LeetCode POTD: Number of Ways to Paint an N×3 Grid

Hello connections, Here is my solution to a LeetCode dynamic programming problem. Time Complexity: O(N) Space Complexity: O(1) Learning from the problem: By observing repeating color patterns, we can reduce the problem to tracking only two states: grids where adjacent columns share the same pattern (ABA) and grids where all three colors are different (ABC). Using simple state transitions, we update these counts row by row, achieving an efficient linear-time solution with constant extra space. This problem highlights how recognizing patterns can significantly simplify complex DP problems. #leetcode #problemsolving #cpp #dynamicprogramming #math #datastructures #happycoding

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 POTD: Maximum Product of Splitted Binary Tree 🌳 Goal: We need to split a binary tree by removing exactly one edge, and then find the maximum product of the sums of the two resulting subtrees. Intuition 💡: At first, I thought, let’s just remove every edge one by one, calculate the sum of both parts, and multiply them to get the product. But this brute-force idea isn’t very intuitive 😅 because it would take O(n²) time. So the real question is… how do we optimize this? Quick Observation 👀: The product becomes maximum when part1_sum ≈ part2_sum So instead of blindly trying all edges, we should aim to split the tree in a way that balances the subtree sums as much as possible. How do we do that? 🤔 First, calculate the total sum of all nodes in the tree Then, for every node, compute the subtree sum The second part will simply be: subtree2 = total_sum - subtree1 Compute the product: prod = subtree1 × subtree2 Keep updating the answer whenever we find a bigger product That’s it! Simple idea, O(n) efficient approach, and a clean tree traversal solution 🌲✨ #trees #subtree #potd #leetcode #programming #problem_solving 

LeetCode is HARD until you Learn these 15 Patterns: 1.Prefix Sum 2. Two Pointers 3.Sliding Window 4. Fast& Slow Pointers 5. LinkedList In-place Reversal 6.Monotonic Stack 7. Top 'K' Elements 8. Overlapping Intervals 9. Modified Binary Search 10.Binary Tree Traversal 11. Depth-First Search (DFS) 12.Breadth-First Search (BFS) 13.Matrix Traversal 14.Backtracking 15. Dynamic Programming Patterns

🚀 LeetCode Daily Practice – Day 43 Problem: Palindrome Partitioning II (Dynamic Programming) Today’s problem focused on splitting a string into the fewest possible palindromic substrings. At first, it feels like brute force — but the right DP structure makes it efficient. 🎯 Core Idea We pre-compute which substrings are palindromes. Then we use DP to determine the minimum cuts needed so every segment becomes a palindrome. For each position, we check all valid palindrome endings and update the minimum partition count. This transforms an exponential backtracking problem into a manageable bottom-up solution. 🔍 Why This Problem Is Interesting It combines two DP layers: palindrome checking and cut optimization. Instead of rechecking every substring repeatedly, we reuse previously computed truth values. 🧠 Key Insight Once we know that certain substrings are palindromes, the problem becomes purely about minimizing boundaries — and DP is perfect for that. ⏱ Time Complexity → O(n²) 🧠 Space Complexity → O(n²) 🌱 Daily Learning Takeaway Precomputation can dramatically simplify problems. Figuring out what to compute ahead of time is often the biggest breakthrough. #leetcode #dsa #dynamicprogramming #palindrome #coding #learningeveryday #growthmindset #developer #problemsolving

LeetCode is HARD until you Learn these 15 Patterns: 1. Prefix Sum 2. Two Pointers 3. Sliding Window 4. Fast & Slow Pointers 5. LinkedList In-place Reversal 6. Monotonic Stack 7. Top ‘K’ Elements 8. Overlapping Intervals 9. Modified Binary Search 10. Binary Tree Traversal 11. Depth-First Search (DFS) 12. Breadth-First Search (BFS) 13. Matrix Traversal 14. Backtracking 15. Dynamic Programming Patterns

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 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

Stop writing mid code. 💀 If you’re still using nested loops for every subarray problem, your runtime is literally crying. We’re moving from O(N^2) stress to O(N) energy with the Two Pointers glow-up. ⚡️ The TL;DR: •Two Pointers: Moving different directions (main character energy). •Sliding Window: For those continuous segments (no gatekeeping). Don't let your complexity be "too much." Check the slides to level up. 🧠 Practice challenge in the last slide! 📌 https://lnkd.in/gE4YJu4J #Coding #SoftwareEngineer #TwoPointers #LeetCode #GlowUp #ProgrammingTips #TechTok

When 10 Lines of Code Become 1 ✨ There’s something satisfying about finding the key to a programming problem 🔑 I was looking at the Nim Game (a classic game theory problem). You could map out every possible move, handle edge cases, and write complex conditional logic. Or… you could find the pattern: n % 4 != 0 Recognizing that one simple mathematical constraint turns an O(n) logic problem into an O(1) math problem. Sometimes, the most effective coding tool isn’t a keyboard — it’s taking a moment to understand the underlying pattern before typing a single character 🧠 #DataStructures #Algorithms #LeetCode #Coding #ProblemSolving #CleanCode