Russian Doll Envelopes LeetCode Solution

🔥 Day 8 | Round 4 — Learning Through Optimization! 🔥 Solved a LeetCode problem — Longest Arithmetic Subsequence 💡 This problem focuses on identifying the longest subsequence with a constant difference using Dynamic Programming. 🧩 Approaches Explored: Recursive DP with Memoization ➤ Worked for most cases but resulted in TLE on one test case due to high recursive overhead. Bottom-Up Tabulation using Hash Maps ➤ Efficiently stored differences and avoided repeated computations. Switching from memoization to tabulation helped optimize performance and pass all test cases 💪 A great reminder that the right approach matters as much as the logic 🚀 🔹 Concepts Used: Dynamic Programming | Hash Maps | Tabulation 🔹 Key Takeaway: When recursion hits limits, iterative DP often provides the needed performance boost 🧠 #30DaysOfCode #Round4 #Day8 #LeetCode #DynamicProgramming #Tabulation #DPOptimization #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey 🚀

🔥 Day 9 | Round 4 — Applying LIS in 3D! 🔥 Solved a LeetCode problem — Maximum Height by Stacking Cuboids 💡 This problem extends the Longest Increasing Subsequence (LIS) concept to three dimensions by combining sorting with Dynamic Programming. By sorting each cuboid and then applying DP on valid stacking conditions, the optimal maximum height can be calculated efficiently 💪 Great practice for understanding how classic DP patterns evolve in multidimensional problems 🚀 🔹 Concepts Used: Dynamic Programming | LIS | Sorting 🔹 Key Takeaway: Proper preprocessing transforms complex constraints into simple DP transitions 🧠 #30DaysOfCode #Round4 #Day9 #LeetCode #DynamicProgramming #LIS #Sorting #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey

🔥 Day 19 | Round 4 — Revisiting DP Basics! 🔥 Solved a LeetCode problem — Climbing Stairs 💡 This is a classic Dynamic Programming problem that introduces the idea of breaking a problem into overlapping subproblems. By using recursion with memoization, the solution efficiently avoids repeated calculations and builds up the final answer 💪 Simple problems like this strengthen the foundation of DP thinking 🚀 🔹 Concepts Used: Dynamic Programming | Memoization | Recursion 🔹 Key Takeaway: Many DP problems follow simple recurrence relations once the base cases are clear 🧠 #30DaysOfCode #Round4 #Day19 #LeetCode #DynamicProgramming #ClimbingStairs #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey 🚀

The art of breaking things down. 🧩 The hardest part of coding isn't typing the syntax; it's learning how to think. This graphic sums it up perfectly. Whether you are building a complex backend system or just making morning tea, the logic remains the same: Gather inputs → Validate conditions → Execute the plan. If you can learn to break down problems like this, you can master any programming language. Happy coding! 🚀 #ProblemSolving #DeveloperMindset #CodingTips #LogicBuilding #TechCommunity

🔥 Day 14 | Round 4 — Exploring Interval DP Further! 🔥 Solved a LeetCode problem — Minimum Cost Tree From Leaf Values 💡 This problem is another great example of interval-based Dynamic Programming, where the goal is to minimize the total cost by choosing optimal partition points. By recursively dividing the array and combining results using memoization, the solution efficiently avoids repeated calculations 💪 Problems like these help build a strong intuition for range-based DP decisions 🚀 🔹 Concepts Used: Dynamic Programming | Interval DP | Memoization 🔹 Key Takeaway: Breaking a problem into optimal subranges simplifies complex decision-making 🧠 #30DaysOfCode #Round4 #Day14 #LeetCode #DynamicProgramming #IntervalDP #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey 🚀

#43 🚀 LeetCode Problem Solved: Reverse Only Letters 🚀 I recently solved the “Reverse Only Letters” problem on LeetCode using a two-pointer approach in C++. 🧠 Key idea: Use two pointers (i from start, j from end) Swap characters only if both are letters Skip non-letter characters without disturbing their positions This problem was a great practice for: String manipulation Character validation Two-pointer technique 🔍 What I learned: Writing clean conditional logic matters a lot Edge cases (non-alphabetic characters) are crucial Two pointers can significantly simplify string problems 💡 Always enjoying these small challenges that strengthen problem-solving skills step by step. #LeetCode #DSA #CPlusPlus #ProblemSolving #TwoPointers #CodingPractice #LearningEveryday

#Day12of2026 | LeetCode Practice 📌 Problem: 198 – House Robber 🔗 Link : https://lnkd.in/gbm_D9Yh 📚 Concept: Dynamic Programming, Recurrence Relation, Space Optimization 💡 Intuition: At every house, you face a simple decision: rob it or skip it. However, if you rob the current house, you cannot rob the adjacent one, which introduces overlapping subproblems. The core idea is to define a DP state where each index represents the maximum money that can be robbed up to that house. This leads to a classic recurrence: Either skip the current house and keep the previous maximum Or rob the current house and add it to the result from two houses back The problem naturally evolves from: 1. Memoization (top-down) → clear recursion 2. Tabulation (bottom-up) → iterative DP 3. Space optimization → keeping only the last two states 🧠 Key Learning: - Many DP problems reduce to a clear pick vs not-pick decision at each index. - Once the recurrence is identified, DP solutions can often be optimized from O(n) space to O(1). 📎 Sharing how progressing from memoization to space-optimized DP leads to a clean and efficient solution. https://lnkd.in/gK5Gkhh6 #LeetCode #DSA #DynamicProgramming #Consistency #LearningInPublic #LeetCodeDaily #JavaDSA

🚀 LeetCode Daily Practice – Day 41 Problem: Burst Balloons (Dynamic Programming) Today’s problem was one of those DP challenges where greedy strategies completely fail. The goal is to burst balloons in an order that maximizes the coins collected — but every burst changes the values of its neighbors. 🎯 Core Idea Instead of thinking forward (which balloon to burst next), the trick is to think backwards: Assume every balloon is the last one burst in its interval. By fixing the last burst balloon, the interval splits into two independent subproblems, making DP possible. This converts a chaotic optimization problem into a carefully structured interval DP. 🔍 Why This Problem Is Interesting The order of operations changes everything. Bursting what “looks best now” is often wrong, because later balloons depend on it. DP forces us to evaluate all possibilities without recomputing them, revealing the true optimal order. 🧠 Key Insight Some problems only make sense when viewed from the end instead of the beginning. Interval DP is powerful when decisions inside a range depend on its boundaries. ⏱ Time Complexity → O(n³) 🧠 Space Complexity → O(n²) 🌱 Daily Learning Takeaway When local choices mislead, step back and ask: “What if I decide the final outcome first?” Dynamic programming often starts where intuition ends. #leetcode #dsa #dynamicprogramming #intervaldp #coding #learningeveryday #growthmindset #developer #problemsolving

Logic 🧠 A = Milk 🥛 B = Water 💧 C = Empty 🫙 Step 1️⃣ : A → C Step 2️⃣ : B → A Step 3️⃣ : C → B Swap Successful 🔁 Agar aise easy logic se programming sikhni hai 💻 👉 Join Red & White Skill Education Official ❤️🤍 #ProgrammingLogic #SwapLogic #CProgramming #CodingBasics #LogicBuilding #LearnCoding #ProgrammingReels #StudentLife #RedAndWhite #SkillEducation #FutureProgrammer #TechStudents