Big Oh is often used to describe the worst-case of an algorithm by taking the highest order of a polynomial function and ignoring all the constants value since they aren’t too influential for sufficiently large input.

Big Omega `Ω`

Big Omega is the opposite of Big Oh, if Big Oh was used to describe the upper bound (worst-case) of a asymptotic function, Big Omega is used to describe the lower bound of a asymptotic function. In analysis algorithm, this notation is usually used to describe the complexity of an algorithm in the best-case, which means the algorithm will not be better than its best-case.

Big Theta `Θ`

When an algorithm has a complexity with lower bound = upper bound, say that an algorithm has a complexity O(n*log(n)) and Ω(n*log(n)), it’s actually has the complexity Θ(n*log(n)), which means the running time of that algorithm always falls in n*log(n) in the best-case and worst-case.

Data Structures

A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

Algorithms

An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.

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Package last updated on 04 Nov 2018

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algorithms-data-structures

Algorithms and Data Structures

Big O

Big Omega Ω

Big Theta Θ