AlgoPush’s Post

On Day 295 of my #300daysofcode challenge, I'm sharing my solution to LeetCode Problem 981, "Time Based Key-Value Store." This problem requires designing a specialized data store for version control. My video provides a clear walkthrough of the optimal design: using a HashMap to quickly find the key and then applying Binary Search on the timestamps to retrieve the correct historical value. This is a valuable skill for advanced data structure implementation and system design interviews. #DataStructures #Algorithms #LeetCode #CodingInterview #Programming #SystemDesign #300daysofcode

🚀 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 164 of My 365 LeetCode Challenge is done! 💥 Kicked off strong, solved my problem, and the coding vibe is awesome! 🧠 💡 LeetCode 1625 — Lexicographically Smallest String After Applying Operations Today’s challenge was an interesting blend of string manipulation, BFS traversal, and modular arithmetic — the kind of problem that truly sharpens both your algorithmic thinking and pattern recognition skills. 🧠✨ We’re given a numeric string s and two integers a and b. Two operations can be performed repeatedly in any order: 1️⃣ Add a to all digits at odd indices (with digits wrapping around modulo 10). 2️⃣ Rotate the string to the right by b positions. The goal? 🔍 Find the lexicographically smallest string possible after performing any number of these operations. Sounds simple, right? But here’s the catch — since operations can be performed infinitely, the problem hides a state-space exploration challenge. The same string can appear multiple times via different paths, so blindly iterating could easily lead to infinite loops or redundant computations. ⚙️ Approach & Thought Process: To systematically explore all possible transformations, I used Breadth-First Search (BFS) — perfect for enumerating all reachable states in minimal steps. 🔸 Step 1: Start from the original string and push it into a queue. 🔸 Step 2: For each string, perform both allowed operations: - Add a to digits at odd indices (handling digit wraparound using (digit + a) % 10). - Rotate the string by b positions. 🔸 Step 3: Use a set to track all previously visited strings to prevent cycles. 🔸 Step 4: Continuously compare and update the smallest lexicographic string seen so far. Once the queue is exhausted, the smallest recorded string is our answer ✅ 🔥 Key Learning: This problem elegantly demonstrates how graph traversal (BFS) can be applied beyond traditional graphs — even on state transformations of strings. Recognizing this pattern is a huge step toward mastering advanced algorithmic thinking. ✨ Every problem like this one is a reminder that great coding isn’t about memorizing — it’s about seeing structure where others see chaos. #LeetCode #CPlusPlus #CodingChallenge #ProblemSolving #Algorithms #BFS #SoftwareEngineering #Programming #LearningJourney #CodeNewbie #DataStructures

🚀 Day 22/30 – 30DaysOfDSAChallenge 🚀 🧠 Topic: Subsets (Power Set Generation) 📘 Concepts Covered: Recursion | Backtracking | Power Set Today I solved the Subsets problem, where the goal is to generate all possible combinations (subsets) of a given array. This problem is a classic example of recursive backtracking and helps in building a solid foundation for combinatorial logic. 🔹 Approach – Recursive Backtracking 💡 Logic: At each recursive step, decide whether to include or exclude the current element. When we reach the end of the array, one subset is complete and added to the result. This method explores 2ⁿ possible subsets efficiently. ⚙️ Time Complexity: O(2ⁿ) ⚙️ Space Complexity: O(2ⁿ * n) (for storing all subsets) ✅ Result: Accepted ✅ | Runtime: 0 ms 🟢 | Beats: 100% 🚀 ✨ Key Takeaway: Recursion becomes powerful when you break a problem into smaller, self-similar parts. Every inclusion or exclusion step leads you toward mastering backtracking and decision trees. #Day22 #30DaysOfDSAChallenge #LeetCode #Recursion #Backtracking #PowerSet #Subsets #ProblemSolving #CPlusPlus #CodingChallenge #DSA #Programming #CodeEveryday #DeveloperJourney #Algorithms

On Day 302 of the coding journey, I'm sharing my solution to LeetCode Problem 528, "Random Pick with Weight." This problem requires designing a data structure for weighted random selection. My video provides a clear walkthrough of the optimal strategy: using a Prefix Sums array to map weights to cumulative ranges, then performing a fast Binary Search to find the corresponding index. This is a valuable skill for system design and advanced data structure implementation. #DataStructures #Algorithms #LeetCode #CodingInterview #Programming #BinarySearch #PrefixSums

🚀 Day 8 of #120DaysOfCode — String to Integer (atoi) 🧩 Today’s challenge was LeetCode Problem 8: “String to Integer (atoi)”, a classic string parsing problem that teaches how to handle input conversion safely and efficiently — something we often take for granted in programming. 🧠 Problem Summary: We need to implement the myAtoi(string s) function that converts a string into a 32-bit signed integer. The function must: Ignore leading whitespaces Handle optional + or - signs Extract the number until a non-digit is found Clamp (limit) the value within the 32-bit integer range Return the final integer result 📘 Example: Input → " -042" Output → -42 ⚙️ Approach I Used: I solved this problem using a step-by-step parsing approach: Trim spaces: Move through the string to skip leading whitespaces. Check the sign: If the next character is '-', mark the sign as negative, otherwise positive. Convert characters to digits: Loop through the characters while they are digits and build the result (result = result * 10 + digit). Handle overflow: Before updating the result, check if adding another digit would exceed the 32-bit integer range ([-2³¹, 2³¹ - 1]). Return the final value: Apply the sign and return the integer. This approach ensures correctness, prevents overflow, and follows a clean linear-time logic with O(n) complexity and O(1) space. 💡 Key Learning: Even a simple-looking task like string-to-integer conversion involves careful consideration of edge cases, boundaries, and input validation — all critical in real-world software systems. 💬 Next Goal: Continue improving my problem-solving skills with more logic-building and string manipulation challenges! #100DaysOfCode #120DaysOfCode #LeetCode #CProgramming #ProblemSolving #CodingChallenge #DeveloperJourney #LearningEveryday

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 

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

🚀 Day 5 – LeetCode Problem Solving Journey 💻 Today’s problem was “Remove Element”, an easy-level array problem that focuses on in-place modification — a key concept for memory-efficient programming. Problem Statement: Given an array nums and a value val, remove all occurrences of val in-place and return the number of elements that are not equal to val. The relative order of elements may be changed. Example 1: Input: nums = [3,2,2,3], val = 3 Output: 2, nums = [2,2,_,_] Example 2: Input: nums = [0,1,2,2,3,0,4,2], val = 2 Output: 5, nums = [0,1,4,0,3,_,_,_] 🧠 Concept Used: Two-pointer technique In-place replacement of valid elements Time Complexity: O(n) Space Complexity: O(1) Every problem builds consistency, clarity, and confidence — one step closer to mastering DSA. 💪 #LeetCode #Day5 #ProblemSolving #CodingJourney #100DaysOfCode #Python #DSA #Programming #DevelopersCommunity #SoftwareEngineering #TechLearning #CareerGrowth #ipec #ipec30

Day 13 of #100DaysOfCode — Completed Today’s focus was on debugging — one of the most essential skills for any developer. Writing code is one thing, but understanding why it doesn’t work as expected is what builds true problem-solving ability. Key Learnings from Day 13: Describing the Problem: The first step to solving an issue is being able to clearly define what’s going wrong. Reproducing the Bug: Some bugs only appear under specific conditions. Learning to recreate them helps in understanding their triggers. Playing Computer: Going through the code line by line as if I were the computer — a powerful method for tracing logic errors. Fixing Errors: Understanding and resolving syntax and runtime errors before execution. Using print() for Debugging: Leveraging print statements to track variable values and expose hidden logic issues during loops or iterations. Using an IDE Debugger: Learning how breakpoints, Step Over, and Step Into can reveal the internal flow of execution and variable states in real time. Reflection: Debugging isn’t just about fixing code — it’s about developing a deeper understanding of how your program actually runs. Today’s session reminded me that careful observation and logical reasoning are just as important as coding itself. Each bug is an opportunity to learn how the computer “thinks.” #100DaysOfCode #Python #Programming #Debugging #CodingJourney