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COMP 202: Principles of Object-Oriented Programming II

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Home — Fall 2008

Accounting For The Resources Used By Computations: "Big-Oh" notation

A running program consumes resources such as time (seconds) and space (bits). Frequently, we abstract our units, and measure steps and objects, instead of seconds and bits.
When comparing programs (or algorithms), you should first pay attention to gross differences in time or space consumed, for example, n^3 versus n^2 steps, rather than 3n versus 2n steps.
For a few programs, the cost is fixed and can be calculated by examining the program text. More frequently, however, cost depends on characteristics of the input, such as length.

When we make gross comparisons of programs, we often refer to the ``order-of-magnitude'' of the cost. The notation used is sometimes called ``Big-Oh,'' and is always of the form O(f(n)) where f(n) is some function over the positive integers.
The Big-Oh notation simply means that the cost function is bounded by (is less than) some multiple of the function f(n). For example, if we say
we mean that P equals n^3, plus some terms that are ``on the order of n^2''---i.e., they don't grow faster than kn^2, where k is some constant term.

More precisely,

Definition. A function g(n) is said to be O(f(n)), written
if there are positive integers c and n0 such that
for all n >= n0.
In other words, O(f(n)) is the set of all functions h(n) such that there exist positive integers c and n0 such that
for all n >= n0.

For example,

1+2+3+ ... +n = n(n+1)/2 = n^2/2 + n/2

1+2+3+ ... +n = n^2/2 + O(n)

1+2+3+ ... +n = O(n^2)

Here are some equivalences that allow you to manipulate equations involving order-of-magnitude quantities:
- f(n) = O(f(n))
- K * O(f(n)) = O(f(n))
- O(f(n)) + O(f(n)) = O(f(n))
- O(f(n)) * O(g(n)) = O(f(n) * g(n))

Also, the base to which a logarithm is computed doesn't affect the order of magnitude, because changing the base of the logarithm from 2 to c changes the value by a constant factor of log2(c).

(written by Alan Cox)

Quick and Dirty Explanation:

Big-Oh tells us how a cost of running a program (algorithm) scales with respect to n for large values of n, e.g. linearly, quadraticly, logarithmically, etc. The slower the cost rises with n, the better, so long as we are dealing with large values of n.

Examples:

Summing a list of numbers: O(n) -- single traversal of the list
Sorting a list by inserting first into a sorted rest: O(n^2) -- double traversal of the list (a traversal to insert each element)
Finding an element in a perfectly balanced binary search tree: O(log(n)) -- height of a balanced tree is O(lon(n))
Finding an element is a completely unbalanced tree, worst case scenario: O(n) -- all elements along one branch = linear structure.

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