Explain the concept of Big O notation in algorithm complexity analysis.

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∻Big O Notation in Algorithm Complexity Analysis∻ ‖ ▪ ⋮ Definition⋮ Big O notation is a mathematical notation used to describe the upper bound of an algorithm's running time or space requirements in terms of the size of the input data (n). It provides a high-level understanding of the algorithm's efficiency and scalability. ‖ ‖ ▪ ⋮ Purpose⋮ The main purpose of Big O notation is to classify algorithms according to how their run time or space requirements grow as the input size grows. ‖ ‖ ▪ ⋮ Usage⋮ When comparing algorithms, Big O notation can help determine which algorithm is more efficient for large input sizes. It abstracts away constants and lower-order terms to focus on the main factor that affects the growth rate. ‖ ‖ ▪ ⋮ Examples⋮ Common Big O notations include $O(1)$ for constant time, $O(\log n)$ for logarithmic time, $O(n)$ for linear time, $O(n\log n)$ for linearithmic time, $O(n^2)$ for quadratic time, etc. ‖ ‖ ▪ ⋮ Limitations⋮ Big O notation does not give an exact number of operations; it only describes the growth rate. It also ignores constants and lower-order terms, which can be significant for small input sizes or specific use cases. ‖ ⚹ Big O notation is a fundamental concept in computer science for analyzing and conveying the efficiency of algorithms. It helps in understanding how the time or space requirements of an algorithm will scale with the size of the input. ⚹

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