Burst Balloons LeetCode Solution

Hello connections, Here is my solution to a LeetCode dynamic programming problem. Time Complexity: O(N × M) Space Complexity: O(N × M) Learning from the problem: This problem requires carefully handling negative values while ensuring that at least one pair of elements is selected. By using dynamic programming and considering whether to take or skip elements from either array, we can build the maximum dot product step by step. A key insight is extending a subsequence only when it improves the total, otherwise starting fresh with the current pair. This is a great example of how DP helps manage multiple choices and constraints simultaneously. #leetcode #problemsolving #cpp #dynamicprogramming #arrays #datastructures #happycoding

🔥 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 🚀

Hello connections, Here is my solution to a LeetCode problem focused on dynamic programming and string manipulation. Time Complexity: O(N × M) Space Complexity: O(N × M) Learning from the problem: The key idea is to model the problem using dynamic programming, where each state represents the minimum ASCII delete cost needed to make two prefixes of the strings equal. When characters match, no deletion is required; otherwise, we choose the cheaper deletion between the two strings. This approach systematically explores all possibilities and guarantees the optimal result. This problem is a great example of how DP helps balance multiple choices while minimizing cost. #leetcode #problemsolving #cpp #dynamicprogramming #strings #datastructures #happycoding

🚀 LeetCode Progress Update | Dynamic Programming Just solved LeetCode – House Robber using Dynamic Programming 🧠✅ This problem is a great example of how breaking a problem into optimal subproblems leads to an efficient solution. 🔍 Key Learning Points: Defined a clear DP state: dp[i] = maximum money that can be robbed from the first i houses At each step, chose between: Stealing from the current house + dp[i-2] Skipping the current house → dp[i-1] Final transition: dp[i] = max(dp[i-1], nums[i-1] + dp[i-2]) ⚙️ Complexity: Time: O(n) Space: O(n) (can be optimized to O(1)) 💡 This problem strengthened my understanding of: DP base cases State transitions Translating problem constraints into DP logic Consistent practice + clarity in fundamentals is the key 🔑 Onward to the next challenge 💪 #LeetCode #DynamicProgramming #DSA #ProblemSolving #Java #CodingJourney #SoftwareEngineering #LearningByDoing

🚀 Day 186 of #500DaysOfCode Today’s challenge was LeetCode 712 – Minimum ASCII Delete Sum for Two Strings 🧵 This problem was a great example of applying Dynamic Programming to string matching. The goal: make two strings equal with minimum deletion cost, where each deletion costs its ASCII value. 💡 Key insight: Think beyond classic LCS—here, the cost of deletion matters. A DP approach comparing characters and choosing the minimum ASCII sum at each step makes all the difference. Every problem like this sharpens intuition for string DP patterns 🔁 On to the next one! #LeetCode #DSA #DynamicProgramming #Strings #ProblemSolving #CodingJourney #Consistency

🔥 Day 5 | Round 4 — Deep Dive into LIS! 🔥 Solved a classic LeetCode problem — Longest Increasing Subsequence (LIS) 💡 Explored this problem through multiple Dynamic Programming approaches, gradually optimizing from basic recursion to an efficient optimal solution. 🧩 Approaches Covered: Recursive DP (Take / Not Take) DP with Memoization Tabulation (Bottom-Up DP) Space Optimized DP Optimal Approach using Binary Search (O(n log n)) This problem was a great example of how understanding fundamentals helps in reaching optimal solutions 💪 Learning when to optimize is just as important as solving the problem 🚀 🔹 Concepts Used: Dynamic Programming | Binary Search | Optimization 🔹 Key Takeaway: Start simple, then optimize—clarity leads to efficiency 🧠 #30DaysOfCode #Round4 #Day5 #LeetCode #DynamicProgramming #LIS #BinarySearch #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey 🚀

Hi everyone, Do you remember how much time we spent in college just setting up VS Code? Installing extensions, configuring C++/Java, fixing compiler paths — sometimes it took longer than actually writing code. Now, that hassle is gone. In TechieLearns, we’ve built a developer-first, innovative code editor that gives you a VS Code–like experience directly in your browser — no installation, no setup, no configuration issues. Just open and start coding 🚀 ✅ Familiar VS Code–style interface ✅ Supports 30+ programming languages ✅ Powered by the open-source Piston API for code execution ✅ Uses Monaco Editor (the same editor behind VS Code) ✅ Ideal for students, beginners, and quick prototyping Give it a try: 👉 https://lnkd.in/ge9ZejUM

Solved LeetCode 1411: Number of Ways to Paint N×3 Grid 🎨 Used Dynamic Programming with pattern observation to reduce the problem to just 2 states, making the solution efficient even for large n = 5000. ✔️ Time Complexity: O(n) ✔️ Space Complexity: O(1) GitHub Repo:https://lnkd.in/gQq6b_JG Great example of how recognizing patterns can simplify a “Hard” problem. #LeetCode #DSA #DynamicProgramming #ProblemSolving #CodingJourney 🚀

Hi everyone, Do you remember how much time we spent in college just setting up VS Code? Installing extensions, configuring C++/Java, fixing compiler paths — sometimes it took longer than actually writing code. Now, that hassle is gone. In TechieLearns, we’ve built a developer-first, innovative code editor that gives you a VS Code–like experience directly in your browser — no installation, no setup, no configuration issues. Just open and start coding 🚀 ✅ Familiar VS Code–style interface ✅ Supports 30+ programming languages ✅ Powered by the open-source Piston API for code execution ✅ Uses Monaco Editor (the same editor behind VS Code) ✅ Ideal for students, beginners, and quick prototyping Give it a try: 👉 https://lnkd.in/g6U79-zK

Hi everyone, Do you remember how much time we spent in college just setting up VS Code? Installing extensions, configuring C++/Java, fixing compiler paths — sometimes it took longer than actually writing code. Now, that hassle is gone. In TechieLearns, we’ve built a developer-first, innovative code editor that gives you a VS Code–like experience directly in your browser — no installation, no setup, no configuration issues. Just open and start coding 🚀 ✅ Familiar VS Code–style interface ✅ Supports 30+ programming languages ✅ Powered by the open-source Piston API for code execution ✅ Uses Monaco Editor (the same editor behind VS Code) ✅ Ideal for students, beginners, and quick prototyping Give it a try: 👉 https://lnkd.in/g6U79-zK