The Big O notation is a mathematical concept that is used to analyse the running time or memory complexity of algorithms. It describes how the runtime of an algorithm grows in relation to the input size . The fastest growth factor is identified and constant factors and lower order terms are ignored. For example, a runtime of means that the runtime increases quadratically to the size of the input, which is often observed in practice with nested loops. The Big O notation helps developers and researchers to compare algorithms and find more efficient solutions by providing a clear overview of the behaviour of algorithms with large amounts of data.
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