Asymptotic Notations in Data Structure

Asymptotic Notations are languages that allow us to analyze an algorithm‟s running time by identifying its behavior as the input size for the algorithm increases. This is also known as an algorithm‟s growth rate. Does the algorithm suddenly become incredibly slow when the input size grows? Does it mostly maintain its quick run time as the input size increases? Asymptotic Notation gives us the ability to answer these questions.

Types of Asymptotic Notation
In the first section of this doc, we described how an Asymptotic Notation identifies the behavior of an algorithm as the input size changes. Let us imagine an algorithm as a function f, n as the input size, and f(n) being the running time. So for a given algorithm f, with input size n you get some resultant run time f(n). This results in a graph where the Y axis is the runtime, X axis is the input size, and plot points are the resultants of the amount of time for a given input size.You can label a function, or algorithm, with an Asymptotic
Notation in many different ways. Some examples are, you can describe an algorithm by its best case, worse case, or equivalent case. The most common is to analyze an algorithm by its worst case. You typically don‟t evaluate by best case because those conditions aren‟t what you‟re planning for. A very good example of this is sorting algorithms; specifically, adding elements to a tree structure. Best case for most algorithms could be as low as a single operation. However, in most cases, the element you‟re adding will need to be sorted appropriately through the tree, which could mean examining an entire branch. This is the worst case, and this is what we plan for.

Types of functions, limits, and simplification
Logarithmic Function - log n
Linear Function - an + b
Quadratic Function - an^2 + bn + c
Polynomial Function - an^z + . . . + an^2 + a*n^1 + a*n^0, where z is some constant
Exponential Function - a^n, where a is some constant
These are some basic function growth classifications used in various notations. The list starts at the slowest growing function (logarithmic, fastest execution time) and goes on to
the fastest growing (exponential, slowest execution time). Notice that as „n‟, or the input, increases in each of those functions, the result clearly increases much quicker in quadratic, polynomial, and exponential, compared to logarithmic and linear. One extremely important note is that for the notations about to be discussed you should do your best to use simplest terms. This means to disregard constants, and lower order terms, because as the input size (or n in our f(n) example) increases to infinity (mathematical limits), the lower order terms and constants are of little to no importance. That being said, if you have constants that are 2^9001, or some other ridiculous, unimaginable amount, realize that simplifying will skew your notation accuracy.

Since we want simplest form, lets modify our table a bit…
Logarithmic - log n
Linear - n
Quadratic - n^2
Polynomial - n^z, where z is some constant
Exponential - a^n, where a is some constant

Big-O Notations:
Big-O, commonly written as O, is an Asymptotic Notation for the worst case, or ceiling of growth for a given function. It provides us with an asymptotic upper bound for the growth rate of runtime of an algorithm. Say f(n) is your algorithm runtime, and g(n) is an arbitrary time complexity you are trying to relate to your algorithm. f(n) is O(g(n)), if for some real constant c (c > 0), f(n) <= c g(n) for every input size n (n > 0).

Big-Omega Notations:
Big-Omega, commonly written as Ω, is an Asymptotic Notation for the best case, or a floor growth rate for a given function. It provides us with an asymptotic lower bound for the
growth rate of runtime of an algorithm. f(n) is Ω(g(n)), if for some real constant c (c > 0), f(n) is >= c g(n) for every input size n (n > 0).

Theta Notations:
Theta, commonly written as Θ, is an Asymptotic Notation to denote the asymptotically tight bound on the growth rate of runtime of an algorithm. f(n) is Θ(g(n)), if for some real constants c1, c2 (c1 > 0, c2 > 0), c1 g(n) is < f(n) is < c2 g(n) for every input size n (n > 0).
∴ f(n) is Θ(g(n)) implies f(n) is O(g(n)) as well as f(n) is Ω(g(n)). Feel free to head over to additional resources for examples on this. Big-O is the primary notation use for general algorithm time complexity.

Mukesh Rajput
Mukesh Rajput

Mukesh Rajput

I am a Computer Engineer, a small amount of the programming tips as it’s my hobby, I love to travel and meet people so little about travel, a fashion lover and love to eat food, I am investing a good time to keep the body fit so little about fitness also..

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