Solved LeetCode POTD 1611 with Mazhar Imam Khan's insight

🚀 Day 19/50 | LeetCode Coding Challenge Today's problem: Maximum Frequency After Operations - A challenging medium-level problem that tested my optimization skills! 💡 The Challenge: Given an array and k operations, we can add values in range [-k, k] to selected indices. The goal? Maximize the frequency of any element. 🎯 Key Learnings: 1️⃣ Binary Search Optimization: Replaced O(n²) nested loops with binary search using lower_bound/upper_bound - reduced complexity from O(n²) to O(n log n) 2️⃣ Two-Pointer Technique: Implemented sliding window to efficiently find valid ranges where elements can be transformed 3️⃣ Integer Overflow Awareness: Used long long for calculations with values up to 10⁹ to prevent overflow errors 4️⃣ Multiple Strategies: Combined two approaches: Keeping existing values unchanged Transforming all elements to a new target value ⚡ The Result: ✅ 633/633 test cases passed ✅ Time complexity: O(n log n) ✅ Optimized from TLE to accepted solution 🔥 Biggest Takeaway: When you hit Time Limit Exceeded, don't give up! Analyze your bottlenecks: Identify nested loops → Consider binary search or two pointers Watch for repeated computations → Use preprocessing Always handle edge cases (overflow, boundary conditions) The journey from brute force to optimal solution taught me more than just getting it right the first time ever could. Day 19 ✅ | 31 more days to go 💪 Shishir chaurasiya and PrepInsta #100DaysOfCode #CodingChallenge #LeetCode #DSA #ProblemSolving #SoftwareEngineering #CPlusPlus #Algorithms #TechJourney #LearningInPublic

Tackling a Quick LeetCode Challenge: Final Value of Variable After Performing Operations (2011) It's amazing how a simple iteration can solve a coding problem! This one is a perfect example of keeping it simple: tracking a variable's state change based on a list of defined operations. The Problem: We start with x=0 and are given an array of string operations (like ++X, X++, --X, X--). We need to calculate the final value of x. My C++ Approach: I noticed that regardless of whether the increment/decrement is pre (++X, −−X) or post (X++, X−−), the effect is the same for the final value. Therefore, I only need to check what the operation is, not where the X is. I iterate through the operations array. Inside the loop, I check if the string contains two plus signs ("++"). An even simpler check, as shown in the code, is checking if the operation string equals "++X" or "X++" or even just checking for the presence of a '+'. If it's an increment operation, I use x++. If it's a decrement operation, I use x--. A neat little problem to practice basic array iteration and conditionals! Full code is accepted and running in the screenshot. Have you solved this one? What was your approach? #DataStructures #Algorithms #CodingInterviewPrep #SoftwareDevelopment #DailyCoding #C++

🚀 LeetCode 75: A Must-Solve for Every Aspiring Developer   Just wrapped up a deep dive into the LeetCode 75 curated list—and here’s why it’s a game-changer: 🔹 Structured Progression: Starts with easy problems, gradually ramps up to medium and hard—perfect for building confidence and skill.   🔹 Real-World Relevance: Covers core topics like arrays, strings, linked lists, trees, graphs, and dynamic programming—exactly what top tech interviews demand.   🔹 Pattern Recognition: Solving these helps you spot recurring logic patterns—sliding window, two pointers, BFS/DFS, etc.   🔹 Time Complexity Mastery: Forces you to think in terms of optimization, not just brute force.   🔹 Revision Goldmine: Ideal for creating flashcards, cheat sheets, and revision grids (which I love building 😄).   🔹 Confidence Booster: Completing this list feels like leveling up your problem-solving superpowers. 💡 Tip: Don’t just solve—analyze, refactor, and document your learnings. LeetCode75 #DSA #CodingJourney #TechInterviewPrep #LinkedInLearning #Problem.

🧠 Thought process while solving LeetCode: 1️⃣ “Easy problem” — this should be quick. 2️⃣ 45 minutes later — searching “two sum solution explained like I’m 5.” 3️⃣ “Ohhh, now it makes sense!” Every problem is a reminder that progress in tech isn’t always linear — it’s built on patience, analysis, and reflection. Through consistent LeetCode practice, I’ve learned that: : Debugging teaches discipline. Understanding why a solution works is more valuable than just getting the right output. Each challenge strengthens structured thinking — an essential skill in real-world problem-solving. #LeetCode #ProblemSolving #LearningMindset #SoftwareEngineering #ContinuousLearning

Day 26 of #50DaysLeetCodeChallenge 🧩 Problem: 561. Array Partition Today’s problem was a refreshing one — an Easy-level LeetCode challenge that reinforces how sorting and pairing can optimize outcomes. It’s one of those problems that looks simple but beautifully demonstrates the power of greedy algorithms. 🧠 Thought Process: 🔹 The task is to group integers into pairs such that the sum of the smaller element in each pair is maximized. 🔹 Sorting the array ensures each pair is as balanced as possible. 🔹 Once sorted, we simply take every alternate element (starting from index 0), since that will always represent the smaller element in each optimal pair. 🔹 This method eliminates unnecessary complexity and ensures maximum total sum. ⚙️ My Approach: 🔸 Sort the array using Arrays.sort(nums). 🔸 Initialize sum = 0. 🔸 Loop through the array with a step of 2, adding nums[i] to the sum each time. 🔸 Return the final sum — the maximum possible result. 📈 Performance: ✅ Accepted ⚡ Runtime: 17 ms (Beats 13.97%) 💾 Memory: Efficient and clean implementation 💡 Reflection: Sometimes, simplicity is the key to optimization. Even without complex data structures, understanding the problem’s core logic can lead to elegant solutions. Each day’s challenge sharpens both reasoning and coding clarity. Grateful to Trainer Shishir chaurasiya and PrepInsta for guiding me through consistent, concept-driven learning in DSA 🙏 #LeetCode #50DaysLeetCodeChallenge #ProblemSolving #Java #AlgorithmDesign #CodingChallenge #PrepInsta #DeveloperJourney #LogicBuilding #DSA #CleanCode #LearningEveryday

💻 Day 18 of My LeetCode Journey 🚀 Problem: LeetCode 2 — Add Two Numbers (Medium) 📘 Problem Overview: You are given two non-empty linked lists representing two non-negative integers. Each node in the lists contains a single digit, and the digits are stored in reverse order — meaning the 1’s place is at the head of the list. Your task is to add the two numbers and return the sum as a new linked list, also in reverse order. 💡 Key Learnings and Algorithmic Insights: 🔹 Core Concept: This problem is an excellent exercise in linked list traversal, digit-by-digit addition, and carry management, simulating manual addition in code form. 🔹 Approach: 1️⃣ Initialize a dummy node to construct the result list. 2️⃣ Use two pointers to traverse both linked lists simultaneously. 3️⃣ Maintain a carry variable to handle sums that exceed 9. 4️⃣ Continue until all digits and any remaining carry are processed. 🔹 Time Complexity: O(max(m, n)) — both lists are traversed once. 🔹 Space Complexity: O(max(m, n)) — for the resulting linked list. 🎯 Key Takeaway: This problem elegantly demonstrates how data structures can simulate real-world operations. It emphasizes careful pointer manipulation, iteration logic, and understanding of how information flows across linked nodes. Each problem in this journey reinforces the importance of clean logic, structured thinking, and consistency in learning. On to Day 19 💡 #LeetCode #Day18 #CodingChallenge #ProblemSolving #LinkedList #DSA #AlgorithmDesign #SoftwareEngineering #LearningEveryday #100DaysOfCode

🚀 LeetCode Daily Challenge – Day 51 🧩 Problem: LeetCode 3289 – The Two Sneaky Numbers of Digitville 🔗 Problem Link: https://lnkd.in/gsaJMm8J 🧠 Intuition: Today’s challenge takes us into the realm of frequency counting and pattern detection 🔢🔍. We’re asked to find the “sneaky” numbers — the ones that appear more than once in a given array. It’s a simple yet elegant problem that highlights how crucial data tracking is — whether it’s in code or in real-world analytics. ⚙️ Approach (Counting Technique): 1️⃣ Initialize a frequency array count[] to keep track of occurrences. 2️⃣ Traverse through the input nums — increment the count for each number. 3️⃣ After the pass, any number with a count greater than 1 is a sneaky number 👀. 4️⃣ Collect these numbers and return them. 💡 Key Insight: This problem emphasizes the importance of frequency mapping — a foundational concept for problems like duplicates, anagrams, and data analysis. Sometimes, solving efficiently is about counting smartly rather than searching repeatedly. ⏱️ Time Complexity: O(n) 💾 Space Complexity: O(1) (since the number range is fixed to <100) 📈 Learning from Day 51: Even the simplest algorithms teach big lessons — tracking, observation, and attention to detail. Whether it’s arrays, logs, or life — what you measure, you can master. 📊✨ 🌟 Motivation: Don’t underestimate the power of small problems — they build the intuition that drives big solutions. Keep counting your progress, not your setbacks. Every line of code adds up. 💪💻 #️⃣ Hashtags: #Day51 #LeetCode #CodingChallenge #Java #DSA #ProblemSolving #FrequencyCount #LogicBuilding #100DaysOfCode #Consistency #GrowthMindset #KeepCoding #LearnByDoing

💻 Day 17 of My LeetCode Journey ✨ Problem: LeetCode 5 Longest Palindromic Substring (Medium) 📘 Problem Overview: Given a string s, the task is to return the longest palindromic substring in s. A palindrome is a string that reads the same forward and backward. For example: Input: "babad" → Output: "bab" (or "aba") Input: "cbbd" → Output: "bb" 💡 Key Learnings and Algorithmic Insights: 🔹 Core Challenge: Finding the longest substring that remains the same when reversed efficiently, without checking every possible substring (which would take O(n³) time). 🔹 Approach 1 – Expand Around Center (Optimized O(n²)): Treat each character (and gap between characters) as the center of a potential palindrome. Expand outwards while the left and right characters match. Track the longest substring found during expansion. 🔹 Approach 2 – Dynamic Programming: Build a table dp[i][j] that stores whether the substring s[i..j] is a palindrome. Use previous computed states to identify longer palindromes. Great for understanding the recurrence logic in string problems. 🔹 Bonus Insight: For large strings (thousands of characters), the center expansion approach outperforms DP in both memory and simplicity. 🧠 Complexity: Time: O(n²) Space: O(1) for center expansion or O(n²) for DP 🎯 Key Takeaway: This problem beautifully demonstrates how symmetry and pattern recognition can simplify complex problems. Understanding palindrome properties and optimizing expansions around potential centers reflects how logical insights can outperform brute force computation. Each problem teaches not just coding techniques but also how to think systematically about structure, repetition, and optimization. On to Day 18 🚀 #LeetCode #Day17 #CodingChallenge #ProblemSolving #DSA #StringAlgorithms #DynamicProgramming #Optimization #SoftwareEngineering #LearningEveryday

I recently tackled LeetCode 2654 – Minimum Number of Operations to Make All Array Elements Equal to 1. Most solutions simulate every operation. The approach that worked for me came from stepping back and thinking mathematically: 1️⃣ If there’s already a 1 in the array, it can be spread to all other elements efficiently. 2️⃣ If no 1 exists, find the shortest subarray whose GCD is 1 – creating a 1 from that subarray is the minimal first step. 3️⃣ Once a 1 exists, the rest follows in a straightforward way. It’s a reminder that sometimes understanding the structure of a problem beats brute-force coding. And here’s a little validation: ✅ Runtime: 1ms, beats 100% of submissions ✅ Memory: 56.1MB, beats 100% of submissions For anyone doing coding challenges or preparing for interviews, focus on insights like these – they often save more time than writing extra lines of code. #ProblemSolving #Algorithms #CodingMindset #LeetCode #SoftwareEngineering

#digitaltechnology #code #logic #objectorientedprogramming #oop #reallife #ides #assembler #compiler #interpreters #action #calling #systems #stewardship #purpose Code is not just logic... it’s a dynamic fusion of logic, language, and design. While logic forms the backbone, enabling a program to make decisions and follow structured steps, coding also demands fluency in syntax, creativity in problem-solving, and clarity in communication. Developers must not only think algorithmically but also write readable, maintainable code that interacts with users, systems, and other developers. They navigate tools, frameworks, and evolving requirements, often balancing efficiency with elegance. In essence, coding is as much about crafting thoughtful, adaptable solutions as it is about executing logical instructions.