: bit.ly/46fMLUt

🚀 Day 22/60 — LeetCode Discipline Problem Solved: Reverse Integer (Revision) Difficulty: Medium Today’s practice focused on revisiting the classic integer manipulation problem — Reverse Integer. At first glance the problem appears straightforward: reverse the digits of a number. However, the real challenge lies in carefully handling edge cases such as integer overflow and maintaining correctness within the 32-bit signed integer range. Instead of using built-in conversions, the solution iteratively extracts digits and rebuilds the reversed number while ensuring that the result stays within valid bounds. 💡 Focus Areas: • Strengthened digit extraction using modulo operations • Practiced iterative number reconstruction • Handled integer overflow edge cases • Reinforced careful boundary condition checks • Focused on writing efficient and clean logic ⚡ Performance Highlight: Achieved ~99.9% runtime efficiency on submission. Revisiting foundational problems like this continues to sharpen attention to detail and improve algorithmic discipline. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Algorithms #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

🚀 Day 16/100 Days of Code Challenge Solved the classic “Sort Colors” problem (LeetCode 75) today! 🎯 🔹 Problem Summary: Given an array containing 0s, 1s, and 2s (representing colors), sort them in-place without using built-in sort. 🔹 Approach Used: Maintained three pointers: low, mid, high Efficiently partitioned the array in a single pass 🔹 Key Learning: ✔️ In-place sorting can be done in O(n) time ✔️ Using pointers smartly avoids extra space 💡 Time Complexity: O(n) 💡 Space Complexity: O(1) 🔹 Code Highlight : Used swapping and pointer movement to organize elements in one traversal. Consistency is the key 🔑 — one step closer to mastering DSA! #Day16 #100DaysOfCode #DSA #Java #CodingChallenge #LeetCode #ProblemSolving #TechJourney