How to Solve LeetCode #33 - Search in Rotated Sorted Array

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗿𝗿𝗮𝘆 𝗥𝗲𝘀𝗶𝘇𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 Ever wondered what happens behind the scenes when you use append() in Go or add an element to a Python list or a Java ArrayList?  They all rely on something called the Geometric Dynamic Array Resizing Algorithm one of the most elegant examples of computer science, balancing speed and memory efficiency. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮  When a dynamic array runs out of space: It doesn’t just grow by one element (that would be slow). Instead, it grows geometrically usually doubling its capacity (×2) or increasing by 1.5× depending on the language. This means fewer reallocations, faster average performance, and a predictable O(1) amortized time complexity for append operations. For example, in Go: If a slice’s capacity is small (<1024), it doubles on growth. If it’s large (≥1024), it grows by ~25%. This behavior is managed internally by Go’s growslice() function a beautiful piece of design that optimizes both speed and memory usage. It’s a great reminder that even “simple” operations like appending to a list rely on deep algorithmic principles like amortized analysis and geometric progression. Understanding these details helps us write more efficient, predictable, and scalable code. #GoLang #Programming #ComputerScience #DataStructures #AmortizedAnalysis #GoDeveloper

🚀 Big O Notation — The Language of Algorithm Efficiency! If you’ve ever wondered how to measure how fast or scalable your code is, Big O Notation is your answer. It explains how the runtime of an algorithm grows as your input size increases — a must-know for every developer and computer science student. 💡 Here’s a simple breakdown 👇 🔸 O(1) — Constant Time Fastest! Runtime doesn’t depend on input size. 📘 Example: Accessing an element in an array or hash table. 🔹 O(log n) — Logarithmic Time Grows slowly even as input increases. 📘 Example: Binary search, balanced binary trees. 🔸 O(n) — Linear Time Runtime grows directly with input size. 📘 Example: Finding max/min in an unsorted array. 🔹 O(n log n) — Linearithmic Time Efficient mix of linear and logarithmic. 📘 Example: Merge Sort, Quick Sort (average), Heap Sort. 🔸 O(n²) — Quadratic Time Common in nested loops. 📘 Example: Bubble Sort, Selection Sort, Insertion Sort. 🔹 O(n³) — Cubic Time Heavier — often seen in matrix operations. 📘 Example: Naïve matrix multiplication. 🔸 O(2ⁿ) — Exponential Time Doubles with every extra element — very slow! 📘 Example: Recursive subset sum or traveling salesman problem. 🔹 O(n!) — Factorial Time The slowest growth — factorial explosion! 📘 Example: Generating all permutations. 🔸 O(√n) — Square Root Time Grows proportionally to the square root of input. 📘 Example: Finding primes up to n using the Sieve of Eratosthenes. 📊 In short: From O(1) ➡️ O(n!) — as complexity increases, performance decreases. Understanding these helps you write efficient, scalable, and optimized code. 💻 #BigONotation #DataStructures #Algorithms #Coding #Programming #TechLearning #ComputerScience #SoftwareEngineering #CodeOptimization #Developers #ByteByteGo #LearningNeverStops #ProblemSolving #TechEducation #CodingInterview #CareerInTech #DataScience #MachineLearning #ArtificialIntelligence #Python #Java #JavaScript #SoftwareDevelopment #Engineering #StudyResources #CSFundamentals

“Don’t memorize the code, try to understand why this solution works for this problem” Day 61/90 – DSA Challenge Update 61 days into my 90-day DSA learning journey, and today I solved LeetCode Problem 2011: Final Value of Variable After Performing Operations! 🔹 Approach Initialize X = 0. Loop through each operation: If it contains "++", increment X. If it contains "--", decrement X. Return X. 🔹Optimized Approach: No need for complex parsing; just check the presence of ++ or -- in each string. 🔹 Complexity Time Complexity: O(n), where n = number of operations. Space Complexity: O(1), constant extra space. 🔹Real-World Applications Compiler & interpreter design: Updating variable states based on increment/decrement operations. Simulation & automation systems: Tracking counters, scores, or resource usage in sequential operations. Game development: Updating player stats, scores, or counters efficiently. 🔹 Key Takeaways Simple string operations can have real-world implications in state tracking. Optimized and readable code often beats brute force for straightforward problems. 🔹Starts so far: 250+ LeetCode problems solved Strengthened problem-solving and coding fluency in Java Covered stacks, queues, linked lists, hashmaps, sliding window, binary search, and more. The journey continues—only 29 days left to master DSA before the deadline! #DSAChallenge #Java #LeetCode #ProblemSolving #90DayChallenge #CodingJourney #Algorithms

🚩 Problem: 238. Product of Array Except Self 🔥 Day 46 of #100DaysOfLeetCode 🔍 Problem Summary: Given an integer array nums, return an array answer such that answer[i] is equal to the product of all the elements of nums except nums[i]. You must solve it without using division and in O(n) time. 🧠 Intuition: Instead of recalculating products repeatedly, we can compute prefix and suffix products: prefix[i] → product of all elements before index i. suffix[i] → product of all elements after index i. Then, answer[i] = prefix[i] * suffix[i] But to optimize space, we can do this in one pass using constant extra space (excluding the output array). ✅ Optimized Approach (Prefix & Suffix Multiplication): Traverse from left to right, building prefix products in the answer array. Traverse from right to left, multiplying suffix products into the existing answer. 📊 Complexity: Time Complexity: O(n) Space Complexity: O(1) (excluding output array) ✨ Key Takeaway: This problem highlights the power of prefix-suffix techniques — optimizing from O(n²) to O(n) by reusing partial computations. A great demonstration of how small mathematical insights can lead to highly efficient solutions. Link:[https://lnkd.in/g_Wxm9rc] #100DaysOfLeetCode #Day46 #Problem238 #ProductOfArrayExceptSelf #PrefixSum #SuffixProduct #Java #Algorithms #DSA #LeetCode #ProblemSolving #CodingChallenge #InterviewPreparation #CrackingTheCodingInterview #CodingCommunity #DataStructures #SoftwareEngineering #DeveloperJourney #ArjunInfoSolution #CodeNewbie #Programming #LearnToCode #TechCareers #CareerGrowth #ComputerScience #CodeEveryday #ZeroToHero #CodeLife #CodingIsFun #GeeksForGeeks #GameDeveloper #Unity #JavaDeveloper #AI #MachineLearning

🚩 Problem: 442. Find All Duplicates in an Array 💻 Day 47 of #100DaysOfLeetCode 🔍 Problem Summary: Given an integer array nums of length n where each element is between 1 and n (inclusive), some elements appear twice and others appear once. Return all the elements that appear twice — without using extra space and in O(n) time. 🧠 Intuition: We can’t use extra data structures like HashMap or Set, so we need to mark visited numbers cleverly. Since all numbers are in range 1...n, we can use index marking: For each number num, mark its corresponding index abs(num) - 1 as negative. If we find it already negative, it means this number appeared before → duplicate found. ✅ Optimized Approach (In-Place Marking): Traverse the array. Use the absolute value of each number to find its index. If the number at that index is already negative → duplicate. Else, mark it negative. 📊 Complexity: Time Complexity: O(n) Space Complexity: O(1) (ignoring output list) ✨ Key Takeaway: This problem highlights a powerful trick — using array indices to track seen elements. In-place marking is a common interview pattern for problems with constraints on space usage. LINK:[https://lnkd.in/gt88TFzp] #100DaysOfLeetCode #Day48 #Problem442 #FindAllDuplicatesInAnArray #LeetCode #ProblemSolving #DSA #Algorithms #Java #CodingChallenge #Hashing #ArrayManipulation #InPlaceAlgorithm #CodingCommunity #CodeNewbie #InterviewPreparation #CrackingTheCodingInterview #SoftwareEngineering #DeveloperJourney #ArjunInfoSolution #Programming #LearnToCode #TechCareers #CareerGrowth #ComputerScience #CodeEveryday #ZeroToHero #CodeLife #CodingIsFun #GameDeveloper #JavaDeveloper #Unity #AI #MachineLearning

🧠 𝗛𝗼𝘄 𝗮 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗖𝗼𝗺𝗽𝗶𝗹𝗲𝗿 𝗪𝗼𝗿𝗸𝘀 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆, I’ve been diving deep into compiler design, and it’s been fascinating to understand how our high-level code turns into something the computer can actually execute! From what I’ve learned, a compiler typically goes through six main stages: 𝟭. 𝗦𝗼𝘂𝗿𝗰𝗲 𝗖𝗼𝗱𝗲 📝  This is where we write our program in a human-readable programming language like C++, Go, or Rust. 𝟮. 𝗟𝗲𝘅𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗟𝗲𝘅𝗲𝗿) ✂️  The lexer takes the source code and breaks it down into small units called tokens (keywords, identifiers, operators, etc.). 𝟯. 𝗦𝘆𝗻𝘁𝗮𝘅 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗮𝗿𝘀𝗲𝗿) 🌳  The parser organizes these tokens into a structured form called an Abstract Syntax Tree (AST), representing the grammatical structure of the code. 𝟰. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 ✅  This step checks whether the AST makes logical sense — verifying things like type correctness, variable declarations, and function definitions. 𝟱. 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 (𝗜𝗥) ⚙️  The compiler then transforms the AST into an intermediate code (like LLVM IR), which serves as a bridge between high-level languages and machine code. This layer also enables cross-language interoperability. 𝟲. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 🚀  Finally, the compiler converts the IR into optimized machine code (binary), which the CPU can execute directly. Toolchains like LLVM handle much of this optimization process. Compilers are truly one of the most elegant pieces of engineering — translating human logic into machine instructions with precision and efficiency! 💡 #Programming #CompilerDesign #ComputerScience #LLVM #SystemsProgramming #LearningJourney

🔹 DSA Daily | Count Zeroes in a Binary Sorted Array (Optimized with Binary Search) (C++) Today, I solved an optimized version of the **Count Zeroes** problem — leveraging **binary search** to reduce time complexity. 💡  🧩 Problem Statement:   Given a **binary sorted array** (containing only 0s and 1s), count the number of **zeroes** efficiently.  ⚙️ Approach (Binary Search):   1️⃣ Initialize `low` and `high` pointers for binary search.   2️⃣ Use binary search to find the **first occurrence of 0**.   3️⃣ Once found, the total number of zeroes = `array size - index of first zero`.   4️⃣ This method avoids traversing the entire array and improves efficiency significantly. ⏱️ Time Complexity: O(log n) — binary search   💾 Space Complexity: O(1) — constant space  This problem highlights how **classic searching techniques** like binary search can make solutions both **fast and elegant**. 🚀  #DSA #CPlusPlus #Coding #ProblemSolving #BinarySearch #Arrays #CodeEveryday #GeeksforGeeks #LeetCode #ProgrammingJourney #DataStructures #Algorithms #InterviewPrep #CodingCommunity #LogicBuilding #EfficientCode #LearnToCode #TechJourney

🔹 Day 6 of 30 – LeetCode Challenge: Longest Common Subsequence 🔠 Today’s challenge was all about finding the Longest Common Subsequence (LCS) between two strings — one of the most important problems in Dynamic Programming. 🧩 Problem: Given two strings text1 and text2, find the length of their longest subsequence that appears in both. A subsequence keeps the order of characters but doesn’t require them to be consecutive. Example: Input: text1 = "abcde", text2 = "ace" Output: 3 Explanation: LCS = "ace" 💡 Approach: I used a Dynamic Programming table where: dp[i][j] = LCS length between text1[0:i] and text2[0:j] If characters match → 1 + dp[i-1][j-1] Else → max(dp[i-1][j], dp[i][j-1]) ⚙️ Complexity: Time Complexity: O(m × n) Space Complexity: O(m × n) 🏆 Result: ✅ All test cases passed 💡 Improved understanding of 2D DP table formulation 📚 Learned how to build relationships between subproblems 💬 Learning: The LCS problem is the foundation for many advanced algorithms like Edit Distance, Diff Tools, and DNA sequence alignment. It’s a great exercise to visualize how dynamic programming connects overlapping subproblems! #Day6 #LeetCode #DynamicProgramming #LCS #Python #Algorithms #DataStructures #30DaysOfCode #CodingChallenge #MTech

Leveling up my C journey at Emertxe: My fourth project takes me into compiler fundamentals with a Lexical Analyzer! 🔍 Constructed a sophisticated lexical analyzer in C that processes source code through advanced character-by-character analysis and tokenization. The system identifies and categorizes all C language elements including keywords, identifiers, numbers (supporting decimal, octal, hexadecimal formats), operators, and literals while managing complex states for nested comments, string literals, and preprocessor directives. It implements comprehensive syntax validation, detecting errors like missing semicolons, unmatched brackets, and invalid identifiers with precise line number reporting. 🛠️ Technologies Used: C, Finite State Machines, Pattern Matching, String Processing, Error Handling, File I/O 🔑 Key Challenges & Learnings: ⚡ Challenge: Handling nested comments and string literals within code 💡 Solution: Implemented stack-based state machine with context tracking 📚 Learning: Mastered finite automata design for language parsing ⚡ Challenge: Token ambiguity between operators and complex symbols 💡 Solution: Developed lookahead buffer with token precedence rules 📚 Learning: Learned compiler-level tokenization strategies ⚡ Challenge: Error recovery and meaningful error reporting 💡 Solution: Implemented error recovery heuristics with intelligent error localization 📚 Learning: Understood robust error handling in language processors ⚡ Challenge: Performance optimization for large source files 💡 Solution: Designed efficient buffering and streaming processing 📚 Learning: Mastered performance optimization in text processing 🌍 Real-World Applications: • Compiler development - Core component of programming language compilers • IDE development - Syntax highlighting and code analysis tools • Code quality tools - Static analysis and linting applications 🔗 GitHub Link: https://lnkd.in/gFW-Nejw #Emertxe #CompilerDesign #LexicalAnalysis #StateMachines #ProgrammingLanguages #CProgramming #SyntaxAnalysis #ComputerScience #SoftwareEngineering #Parsing #Tokenizer