The complexity ladder, Data Structure & Algorithms

Assignment Help:

The complexity Ladder:

  • T(n) = O(1). It is called constant growth. T(n) does not raise at all as a function of n, it is a constant. For illustration, array access has this characteristic. A[i] takes the identical time independent of the size of the array A.
  • T(n) = O(log2 (n)). It is called logarithmic growth. T(n) raise proportional to the base 2 logarithm of n. In fact, the base of logarithm does not matter. For instance, binary search has this characteristic.
  • T(n) = O(n). It is called linear growth. T(n) linearly grows with n. For instance, looping over all the elements into a one-dimensional array of n elements would be of the order of O(n).
  • T(n) = O(n log (n). It is called nlogn growth. T(n) raise proportional to n times the base 2 logarithm of n. Time complexity of Merge Sort contain this characteristic. Actually no sorting algorithm that employs comparison among elements can be faster than n log n.
  • T(n) = O(nk). It is called polynomial growth. T(n) raise proportional to the k-th power of n. We rarely assume algorithms which run in time O(nk) where k is bigger than 2 , since such algorithms are very slow and not practical. For instance, selection sort is an O(n2) algorithm.
  • T(n) = O(2n) It is called exponential growth. T(n) raise exponentially.

In computer science, Exponential growth is the most-danger growth pattern. Algorithms which grow this way are fundamentally useless for anything except for very small input size.

Table 1 compares several algorithms in terms of their complexities.

Table 2 compares the typical running time of algorithms of distinct orders.

The growth patterns above have been tabulated in order of enhancing size. That is,   

  O(1) <  O(log(n)) < O(n log(n)) < O(n2)  < O(n3), ... , O(2n).

Notation

Name

Example

O(1)

Constant

Constant growth. Does

 

 

not grow as a function

of n. For example, accessing array for one element A[i]

O(log n)

Logarithmic

Binary search

O(n)

Linear

Looping over n

elements, of an array of size n (normally).

O(n log n)

Sometimes called

"linearithmic"

Merge sort

O(n2)

Quadratic

Worst time case for

insertion sort, matrix multiplication

O(nc)

Polynomial,

sometimes

 

O(cn)

Exponential

 

O(n!)

Factorial

 

 

              Table 1: Comparison of several algorithms & their complexities

 

 

 

Array size

 

Logarithmic:

log2N

 

Linear: N

 

Quadratic: N2

 

Exponential:

2N

 

8

128

256

1000

100,000

 

3

7

8

10

17

 

8

128

256

1000

100,000

 

64

16,384

65,536

1 million

10 billion

 

256

3.4*1038

1.15*1077

1.07*10301

........

 


Related Discussions:- The complexity ladder

Define about the structure - container, Define about the Structure - Contai...

Define about the Structure - Container - Some containers hold elements in some sort of structure, and some don't. Containers with no structure include bags and sets. Containe

Applications of shortest path algorithms, The minimum cost spanning tree ha...

The minimum cost spanning tree has broad applications in distinct fields. It represents several complicated real world problems such as: 1. Minimum distance for travelling all o

Explain all-pair shortest-paths problem, Explain All-pair shortest-paths pr...

Explain All-pair shortest-paths problem Given a weighted linked graph (undirected or directed), the all pairs shortest paths problem asks to find the distances (the lengths of

Deletion of a node from an avl tree, For AVL trees the deletion algorithm i...

For AVL trees the deletion algorithm is a little more complicated as there are various extra steps involved in the deletion of node. If the node is not a leaf node, then it contain

Define order of growth, Define order of growth The  efficiency  analysi...

Define order of growth The  efficiency  analysis  framework  concentrates   on  the  order  of  growth  of  an  algorithm's   basic operation count as the principal indicator o

Arrays, This unit discussed about data structure called Arrays. The easiest...

This unit discussed about data structure called Arrays. The easiest form of array is a one-dimensional array which may be described as a finite ordered set of homogeneous elements

Definitions of graph, A graph G might be defined as a finite set V of verti...

A graph G might be defined as a finite set V of vertices & a set E of edges (pair of connected vertices). The notation utilized is as follows: Graph G = (V, E) Consider the g

The complexity ladder, The complexity Ladder: T(n) = O(1). It is ca...

The complexity Ladder: T(n) = O(1). It is called constant growth. T(n) does not raise at all as a function of n, it is a constant. For illustration, array access has this c

File organisation, File organisation might be described as a method of stor...

File organisation might be described as a method of storing records in file. Also, the subsequent implications approaching these records can be accessed. Given are the factors invo

Explain in detail about the abstract data type, Abstract data type The ...

Abstract data type The thing which makes an abstract data type abstract is that its carrier set and its operations are mathematical entities, like geometric objects or numbers;

Write Your Message!

Captcha
Free Assignment Quote

Assured A++ Grade

Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!

All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd