What is big-o notation for describing time complexity of algorithm?

Answer 1

Big O notation allows to specify the complexity of an algorithm in a simple formula, by dismissing lower-order variables and constant factors.

For example, one might say that a sorting algorithm has O(n * lg(n)) complexity, where n is the number of items to sort.
Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.

Answer 2

Big O Notation is typically used for describing the worst case running time scenario of a program. For example, the bubble sort has a O(n2) notation because in the worst case scenario, you have to sort every element and then traverse through the data set once more to verify sorted order, meaning a data set of n items had n2 operations to complete.

Answer 3

It is used to describe the time complexity of an algorithm, where O(1) is a constant time, O(n) is linear time, and O(n^2) is exponential time. Big Oh Notation reduces the time complexity to an approximation by defining the complexity as the largest parameter. For example, if an estimated time complexity is x^2+x*5+100, Big Oh Notation would state that the time complexity is O(n^2). This is because, as x increases, the x*5 and 100 will become increasingly insignificant as x approaches infinity.

Answer 4

Big-O notation describes the behavior of a function for big inputs. It tries to capture the core of a function. Some examples of big-O are: f(x)=x is in O(x) and f(x)=x^2 is in O(x^2).

Answer 5

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