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[BigONotation] is used to describe the complexity of [Algorithm]s (aka ComplexityTheory) and is the standard way to to describe how the execution time of a function scales to its input size. Big O notation is generally written with, well, a big O, and specifically defines the __upper bound__ of the complexity of an algorithm in the average case. In the following examples, the symbol ''n'' denotes the size of the input and the symbol ''k'' denotes an arbitrary constant. ; ''!O(1)'' ie ''constant'': Independant of its input size. Such an algorithm would always execute the same sequence of steps no matter what the input size. ; ''O(n)'' ie ''linear'': The amount of processing increases linearly as the size of the input increases. Searching an unsorted list by reading each element is a linear time algorithm. ; ''O(n log n)'' ie ''log linear'' : Many good algorithms that need to process their input according to other parts of itself are log linear. QuickSort is the canonical example. ; ''O(n^k)'' ie ''polynomial'' : This is going to be quite slow with a large input size. Many naive algorithms that need to process their input according to other parts of itself execute in polynomial time. ''k'' is commonly 2, and is usually the result of a nested loop, where both the outer and inner loop iterate over the entire input. Thus, a total of ''n * n'' iterations is required. The canonical example would be BubbleSort. ; ''O(k^n)'' ie ''exponential'' : Such an algorithm will probably not terminate within the lifetime of the Universe for large inputs. Consider: with ''k := 2'', if the time it takes is to process input of size ''n = 1'' is one nanosecond (1/1,000,000 sec), it would take over a year to process an input of size ''n = 45'', and over 35 years to process just 5 more elements for ''n = 50''. [BigONotation] is complemented by Little O notation, theta notation and others. However, they are rarely used outside of CS theory. These deal with cases such as the lower bound of the average case and the worst case. F.ex, for degenrate input, QuickSort scales to ''n^2'' instead of ''n log n''. [BigONotation] is a concerned purely with the theoretical aspects of an [Algorithm] and ignores RealWorld efficiency issues that arise from its implementation. Only the fastest growing component of the complexity of an [Algorithm] is used to describe the complexity, and constant factors are discarded. F.ex, if an [Algorithm] scales by ''5n^2 + 6n - 2'', it would be described as being of complexity ''O(n^2)''. This means in terms of [BigONotation] it is in the same class as an [Algorithm] that scales ''n^2 + 10n'', even though the latter would perform much better with growing input sizes. See also: * [A rough guide to big-oh notation|http://www.cs.strath.ac.uk/~mdd/teaching/alg&comp/big_oh.html] * [An informal introduction to O(N) notation|http://www.perlmonks.org/index.pl?node_id=227909]
3 pages link to
BigONotation
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BubbleSort
ComplexityTheory
AVLTree
