Exploring Polymorphism and Virtual Functions in C++

🚀 Day 63 of #100DaysOfCode Today I tackled LeetCode 84 — Largest Rectangle in Histogram using C++ 💪 📌 Problem Summary: Given an array representing the heights of bars in a histogram, the goal is to find the largest rectangular area that can be formed within the histogram. For example: Input: [2,1,5,6,2,3] Output: 10 (formed between heights 5 and 6) 📌 Approach: This is a classic stack-based problem involving Next Smaller Element (NSE) and Previous Smaller Element (PSE) logic. Steps: 1️⃣ Use a monotonic stack to find the index of the next smaller element for each bar. 2️⃣ Similarly, find the previous smaller element for each bar. 3️⃣ Compute width = nsi[i] - psi[i] - 1 4️⃣ Calculate area = height[i] * width and keep track of the maximum. 📌 Complexity: ⏱ Time: O(n) — each element is pushed and popped once. 💾 Space: O(n) — for storing stack and helper arrays. 📌 Key Learning: This problem strengthens the understanding of monotonic stacks, boundary calculations, and range-based area determination — a foundation for many advanced DSA problems (like Maximal Rectangle). Consistency pays off — 60 days strong and still coding daily! 🔥 Raj Vikramaditya Raghav Garg Nancy Solanki Shweta Arora Harsh Raj Harshita Verma Love Babbar Prince Singh Shivam Mahajan Rohit Negi Neeraj Walia Nishant Chahar Kushal Vijay #LeetCode #100DaysOfCode #CodingChallenge #Cplusplus #DataStructures #LinkedList #ProblemSolving #SoftwareEngineering #Developers #Programming #TechJourney #KeepLearning #DailyCoding #CodeNewbie#LeetCode #100DaysOfCode #Programming #Cplusplus #LinkedList #ProblemSolving #SoftwareEngineering #TechJourney #CodeNewbie #DSA

🚀 Day 3 Topic: Sealed, Static & Partial Classes in C# C# gives us three special keywords that change how a class behaves. Let’s understand them in the simplest possible way 👇 🔒 1️⃣ Sealed Class — “No Inheritance Allowed” A sealed class can create objects, but cannot be inherited. ✔️ Meaning You can create an object of a sealed class But you cannot create a child class from it ✔️ When to use? When you want to stop other classes from extending/modifying your class 💡 Think of it like a locked box — you can use it, but can’t add anything to it. 🧰 2️⃣ Static Class — “Only One Copy, No Objects” A static class cannot create objects. Everything inside it must be static. ✔️ Meaning ❌ No object creation ❌ Cannot inherit or be inherited ✔️ Use directly by class name (like Math) ✔️ When to use? Utility/helper classes Global functions (e.g., “Convert”, “Math”) 💡 Think of it like a toolbox on a wall — everyone can use the tools, but nobody can make a copy. 📂 3️⃣ Partial Class — “One Class, Many Files” A partial class can create objects, because it’s just a normal class split into pieces. ✔️ Meaning ✔️ Object creation allowed ✔️ Inheritance allowed ✔️ All parts combine into one class at compile time ✔️ When to use? Large classes Auto-generated + manual code separation + Cleaner organization ❓ Tricky Interview Question 👉 If a static class cannot be instantiated, how does its static constructor run? 💬 One-Line Summary “Sealed stops inheritance, Static stops objects, Partial splits the class.” #DotNet #CSharp #DotNetDeveloper #DotNetCommunity #Programming #CodingChallenge #CodeDesign #TechLearning #SoftwareEngineering #CleanCode #OOP #Developers #100DaysOfCode #LearnToCode #Day3 #CSharpBasics #MicrosoftDotNet #BackendDeveloper #DevCommunity

🎯 Day 10 of my 120 Days of Coding Challenge — LeetCode Problem 10: Regular Expression Matching (Hard) Today’s challenge was one of the classic Dynamic Programming problems that really tests your logical thinking and pattern-handling skills! 💡 🧩 Problem Statement: Given a string s and a pattern p, we need to determine if the entire string matches the pattern. The pattern may contain: . → Matches any single character * → Matches zero or more of the preceding element For example: s = "aa", p = "a" → ❌ (no match) s = "aa", p = "a*" → ✅ (matches) s = "ab", p = ".*" → ✅ (matches any string) 💭 Approach Used: I used a Dynamic Programming (DP) approach to efficiently check matches between prefixes of s and p. Here’s how I broke it down: Define dp[i][j] as whether the first i characters of s match the first j characters of p. Handle base cases like empty strings and patterns with *. Build up the DP table based on matching rules: Direct match or . → inherit from dp[i-1][j-1]. * → can represent zero or multiple occurrences of the previous character. ⏱ Time Complexity: O(m × n) 📦 Space Complexity: O(m × n) ✨ Key Takeaway: This problem taught me how powerful DP can be in handling complex pattern-based problems, especially when multiple possibilities (like zero or multiple occurrences) are involved. It’s a perfect mix of logic and structure — and very satisfying once it clicks! 🔥 #Day10 #100DaysOfCode #120DaysOfCode #LeetCode #DynamicProgramming #ProblemSolving #CodingChallenge #CPlusPlus #LearningEveryday #KeepCoding

🥷 Day 164 of My 365 LeetCode Challenge is done! 💥 Kicked off strong, solved my problem, and the coding vibe is awesome! 🧠 💡 LeetCode 1625 — Lexicographically Smallest String After Applying Operations Today’s challenge was an interesting blend of string manipulation, BFS traversal, and modular arithmetic — the kind of problem that truly sharpens both your algorithmic thinking and pattern recognition skills. 🧠✨ We’re given a numeric string s and two integers a and b. Two operations can be performed repeatedly in any order: 1️⃣ Add a to all digits at odd indices (with digits wrapping around modulo 10). 2️⃣ Rotate the string to the right by b positions. The goal? 🔍 Find the lexicographically smallest string possible after performing any number of these operations. Sounds simple, right? But here’s the catch — since operations can be performed infinitely, the problem hides a state-space exploration challenge. The same string can appear multiple times via different paths, so blindly iterating could easily lead to infinite loops or redundant computations. ⚙️ Approach & Thought Process: To systematically explore all possible transformations, I used Breadth-First Search (BFS) — perfect for enumerating all reachable states in minimal steps. 🔸 Step 1: Start from the original string and push it into a queue. 🔸 Step 2: For each string, perform both allowed operations: - Add a to digits at odd indices (handling digit wraparound using (digit + a) % 10). - Rotate the string by b positions. 🔸 Step 3: Use a set to track all previously visited strings to prevent cycles. 🔸 Step 4: Continuously compare and update the smallest lexicographic string seen so far. Once the queue is exhausted, the smallest recorded string is our answer ✅ 🔥 Key Learning: This problem elegantly demonstrates how graph traversal (BFS) can be applied beyond traditional graphs — even on state transformations of strings. Recognizing this pattern is a huge step toward mastering advanced algorithmic thinking. ✨ Every problem like this one is a reminder that great coding isn’t about memorizing — it’s about seeing structure where others see chaos. #LeetCode #CPlusPlus #CodingChallenge #ProblemSolving #Algorithms #BFS #SoftwareEngineering #Programming #LearningJourney #CodeNewbie #DataStructures

Day 115/250 🚀 Most beginners fail to solve this simple-looking problem efficiently! Today I solved “Remove Duplicates from Sorted Array” — one of the most popular LeetCode problems that tests your logical thinking and pointer manipulation skills. 💻 🧩 Problem Statement: Given a sorted array, remove all duplicate elements in-place, so that each element appears only once and return the new length of the array. 👉 No extra space allowed. 👉 Must modify the array in O(1) space. At first, it seems tricky — but the trick lies in using two pointers: 🔹 One pointer (x) to track the position of the last unique element. 🔹 Another pointer (i) to scan through the array. Every time we find a new unique element, we move the slow pointer forward and copy that element. In the end, x + 1 gives us the count of unique elements! ✅ Time Complexity: O(n) ✅ Space Complexity: O(1) ✅ Language Used: C++ 🔥 Hashtags #Coding #LeetCode #DSA #ProblemSolving #CPlusPlus #Programming #SoftwareEngineering #CodeNewbie #100DaysOfCode #InterviewPreparation #LearnToCode #TechCommunity #DeveloperJourney #LinkedInCoding #DSAChallenge #StudentsWhoCode #LeetCodeChallenge #ProgrammersLife #CodingJourney #ViralPost

𝐉𝐮𝐬𝐭 𝐥𝐚𝐮𝐧𝐜𝐡𝐞𝐝 𝐦𝐲 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐏𝐥𝐚𝐧𝐭 𝐆𝐫𝐨𝐰𝐭𝐡 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐨𝐫 – 𝐚 𝐉𝐚𝐯𝐚 𝐒𝐰𝐢𝐧𝐠 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐜𝐨𝐦𝐛𝐢𝐧𝐞𝐬 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬. This project features a fully interactive GUI that demonstrates plant growth through seven distinct stages, each teaching principles of sustainable development. The simulation includes visual feedback, consequence mechanics for overwatering, and professional Swing component implementation. The accompanying demonstration video showcases the smooth UI interactions, growth transitions, and educational messaging that make this more than just a coding exercise – it's a thoughtful exploration of balance through object-oriented design. Built with clean architecture, cross-platform compatibility, and educational value in mind. Perfect example of applying core Java skills to create engaging, purpose-driven software. 🔗 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐲: https://lnkd.in/gxt8vxsT #Java #SwingGUI #ObjectOrientedProgramming #SoftwareDevelopment #JavaProjects #EducationalSoftware #Simulation #CodingPortfolio #GUIDesign #SoftwareEngineering #OpenSource #JavaDeveloper #AkshadForChange #SanjivaniUniversity

🚀 Day 66 of #100DaysOfCode Today’s problem was LeetCode 155 — Min Stack 🧠💪 📌 Problem Summary: We need to design a stack that supports: push() pop() top() getMin() (returns minimum element in O(1) time) All operations must be O(1) — that’s the tricky part. ⚡ 📌 Approach: 👉 Use a single stack to store modified values 👉 Track the minimum separately using a variable 👉 When pushing a smaller value, encode it cleverly so you can retrieve the previous min during pop() 💡 Key Insight: When inserting a value smaller than the current minimum: st.push(2*x - min) min = x This way, the stack encodes both data and min info efficiently. 📌 Complexity: ⏱ Time: O(1) for all operations 💾 Space: O(n) ✨ Learning: This is a great example of space optimization and bit manipulation logic — classic low-level trick that feels like magic the first time you see it! ⚙️ Raj Vikramaditya Raghav Garg Nancy Solanki Shweta Arora Harsh Raj Harshita Verma Love Babbar Prince Singh Shivam Mahajan Rohit Negi Neeraj Walia Nishant Chahar Kushal Vijay #LeetCode #100DaysOfCode #CodingChallenge #Cplusplus #DataStructures #LinkedList #ProblemSolving #SoftwareEngineering #Developers #Programming #TechJourney #KeepLearning #DailyCoding #CodeNewbie#LeetCode #100DaysOfCode #Programming #Cplusplus #LinkedList #ProblemSolving #SoftwareEngineering #TechJourney #CodeNewbie #DSA

🚀 Day 5 – LeetCode Problem Solving Journey 💻 Today’s problem was “Remove Element”, an easy-level array problem that focuses on in-place modification — a key concept for memory-efficient programming. Problem Statement: Given an array nums and a value val, remove all occurrences of val in-place and return the number of elements that are not equal to val. The relative order of elements may be changed. Example 1: Input: nums = [3,2,2,3], val = 3 Output: 2, nums = [2,2,_,_] Example 2: Input: nums = [0,1,2,2,3,0,4,2], val = 2 Output: 5, nums = [0,1,4,0,3,_,_,_] 🧠 Concept Used: Two-pointer technique In-place replacement of valid elements Time Complexity: O(n) Space Complexity: O(1) Every problem builds consistency, clarity, and confidence — one step closer to mastering DSA. 💪 #LeetCode #Day5 #ProblemSolving #CodingJourney #100DaysOfCode #Python #DSA #Programming #DevelopersCommunity #SoftwareEngineering #TechLearning #CareerGrowth #ipec #ipec30

Leetcode Daily challenge day 89: Unlocking the power of dynamic programming! 🚀 Just tackled the 'Ones and Zeroes' problem on LeetCode, where the goal is to find the largest subset of binary strings with at most m 0's and n 1's. 💡 Implemented a DP solution that optimizes subset selection, a valuable technique for solving complex problems. Solving Ones and Zeroes: A DP Approach 🚀 1. Problem Breakdown: Given an array of binary strings, find the largest subset with at most *m 0's* and *n 1's*. 2. DP Strategy: Use dynamic programming to track optimal subset sizes with varying counts of 0's and 1's. 3. Key Insight: For each string, decide whether including it exceeds the (m, n) limits or grows the subset size. 4. Implementation: Iterate through strings, updating DP table `dp[i][j]` for max subset size with i 0's and j 1's. 5. Result: `dp[m][n]` gives the largest subset satisfying the constraints! 6. Takeaway: DP shines in combinatorial optimization problems like this! 💡 #LeetCode #DynamicProgramming #CodingChallenge #ProblemSolving #Tech

Day 31 of #100DaysOfCode – Building Logic with Conditions and Loops in JavaScript Today’s session focused on strengthening core programming logic using conditional statements and loops. Key Learnings: Understood how if, else if, and else conditions control program flow. Explored looping structures to execute repetitive tasks efficiently. Learned how combining these concepts helps in building real-world logic and problem-solving patterns. Takeaway: Mastering loops and conditions is the foundation for writing efficient, dynamic, and logical code in JavaScript. Grateful to Harsh Vandana Sharma from Sheryians Coding School and Sheryians Coding School Community for guiding us through today’s session with clear explanations and practical logic-building examples. #100DaysOfCode #JavaScript #WebDevelopment #CodingJourney #Frontend #ProgrammingLogic