LeetCode LIS Problem Solved with Dynamic Programming

🔥 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 3 | DSA + LeetCode Practice 🚀 Continuing my #100DaysOfCode challenge with a focus on understanding problem-solving patterns and thinking step by step. ✅ Problems Solved Today: Leetcode #9 🔹Palindrome Number Problem: Given an integer x, determine whether it is a palindrome. • Methodology Applied: Mathematical reasoning / Two-pointer logic • Approach: Avoided converting the number into a string Reversed half of the number mathematically Compared the reversed half with the remaining part 📌 Key Learning: Breaking the problem into smaller logical steps helps eliminate unnecessary conversions and leads to a more optimized solution. Leetcode #10 🔹 Regular Expression Matching • Methodology Applied: Dynamic Programming • Approach: Used a DP table where dp[i][j] represents whether the first i characters of string s match the first j characters of pattern p Handled two special cases carefully: → matches any single character → matches zero or more of the preceding element Built the solution bottom-up by considering all valid transitions 📌 Key Learning: Once the DP logic is clear, the solution becomes systematic. 📌 Day 3 Takeaway: Easy problems test clarity. Moving to Day 4 🔁 #Day3 #100DaysOfCode #LeetCode #DSA #ProblemSolving #CPlusPlus #BuildInPublic #DynamicProgramming #LearningInPublic 🚀

🔥 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 🚀

🔥 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 🚀

🔥 Day 6 | Round 4 — Building on LIS Concepts! 🔥 Solved a LeetCode problem — Russian Doll Envelopes 💡 This problem combined sorting techniques with the Longest Increasing Subsequence (LIS) concept to achieve an efficient solution. By sorting envelopes smartly and applying the O(n log n) LIS approach, the problem became much more manageable 💪 Great practice for understanding how classic patterns extend to more complex problems 🚀 🔹 Concepts Used: Sorting | Dynamic Programming | Binary Search | LIS 🔹 Key Takeaway: The right preprocessing step can simplify even the toughest problems 🧠 #30DaysOfCode #Round4 #Day6 #LeetCode #DynamicProgramming #LIS #BinarySearch #Sorting #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

#Day8of2026 | LeetCode Practice 📌 Problem: 712 – Minimum ASCII Delete Sum for Two Strings 📚 Concept: Dynamic Programming, Longest Common Subsequence (LCS) Variant, String DP 💡 Intuition: The goal is to make two strings equal by deleting characters with minimum total ASCII cost. Instead of directly simulating deletions, the problem becomes easier when viewed from a keep-maximum perspective. Key observation: - If we retain a common subsequence with the maximum possible ASCII sum, then everything else must be deleted. - So the idea is: Compute the total ASCII sum of both strings. Find the maximum ASCII sum of a common subsequence between the two strings (a weighted LCS). The minimum deletion cost is simply the ASCII sum of characters not part of this common subsequence. This converts a deletion problem into a DP-based optimization problem. 🧠 Key Learning: Classic LCS can be extended by weighting characters instead of counting length. Subtracting 2 × commonAscii accounts for removing non-common characters from both strings. 📎 Sharing how transforming the problem perspective leads to a clean DP solution. https://lnkd.in/gmDh6bNz #LeetCode #DSA #DynamicProgramming #StringDP #Consistency #LearningInPublic #LeetCodeDaily #JavaDSA #ProblemSolving #LeetCodePOTD

One of the best habits I’m currently building is writing pseudocode before writing real code. It forces me to slow down, think clearly, and design the logic first instead of jumping straight into syntax and getting stuck. If you’re new to programming, pseudocode is one of the simplest tools to reduce overwhelm and build real problem-solving skills. Clear thinking first. Clean code later. #OctoPrep #PythonDeveloper #DataScience #DataAnalysis

Building "Spud" | Week 1 Update Here is the first week's progress on my new programming language, Spud. It’s been a busy week of laying the groundwork for the syntax and memory management. New Features: * I/O: Use say to display output and get to receive user input. * Memory Environment: Variable creation is now supported. * Documentation: Comments are wrapped in (~ and ~), affectionately known as "Potato Ears." * Stability: Implemented major error handling to improve the development experience. You can view the source code progress here: https://lnkd.in/eJAHwi8S #ProgrammingLanguage #InterpreterDesign #OpenSource #DevLog #SpudLang

Programming Is Logic, Not Just Syntax By Anuj Prajapati Why decision-making matters more than memorizing rules. Quick question before we start. When your code breaks, what do you usually think first? Is it: “Ugh, I forgot the syntax again?” “Wait… what am I actually trying to make this code do?” Your answer matters more than you think. Most beginners ... https://lnkd.in/eC2Kk37U