Solved LeetCode 507. Perfect Number with O(1) time complexity using math insight.

Today's LeetCode problem was the foundational 509. Fibonacci Number. While there are faster ways to solve this, I focused on implementing the classic recursive solution. It's such a fundamental concept to understand, as it directly mirrors the mathematical definition: $F(n) = F(n-1) + F(n-2)$. It's a great reminder that coding is often about translating mathematical logic directly into code. The pursuit of efficiency (like using memoization next!) never stops, but understanding the basics is paramount. #LeetCode #Recursion #DSA #Algorithms #Python #ComputerScience

Day 8 of #100DaysOfLeetCode Problem: 9. Palindrome Number Category: Math / Two Pointers / Logic Today’s problem focused on determining whether an integer reads the same backward as forward — essentially checking if a number is a palindrome. 🧠 Key Learnings: Reversed the number mathematically using modulo and integer division. Avoided converting integers to strings, focusing purely on arithmetic logic. Understood how to handle special cases like negative numbers and numbers ending with zero. Strengthened my skills in digit manipulation and loop-based reversal logic. 🎯 Takeaway: Sometimes, avoiding built-in functions helps build a deeper understanding of fundamental logic — every digit and operation counts! #LeetCode #100DaysOfCode #ProblemSolving #CodingJourney #Math #LogicBuilding #Python #AIEngineer #Consistency

𝗗𝗮𝘆 𝟯𝟵 𝗼𝗳 #𝟭𝟴𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗖𝗼𝗱𝗲 Today, I built on the first/last occurrence solution to 𝗰𝗼𝘂𝗻𝘁 𝗼𝗰𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝗲𝘀 𝗼𝗳 𝗮 𝘁𝗮𝗿𝗴𝗲𝘁 𝗶𝗻 𝗮 𝘀𝗼𝗿𝘁𝗲𝗱 𝗮𝗿𝗿𝗮𝘆 𝘄𝗶𝘁𝗵 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀. Using the same lower_bound and upper_bound helpers: Lower bound → first index where element ≥ target Upper bound → first index where element > target The count is simply: count = upper_bound - lower_bound This gives an O(log n) solution — much faster than scanning the entire array, especially with many duplicates. It’s a great example of how breaking a problem into reusable pieces leads to clean and efficient code. Perfect for analytics, frequency analysis, and search optimizations! 📊 #Python #Algorithms #BinarySearch #FrequencyCount #Coding #ProblemSolving

🚀 Day 40 of #100DaysOfDSA Solved LeetCode Problem #69 – Sqrt(x) 🧮 💡 Problem Insight: Given a non-negative integer x, return the square root of x rounded down to the nearest integer. For example: Input: x = 8 Output: 2 (since √8 ≈ 2.828, and floor(2.828) = 2) ✨ Key Learnings: Practiced binary search to find results efficiently without using built-in math functions. Learned how to narrow down search space based on mid-square comparisons. Reinforced understanding of integer division and rounding down behavior. Time Complexity: O(log n) — fast and efficient! Space Complexity: O(1) 💬 Lesson: Binary Search isn’t just for sorted arrays — it’s a mindset for narrowing down possibilities quickly 🚀 #LeetCode #Python #DSA #BinarySearch #ProblemSolving #100DaysOfCode #Day40 #CodingJourney

💡 Big-O & Arrays — Building Strong DSA Foundations In this post, I explore how algorithm efficiency is measured using Big-O Notation and how static vs dynamic arrays differ in performance. These concepts shape how we write scalable, efficient code — the kind that handles real-world data, not just textbook examples. Let’s keep growing, one concept at a time. #DataStructures #Algorithms #BigO #Arrays #Python #LearningJourney #DataScience #Coding

✨ Leetcode #3370: Smallest Number With All Set Bits Today, I solved an interesting bit manipulation problem where the task was to find the smallest number greater than or equal to a given n, such that its binary representation contains only set bits (1s). 🔍 Concept Overview: - This problem beautifully highlights the power of bit manipulation and mathematical insight. - The key lies in observing the pattern of numbers that have all bits set, like 1, 3, 7, 15, 31, etc. - These numbers can be represented in a uniform mathematical form, which makes the solution both elegant and efficient. 💡 Key Learning: - Understanding how bit shifting and logarithmic operations can simplify problems involving binary representations. - Reinforcing the importance of pattern recognition in bit level problems. - Appreciating how a constant time solution can emerge from strong mathematical reasoning. 🧠 Complexity: O(1) A pure logic based solution; concise, optimal, and satisfying! #LeetCode #BitManipulation #Coding #ProblemSolving #Python #LearningEveryday #DataStructuresAndAlgorithms

🚀 LeetCode #1526: Minimum Number of Increments on Subarrays to Form a Target Array Today I tackled an interesting Greedy Algorithm problem that really tests your ability to spot patterns and simplify complex logic. 🧩 Problem Brief: We start with an array of zeros and can increment any subarray by 1 in one operation. The goal is to form the target array using the minimum number of operations. 💡 Key Insight: Instead of simulating every operation, focus on how much each element increases compared to the previous one; each increase represents new operations needed. ⚙️ Formula: operations = target[0] + Σ(max(0, target[i] - target[i-1])) 🧠 Complexity: Time: O(n) Space: O(1) 🎯 Takeaway: Hard problems often become simple once you recognize the pattern behind the process. #LeetCode #Python #DSA #Coding #ProblemSolving #GreedyAlgorithm #LearningEveryday