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Big O Algorithm Complexity Cheat Sheet Know Thy

Download 1M+ code from https://codegive.com/02972a9 big o algorithm complexity: a comprehensive tutorial with code examples big o notation is a crucial concept in computer science used to describe the performance or complexity of an algorithm. it provides a high-level, asymptotic analysis of how the runtime or space requirements of an algorithm grow as the input size increases. instead of focusing on exact execution times (which vary based on hardware, compiler, etc.), big o focuses on the *order of growth*. this allows us to compare algorithms efficiently and choose the most efficient one for a given task. this tutorial will delve into various big o complexities, providing explanations, code examples (primarily in python), and practical implications. *1. understanding the basics:* big o notation expresses the upper bound of an algorithm's complexity. it represents the worst-case scenario. we often ignore constant factors and lower-order terms because they become insignificant as the input size (usually denoted by 'n') grows very large. **f(n) = o(g(n))**: this means that the growth rate of function f(n) is bounded above by the growth rate of function g(n). in simpler terms, for sufficiently large n, f(n) will be less than or equal to a constant multiple of g(n). *2. common big o notations and their meanings:* | big o notation | description | example | python code example (illustrative) | |-----------------|-------------------------------------------------|----------------------------------------------|---------------------------------------------| | o(1) | constant time – runtime is independent of input size | accessing an element in an array | `my_list = [1, 2, 3]; print(my_list[0])` | | o(log n) | logarithmic time – runtime grows logarithmically with input size | binary search | `import bisect; bisect.bisect_left(sorted_list, target)` | | o(n) | lin ... #BigO #AlgorithmComplexity #CheatSheet Big O notation algorithm complexity time complexity space complexity worst-case analysis average-case analysis best-case analysis complexity classes common algorithms performance analysis asymptotic notation algorithm efficiency growth rate computational complexity algorithm optimization