Competitive Programming Struggle: Optimizing the Wrong Thing

Mastering array manipulation is fundamental for any developer. The seemingly simple task of finding the largest element in an array presents an excellent opportunity to optimize for efficiency. While sorting the array (O(n log n) time complexity) is one approach, a more performant solution involves a single traversal. By initializing a 'max' variable and iterating once through the array, we can find the largest element in optimal O(n) time and O(1) space. This highlights the importance of choosing the right algorithm for the job. Dive deeper into the implementation details and critical ideas to think about: https://lnkd.in/eBQGgE5w #DSA #Algorithms #DataStructures #Programming #Coding

Day 77 on LeetCode Find Smallest Letter Greater Than Target 🔤🔍✅ Continuing the streak with a clean Binary Search application — keeping things simple and consistent during mids 💯 🔹 Approach Used in My Solution The goal was to find the smallest character strictly greater than the target in a sorted array, with wrap-around behavior. Key idea: • Apply binary search on the sorted array • Whenever letters[mid] > target, store it as a potential answer • Move left to find an even smaller valid character • If no such character exists, return letters[0] (wrap-around case) This ensures we always get the next greatest letter efficiently. ⚡ Complexity: • Time Complexity: O(log n) • Space Complexity: O(1) 💡 Key Takeaways: • Practiced binary search for “next greater element” problems • Learned how to handle wrap-around edge cases • Reinforced writing clean and optimized search logic 🔥 Small wins every day consistency is the real progress. #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #Consistency #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

“My code passed 𝟏𝟎𝟎+ test cases… so I thought I was done.” 😌 Then test case 118 happened. 💀 I was solving the 𝐋𝐨𝐧𝐠𝐞𝐬𝐭 𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐢𝐧𝐠 𝐒𝐮𝐛𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞 (LIS) problem using Dynamic Programming (𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐨𝐧 + 𝐌𝐞𝐦𝐨𝐢𝐳𝐚𝐭𝐢𝐨𝐧). Everything looked perfect. Clean code. Correct logic. And most importantly — it was passing. Until it didn’t. Large test cases started crashing with runtime errors. That’s when it hit me… 👉 The problem wasn’t my code. 👉 The problem was my thinking. I was using an O(n²) approach for constraints up to 10⁵. No matter how “correct” it is… it’s not scalable. So I switched to the O(n log n) solution using 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡. Same problem. Same goal. Completely different performance. 💡 That moment changed how I look at problems: Correct ≠ Efficient Passing ≠ Scalable 𝘿𝙤 𝙮𝙤𝙪 𝙥𝙧𝙞𝙤𝙧𝙞𝙩𝙞𝙯𝙚 𝙘𝙤𝙧𝙧𝙚𝙘𝙩𝙣𝙚𝙨𝙨 𝙛𝙞𝙧𝙨𝙩 𝙤𝙧 𝙨𝙘𝙖𝙡𝙖𝙗𝙞𝙡𝙞𝙩𝙮 ? #DataStructures #Algorithms #DynamicProgramming #CodingJourney #SoftwareEngineering #ProblemSolving #TechLearning #LearnInPublic #Developers #Coding

📌 We learn stacks as a simple concept: Last In, First Out 👉 That makes it easier to recognize using stack as a pattern for problems such as “Parantheses matching” or “Undo/Redo” But then you come across a problem like Daily Temperatures 🌡️ Why would you even think of using a stack here? 🤷♀️ And more importantly — how? 🤔 That’s where the idea of a “Monotonic stack” comes in. 📌 Instead of using a stack just for insertion order, we use it to maintain a condition. 👉 Elements are kept in a specific order (increasing or decreasing) 👉 The moment that order is violated, we start popping So the stack isn’t just storing elements — it’s helping us eliminate unnecessary comparisons and keep only what’s useful ✔️ And that shift is what makes the solution efficient ⚡ Why this problem captures my interest is because: 👉 Not every problem clearly signals the pattern you need, the real skill is identifying which structure helps you track and optimize information efficiently👍 #softwareengineering #leetcode #algorithms #dsa #stack #coding #programming #problemSolving #computerscience

Day 68 on LeetCode Guess Number Higher or Lower 🎯✅ Today’s problem reinforced the power of Binary Search on an answer space. 🔹 Approach Used in My Solution The goal was to identify a hidden number using the provided guess() API. Key idea in the solution: • Apply binary search between 1 and n • Pick mid and call guess(mid) • Based on the response:   – 0 → correct number found   – -1 → guessed number is too high → move left   – 1 → guessed number is too low → move right • Continue narrowing the search space until the number is found This is a perfect example of searching efficiently using feedback. ⚡ Complexity: • Time Complexity: O(log n) • Space Complexity: O(1) 💡 Key Takeaways: • Strengthened understanding of binary search with external APIs • Learned how to adjust search space based on feedback • Reinforced the concept of searching on answer space 🔥 Another solid step in mastering binary search patterns! #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #DivideAndConquer #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Not all data structures think the same… some give you freedom, others demand discipline. 🔹 Array → Freedom to access anything, anytime 🔹 Queue → Strict order, one way in — one way out 💡 One is about speed & flexibility 💡 The other is about structure & fairness And that’s the real lesson 👇 👉 In coding (and life), knowing WHEN to use WHAT matters more than knowing EVERYTHING. 🔥 Think about it: Need instant access? → Array wins Need ordered processing? → Queue dominates #DataStructures #Algorithms #Programming #Coding #DSA #ComputerScience #SoftwareEngineering #Developers #LearnToCode #CodingLife #Tech #Engineering #ProblemSolving #LinkedInLearning #CareerGrowth #CodeNewbie

HI CONNECTIONS I recently tackled LeetCode 120, a classic problem that perfectly demonstrates how to transform an exponential brute-force search into a linear-time Dynamic Programming solution. 🔍 The Challenge Given a triangle array, find the minimum path sum from top to bottom. For each step, you can move to the adjacent index of the row below. Example: From index i in the current row, you can move to index i or i + 1 in the next row. 🛠️ My Approach: Bottom-Up Dynamic Programming While a top-down approach (starting from the peak) is intuitive, a bottom-up approach is much cleaner because it avoids complex boundary checks at the edges of the triangle. Start at the Base: Begin with the last row of the triangle as your initial "minimum path" values. Move Upwards: For every node in the row above, the minimum path to the bottom is: Current Value + min(Left Child, Right Child) State Compression: Instead of a full 2D table, I used a 1D array (the size of the bottom row) to store and update the minimum sums in place. The Result: After reaching the top, the first element of the array contains the minimum path sum for the entire triangle. 📊 Efficiency Time Complexity: O(n^2) — Where n is the number of rows. we visit each element in the triangle exactly once. Space Complexity: O(n) — Using only a 1D array to store the bottom row's state. 💡 Key Takeaway The "Bottom-Up" mindset is a powerful tool in optimization. By starting at the destination and working back to the source, we can often simplify the logic and reduce the memory footprint of our algorithms. #LeetCode #DynamicProgramming #AlgorithmDesign #SoftwareEngineering #Optimization #ProblemSolving

Day 69 on LeetCode Search in Rotated Array (Brute Force Insight) 🔍✅ Today’s problem initially felt tricky, but turned out to be one of the simplest when approached directly. 🔹 Approach Used in My Solution Instead of overcomplicating with rotation logic, I used a straightforward linear search. Key idea: • Traverse the array from start to end • Compare each element with the target • Return the index once found, otherwise -1 Sometimes, the simplest approach is the most reliable. 🔹 Initial Thought Process (Overthinking Phase 😅) • Considered rotating the array and then searching • Thought about adjusting indices using formulas like:   (index - rotation + n) % n • But realized that all of this is unnecessary for basic search ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Not every problem needs an optimized approach — clarity > complexity • Avoid overengineering when a simple solution works perfectly • Always validate if a direct approach solves the problem efficiently enough 🔥 Great reminder: Sometimes the “easy way” is the smart way. #LeetCode #DSA #Algorithms #DataStructures #Arrays #LinearSearch #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Cracking LeetCode Medium: Maximum Distance Between a Pair of Values 🚀 I recently solved a classic Two-Pointer problem: Maximum Distance Between a Pair of Values. The Problem: Given two non-increasing arrays, find the maximum j - i such that i <= j and nums1[i] <= nums2[j]. The Strategy: Two Pointers 💡 Since both arrays are already sorted (non-increasing), a nested loop (O(n²)) would be too slow. Instead, I used the Two-Pointer technique: 1️⃣ Start both pointers i and j at 0. 2️⃣ If nums1[i] <= nums2[j], it's a valid pair! Record the distance j - i and move j forward to find an even larger gap. 3️⃣ If nums1[i] > nums2[j], the current nums1[i] is too large. Move i forward to find a smaller value. Complexity: Time: O(n + m) — much faster than the O(n * m) brute force! Space: O(1) — no extra memory used. Efficient algorithms are all about leveraging the properties of the data (like sorting). Happy coding! 💻 #LeetCode #Coding #TwoPointers #Algorithms #DataStructures #TechLearning #Programming #ProblemSolving

Learning from Upsolving a Hard Problem on LeetCode Recently, I upsolved a Hard problem from Biweekly Contest 179 on LeetCode, and it gave me a really valuable insight into Dynamic Programming (DP). Key Takeaway: Sometimes, Tabulation fails while Memoization works better. Here’s why 1. In Tabulation (Bottom-Up), we often compute all possible states of the DP table — even those that are never needed to reach the final answer. 2. This can lead to unnecessary computations and, in some cases, TLE (Time Limit Exceeded). 3. In contrast, Memoization (Top-Down) only computes the states that are actually required, thanks to recursion + caching. 4. This makes it more efficient in problems where the state space is large but only a subset is relevant. My Experience: I initially implemented a Tabulation approach, but it resulted in TLE due to redundant state computations. Switching to Memoization helped me compute only the necessary states — and the solution passed efficiently! Lesson Learned: 1. Don’t blindly choose Tabulation over Memoization. 2. Always analyze the state space and transitions. 3. If many states are irrelevant, Memoization might be the better choice. This was a great reminder that choosing the right approach matters just as much as solving the problem itself. #LeetCode #DynamicProgramming #Memoization #Tabulation #Coding #ProblemSolving #TechLearning #SoftwareEngineering #DSA #Upskilling