Big O notation is also used in many other fields to provide similar estimates.

Big O notation has two main areas of application.

Big O notation is useful when analysis of algorithms for efficiency.

This justifies usage of big O notation in the definition.

The two terms are not equal; see Big O notation for more details.

Big O notation is used to describe the running time of graph algorithms.

See big O notation for an explanation of the notation used.

However, the running time of an algorithm is usually expressed in big O notation, ignoring constant factors.

The result is normally expressed using Big O notation.

See asymptotic analysis and big O notation for the notation.

The Big O Notation is often used in the context of complexity class.

It can multiply two matrices in time (see Big O notation).

In statistical mechanics often will be : see Big O notation.

The following two right-hand side big O notations have dramatically different meanings:

For the algorithmic performance concept 'constant factor', see Big O notation.

For example, the statement that the sine function is continuous; or any theorem written in big O notation.

Big O notation can also be used in conjunction with other arithmetic operators in more complicated equations.

The time complexity of an algorithm is commonly expressed using big O notation, which excludes coefficients and lower order terms.

The "O" is for big O notation, meaning "terms involving lower order".

In big O notation, the conjecture is:

Upper and lower bounds are usually stated using the big O notation, which hides constant factors and smaller terms.

See Big O notation for a comparison of the rate of growth of various functions.

This means that the local truncation error (the error made in one step) is , using the big O notation.

Algorithmic complexity is generally specified using Big O Notation.

On a sparse graph, Johnson's algorithm may be more efficient, taking Big O notation time.