Little O Notation
Little O notation is a mathematical concept in computer science used to describe an upper bound on the growth rate of a function, specifically indicating that one function grows strictly slower than another. It is a stricter version of Big O notation, where f(n) = o(g(n)) means f(n) grows asymptotically slower than g(n) as n approaches infinity. This notation is primarily applied in algorithm analysis to characterize the worst-case or average-case time or space complexity with more precision than Big O.
Developers should learn Little O notation when they need to analyze algorithms with fine-grained asymptotic behavior, such as in theoretical computer science, advanced algorithm design, or performance optimization for large-scale systems. It is particularly useful for proving that an algorithm's complexity is strictly better than a given bound, for example, in research papers or when comparing algorithm efficiency in edge cases where Big O might be too coarse.