Last Updated on 4 years by ICT Byte

Algorithm

• Finite set of computational instructions
• Set of steps to solve the problem

Properties:

• Input / output : some input from some standard set of input and must produce output
• Definiteness: each step must be unambiguous
• Finiteness: algorithm must terminate after finite tome or steps
• Correctness: correct set of output values must be provided from each set of inputs

Random Acess Machine Model

• Base machine model for the study of design and analysis of algorithm
• Machine independent environment
• Each basic operation (+, -) takes  steps
• Loops and subroutines are not basic operations
• No shortage of memory

E.g

Fibonacci(n)

{

a=0; b-1, f=1;

for(i=2; i<=n; i++)

{

F=a+b;

A=b;

B=f;

}

return f;

}

GCD (Greatest Common Divisor)

1. Common method to calculate GCD

GCD (4,8)

4=2*2*1

8=2*2*2*1

GCD (4,8)= 2*2*1 =4

• Euclidean algorithm to find GCD

GCD(a,b)

{

If (b==0)

                              Return a;

Else

               Return GCD (b, a mod b)

}

Case 1:

No. of basic operation (division)=5

Case 2

No. of basic operation = 2

Asymptotic Notation

Mathematical notations which are used to describe running time of algorithms

• Complexity analysis of the algorithm is very hard if we try to analyse the exact; so its nearby solution
•  the complexity of an algorithm  is the mathematical function of size of input
• So we analyze the algorithm in terms of upper bound and lower bound
• Mostly we concentrate on worst case only
• Check  image [graph wala]
• Three types of asymptotic notation
  • Big Oh (O) Notation
  • Big Omega (Ω) Notation
  • Big Theta (Ꝋ) Notation

Exponents

x^ax^b=x^a+b

^…

Check photo

Logarithms

1. Logab= loaga+logb
2. Log(a/b)= loga-logb
3. Log (a^b) = bloga
4. Log1=0; log2=1; log1024=10

Series

See photo

Big Oh (O) Notation

• A function f(x)=O (g(x)) iff there exists two positive integers c and n_­o, such that for all n>=n_o, 0<=c*g(n)<=f(n)
• Represents the upper bound of the running time of an algorithm
• Worst-case complexity of an algorithm can be found with big oh

e.g

f(n)=3n²+4n+9

g(n)=n²

Prove that f(n)=O(g(n))

               = O(n²)

Big Omega (Ω) Notation

• A function f(x)=Omega(g(x)) iff there exists two positive integers c and n_­o, such that for all n>=n_o, 0<=c*g(n)<=f(n)
• represents lower bound
• gives best case of algorithm

Big Theta (Ꝋ) Notation

• A function f(x)= Ꝋ (g(x)) iff there exists three positive constans  c1, c2 and n_­o, such that for all n>=n_o, 0<=c1*g(n)<=f(n)<=c2*g(n)
• See photo

Difference between big oh, big omega and big theta

Example (Insertion Sort)

For (i-1; i<n; i++)

{

               X=A[i];

               j=i-1;

while(j>=0 &&  A[j]>x)

{

               A[j+1]=A[j];

               j=j-1

}

A[j=1]=x;

}

To be continued…

Table xa

Time Complexity