Optimizing LeetCode's Longest Arithmetic Subsequence with Tabulation

🔥 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 🚀

#Day25of2026 | LeetCode Practice 📌 Problem: 416 – Partition Equal Subset Sum 🔗 Link : https://lnkd.in/giWhgucS 📚 Concept: Dynamic Programming, Subset Sum, 0/1 Knapsack 💡 Intuition: The array can be partitioned into two equal subsets only if the total sum is even. So the problem reduces to a classic question: 👉 Is there a subset whose sum equals totalSum / 2? This is exactly the Subset Sum problem. For each element, we have two choices: Take it into the subset Do not take it Using DP, we track whether a particular sum is achievable using the first i elements. The transition is straightforward: Either we already had the sum without the current element Or we include the current element and check if the remaining sum was achievable earlier Both memoization (top-down) and tabulation (bottom-up) efficiently solve the problem by avoiding repeated work. 🧠 Key Learning: Many partition problems reduce directly to the Subset Sum DP pattern. Early checks (like total sum being odd) can eliminate unnecessary DP computation. 📎 Sharing how recognizing the subset-sum structure simplifies the problem into a clean DP solution. https://lnkd.in/gwDn49KX #LeetCode #DSA #DynamicProgramming #SubsetSum #Consistency #LearningInPublic #LeetCodeDaily #JavaDSA

#Day18of2026 | LeetCode Practice 📌 Problem: 746 – Min Cost Climbing Stairs 🔗 Link : https://lnkd.in/gsMt554p 📚 Concept: Dynamic Programming, Recursion → Memoization → Tabulation 💡 Intuition: At each step, you can climb either 1 or 2 stairs, and you must pay the cost of the step you land on. - So at any index i, the minimum cost to reach there depends on the minimum cost to reach the previous one or two steps. This naturally forms a DP relation: - Either come from i-1 or i-2, choosing the cheaper path - The final answer is the minimum cost to reach either of the last two steps - Starting with recursion helps understand the decision-making, but it has overlapping subproblems. - Optimizing it using memoization and then tabulation gives an efficient bottom-up solution. 🧠 Key Learning: Many staircase problems follow a clear “1-step or 2-step choice” DP pattern. Once the recurrence is clear, recursion can be systematically optimized into an iterative DP solution. 📎 Sharing how evolving from recursion to tabulation makes the solution efficient . https://lnkd.in/gJ4PnpEF #LeetCode #DSA #DynamicProgramming #Consistency #LearningInPublic #LeetCodeDaily #JavaDSA

🔥 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 17 | Round 4 — Mastering String DP! 🔥 Solved a LeetCode problem — Edit Distance 💡 This classic string-based Dynamic Programming problem focuses on converting one string into another using the minimum number of operations. 🧩 Approaches Explored: Recursive DP (Brute Force Idea) DP with Memoization Bottom-Up Tabulation Space Optimized DP (1D Arrays) By understanding insert, delete, and replace operations clearly, optimizing the solution became much more intuitive 💪 Problems like these greatly strengthen core DP concepts 🚀 🔹 Concepts Used: Dynamic Programming | String DP | Space Optimization 🔹 Key Takeaway: Clear transitions between states make even complex string problems manageable 🧠 #30DaysOfCode #Round4 #Day17 #LeetCode #DynamicProgramming #EditDistance #StringDP #DSA #ProblemSolving #CodingChallenge #DeveloperJourney #CodeEveryday #CPlusPlus #LearnByDoing #ConsistencyIsKey 🚀

🗓 Day 87 / 100 – #100DaysOfLeetCode 📌 Problem 44: Wildcard Matching Today’s problem was a classic dynamic programming challenge involving string pattern matching. The task was to determine whether a string matches a pattern containing: ? → matches any single character * → matches any sequence of characters (including empty) 🧠 My Approach: Used Dynamic Programming where: dp[i][j] represents whether the first i characters of the string match the first j characters of the pattern Handled base cases carefully: Empty string vs pattern Patterns starting with * Transition rules: If characters match or pattern has ? → inherit diagonal state If pattern has * → either: match zero characters or match one/more characters Final answer comes from dp[n][m]. This approach ensures all wildcard possibilities are handled correctly. 💡 Key Learning: This problem reinforced: ✔ thinking in terms of states and transitions ✔ handling multiple matching paths with DP ✔ importance of correct base-case initialization Wildcard matching problems are great practice for mastering string DP patterns. Day 87 done — almost at the finish line 🚀 #100DaysOfLeetCode #LeetCodeChallenge #ProblemSolving #DynamicProgramming #StringMatching #DP #Algorithms #DSA #CompetitiveProgramming #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

🔥 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

🚀 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

📘 Problem Solving Update – Today’s Practice Had a productive session solving two solid problems focused on sliding window and dynamic programming. ✅ GFG POTD – Substrings with K Distinct Characters Approach: Used the classic “at most K” sliding window technique. The idea is to count substrings with at most K distinct characters and subtract substrings with at most (K−1) distinct characters. This converts the problem into an efficient linear-time solution using two pointers and a frequency tracker, instead of brute force. Key learnings: Two pointer window expansion and contraction Frequency tracking for distinct characters Turning an “exactly K” problem into a difference of two simpler problems ✅ LeetCode Daily – Minimum ASCII Delete Sum for Two Strings Approach: Solved using Dynamic Programming with Space Optimization. The goal is to make both strings equal by deleting characters with minimum ASCII cost. Defined DP states based on suffixes of the strings, where each state represents the minimum cost needed from that point onward. Optimized from 2D DP to 1D using only previous and current rows. Key learnings: DP on strings Transition based on character match vs delete choices Space optimization without losing clarity Consistent practice, deeper understanding. On to the next one 🚀 #ProblemSolving #DSA #LeetCode #GeeksForGeeks