BIG O NOTATION Meaning and
Definition
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Big O notation is a mathematical notation used to analyze and describe the behavior and efficiency of algorithms or functions. It expresses the upper bound or worst-case scenario of the time complexity or space complexity of an algorithm, in terms of the input size.
In simpler terms, Big O notation allows us to estimate the performance of an algorithm by identifying the rate at which the algorithm's performance will grow as the input size increases. It describes how an algorithm's running time or memory usage scales with the input.
The notation is represented as O(f(n)), where f(n) is a function that represents the maximum number of steps or operations an algorithm will perform as a function of the input size n. It provides an upper limit or worst-case scenario of the algorithm's performance.
For example, if an algorithm has a time complexity of O(n^2), it means that the algorithm's running time will grow proportionally to the square of the input size. Similarly, if an algorithm has a space complexity of O(n), it implies that the algorithm's memory usage will increase linearly with the input size.
Big O notation is extensively used in computer science and algorithm analysis to compare and contrast different algorithms, make informed choices about algorithm design, and assess the scalability of algorithms in real-world applications.
