LeetCode 712: Minimum ASCII Delete Sum for Two Strings

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 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 18 | Round 4 — Tackling Pattern Matching with DP! 🔥 Solved a LeetCode problem — Wildcard Matching 💡 This problem revolves around matching a string with a pattern containing wildcards using Dynamic Programming. 🧩 Approaches Explored: Recursive DP (Brute Force Idea) DP with Memoization Bottom-Up Tabulation Space Optimized DP Handling special characters like * and ? required careful state transitions, making this a great exercise in string DP 💪 Gradually, these complex DP patterns are starting to feel more intuitive 🚀 🔹 Concepts Used: Dynamic Programming | String DP | Pattern Matching 🔹 Key Takeaway: Defining clear base cases is crucial when dealing with complex matching rules 🧠 #30DaysOfCode #Round4 #Day18 #LeetCode #DynamicProgramming #WildcardMatching #StringDP #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 🚀

34). 😁 🚀 LeetCode #3 – Longest Substring Without Repeating Characters | Sliding Window | C++ Solved LeetCode Problem 3, which focuses on string processing using the sliding window technique. 🔍 Problem Overview Given a string s, the task is to find the length of the longest substring without repeating characters. 📌 Example Input: s = "abcabcbb" Output: 3 (The substring "abc" has all unique characters) Two pointers, left and right, define the current window, while an unordered_set keeps track of the characters present in that window.  As the right pointer moves forward through the string, the code checks whether the current character already exists in the set. If it does, the window is shrunk from the left by removing characters until the duplicate is eliminated.  Once the window contains only unique characters, the current character is inserted into the set, and maxlength is updated with the maximum window size seen so far. -> After the loop completes, maxlength represents the length of the longest substring without repeated characters. ⚙️ Complexity Analysis ⏱ Time Complexity: O(n) 💾 Space Complexity: O(min(n, charset)) 📌 Language: C++ 📌 Platform: LeetCode #LeetCode #CPlusPlus #DSA #SlidingWindow #Strings #Hashing #TwoPointers #ProblemSolving #CodingPractice #CompetitiveProgramming #SoftwareEngineering #ComputerScience #CSStudents #BCA #BCAStudents #InterviewPreparation #DailyCoding #LearningToCode #DeveloperJourney #TechJourney #Consistency #CodingLife

Day 4 – LeetCode 643 | Maximum Average Subarray I | Sliding Window Description: Day 4 of my LeetCode consistency challenge. Solved LeetCode 643: Maximum Average Subarray I using the Sliding Window technique. Instead of recalculating sums for every subarray (which is inefficient), I maintained a fixed-size window of length k, updated the sum in constant time, and tracked the maximum average. This reduces the time complexity to O(n) with O(1) extra space. Key takeaways: • Why sliding window is optimal for fixed-length subarray problems • How to avoid unnecessary recomputation • Clean handling of averages without precision issues Consistency > Motivation. On to Day 5. Tags: #LeetCode #Day4 #SlidingWindow #DataStructures #Algorithms #Java #ProblemSolving #CodingChallenge #SoftwareEngineering

Day 59 of #100DaysOfLeetCode Today’s problem was a solid test of dynamic programming, state transitions, and pattern counting — one of those problems where understanding the pattern matters more than brute force. 🟦 Number of Ways to Paint N × 3 Grid (LeetCode 1411) The challenge was to calculate how many ways we can paint an N × 3 grid using three colors, ensuring that: No two adjacent cells (horizontally or vertically) have the same color The result must be computed modulo 10⁹ + 7 🧠 My Approach Instead of tracking every possible coloring, I reduced the problem to two repeating patterns: Type A: All three columns have different colors Type B: Two columns share the same pattern but still respect constraints Using these two states, I applied DP with constant space, updating counts row by row using recurrence relations. This turned an exponential problem into a linear-time solution. 💡 What I Learned Pattern recognition simplifies complex DP problems Reducing states is often the key to efficiency Mathematical recurrence > brute force simulation 📊 Complexity Analysis Time Complexity: O(n) Space Complexity: O(1) ✅ Day 59 Summary A classic DP problem that rewards thinking in patterns rather than configurations. Challenging, elegant, and satisfying to solve. Onward to Day 60. Halfway momentum still strong. 🚀 #100DaysOfLeetCode #Day59 #LeetCode #HardProblem #DynamicProgramming #DP #Math #Optimization #Python #DSA #ProblemSolving #CodingJourney #AdityaCodes

#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

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