Solved LeetCode 474: Largest Binary Subset with DP

Revisiting this two-pointer problem on LeetCode. Last time, I didn't pay much attention to the Dynamic Programming solution which is very insightful. I'd recommend considering all known approaches to a solution to understand the topics better. Turns out that the simpler two-pointer solution to this one ran faster for me than the Dynamic Programming solution. 5. Longest Palindrome Substring https://lnkd.in/gX8GRDJV

🔥 Day 19 of LeetCode Challenge – Problem #213: House Robber II Today’s challenge was a dynamic programming problem that extends the classic House Robber concept into a circular arrangement. 💡 Concept Overview: In this variation, the first and last houses are adjacent — which means robbing both is not allowed. The challenge is to maximize profit without triggering alarms in this circular setup. 🧩 Approach Used: Handled base cases for arrays of size 1 and 2. Split the circular problem into two linear subproblems: 1️⃣ Rob houses from index 0 to n-2 2️⃣ Rob houses from index 1 to n-1 Used a helper function with Dynamic Programming (DP) to calculate the maximum money that can be robbed in each case. The final answer is the maximum of both results. ⚙️ Algorithm Used: Dynamic Programming 🧾 Language: Java ⏱ Runtime: 0 ms (beats 100%) 💾 Memory: 42.84 MB 📈 Key Insight: Transforming a circular dependency problem into two linear DP runs simplifies complexity and ensures optimal results. #LeetCodeJourney #100DaysOfCode #CodeNewbie #DeveloperCommunity #JavaDeveloper #CodingPractice #AlgorithmDesign #CareerGrowth #TechInnovation #KeepLearning

🚀 LeetCode POTD — 474. Ones and Zeroes 🎯 Difficulty: Medium | Topics: Dynamic Programming, 0/1 Knapsack 🔗 Solution Link: https://lnkd.in/gscqurzi Today’s #LeetCode Problem of the Day was a classic 0/1 Knapsack variation — and a great reminder of how dynamic programming helps balance multiple constraints efficiently. The task was to find the largest subset of binary strings that can be formed with at most m zeros and n ones. 🧠 My Approach: For each string, count the number of 0s and 1s. Use a 2D DP array (dp[m+1][n+1]), where dp[i][j] represents the maximum subset size possible using i zeros and j ones. Traverse in reverse (to avoid recomputation) and update using: dp[i][j] = max(dp[i][j], dp[i - zeros][j - ones] + 1); The final answer is dp[m][n]. 📈 Complexity: Time: O(len(strs) × m × n) Space: O(m × n) 💡 Takeaway: Dynamic Programming is all about breaking problems into smaller overlapping subproblems — once you see that, even “complex” constraints start falling into place logically. #LeetCode #ProblemOfTheDay #DynamicProgramming #Knapsack #DSA #CodingChallenge #ProblemSolving #C++ #SoftwareEngineering #CodingJourney #100DaysOfCode #LearningInPublic #TechCommunity

🚀 Day 17 of #100DaysofCode Challenge! 🚀 🔍 What is Dynamic Programming (DP), and why does it matter? I recently watched Aditya Verma’s introductory video on DP and here are the highlights: • DP is an optimization over naïve recursion — it’s about identifying when you’re solving the same sub-problem repeatedly, and instead reuse those results (overlapping sub-problems + optimal substructure). • The typical workflow: start with a recursive solution, notice repeated work → switch to memoization (top-down) → then possibly convert to tabulation (bottom-up). • The key mindset shift: Think in terms of subproblem states and transitions/choices — that’s what lets you build from smaller results to the full solution. • Use DP when brute force recursion is too slow and when the problem naturally splits into smaller pieces whose optimal solutions combine. • Base cases, initialization, and correct ordering (especially in bottom-up) are crucial — you can’t just “DP-ify” any recursion without thinking about these. #Day17 #DynamicProgramming #Algorithms #ProblemSolving #CodingMindset #SoftwareEngineering #AdityaVerma #CodingJourney

Day 45 of LeetCode grind — “Make Array Elements Equal to Zero” (Problem #3354) This one looked like a simulation problem at first glance, but it’s actually an elegant math trick hiding under a messy description. Instead of simulating all that left-right bouncing chaos, you just compare prefix sums — figuring out where the array can be “balanced” around a zero. Key takeaway: Sometimes the shortest code comes from refusing to believe the problem is as complicated as it sounds. ✅ Accepted 🧠 Concepts: Prefix Sum, Balance Points 💬 Lesson: Elegant logic > brute force chaos Slowly crawling my way through 100 days of consistency — still standing, still debugging. #LeetCode #100DaysOfCode #ProblemSolving #Cplusplus #CodingJourney

Ideas for a programming language, like Rust but a scope transient thing, like set a memory location at the thread level or global only once and have many things allowed to access, like not creating references and copying, just access the different scopes of originals and scope delete like Rust when the scope ends. Also like accessing more to the outer and inner. I at least want reusable code like a function that does not create a reference every call. Like if you were to make a monolith function that only uses inside variables only but with functions and reuse. What do you think? Still memory safe if done right. X E.

Leetcode Daily challenge day 89: Unlocking the power of dynamic programming! 🚀 Just tackled the 'Ones and Zeroes' problem on LeetCode, where the goal is to find the largest subset of binary strings with at most m 0's and n 1's. 💡 Implemented a DP solution that optimizes subset selection, a valuable technique for solving complex problems. Solving Ones and Zeroes: A DP Approach 🚀 1. Problem Breakdown: Given an array of binary strings, find the largest subset with at most *m 0's* and *n 1's*. 2. DP Strategy: Use dynamic programming to track optimal subset sizes with varying counts of 0's and 1's. 3. Key Insight: For each string, decide whether including it exceeds the (m, n) limits or grows the subset size. 4. Implementation: Iterate through strings, updating DP table `dp[i][j]` for max subset size with i 0's and j 1's. 5. Result: `dp[m][n]` gives the largest subset satisfying the constraints! 6. Takeaway: DP shines in combinatorial optimization problems like this! 💡 #LeetCode #DynamicProgramming #CodingChallenge #ProblemSolving #Tech

Every great programmer knows that coding isn’t just about syntax - it’s about logic, problem-solving, and efficiency. These 5 essential algorithms will help you think smarter, code faster, and build a strong foundation for any programming challenge.👨🏽💻👍🏼 #programmingbasics #codingskills #learntocode #algorithms #techeducation #problemsolving #programmingtips #logicbuilding #codingwithSCOPE #scopesumago 

Hot take: Using stochastic LLMs to write imperative, step-by-step code is like using a probability engine to calculate 2+2. Sure, it works... but are we applying the wrong tool to the problem, or are we still thinking about programming in outdated paradigms?