Algorithms are the series of steps which have no ambiguity that we follow to solve some problem. There are three types of Algorithms i.e

If we are using loops to code our Algorithm than it is Iterative Algorithm.

If we are using Recursion to code our Algorithm than it is Recursive Algorithm.

The rate of growth is the growth at which our algorithm is increasing i.e order of complexity increases and run time increases as our function input increases.

• @ - It is used for ordering as * is used for ordering
• (************) - Highest Rate of Growth
• (*) - Smallest Rate of Growth
  • Source-Wikipedia and others

The algorithm take max time for which input i.e it will complete and run slowly. The maximum time in which an Algorithm is solved.It is denoted using Big O Notation.

The algorithm take less time for which input i.e it will complete and run faster. The minimum time in which an Algorithm is solved. It is denoted using Big Omega Notation.

The time calculated by average of best case and worst case and we can predict run time for an Algorithm.

There are total five types of Notations

• Big O Notation will gives the upper bound for an Algorithm.
• It is represented by O().
• It will gives us the Worst Case (Maximum time to solve the problem) of the Algorithm.
• Big O Notation is a way of expressing the complexity related to number of items that an algorthm will deal with.
• Theoritically

f(n) = O(n)
If f(n) <= c * g(n)

• Example:

• GRAPH

• Big Omega Notation will gives the lower bound of the Algorithm.
• It is represented by Ω()
• It will gives us the Best Case (Minimum time to solve the problem) of the Algorithm.
• Theoritically

f(n) = Ω(n)
If f(n) >= c * g(n)

• GRAPH

• Big Theta Notation will gives the unique bound of the Algorithm i.e when upper bound and lower bound an Algorithm is same.
• It is represented by θ()
• It will gives the Average Case (Single time to solve the problem) of the Algorithm.
• Its relation is sometimes called as Asymtotically Equal.
• Theoritically

f(n)=θ(n)
If f(n) = c * g(n)

• GRAPH

f(n) < c * g(n)

f(n) > c * g(n)

Complexity tells us how much complex of an Algorithm.

There are two types of Complexities:

Time Complexity tell about how much time an algorithm will take to solve the problem.

For iterative solution the complexity is finded out by analysing the statements of the code.

For recursive solution the complexity can be finded out using these six methods i.e

1. Back Substitution
2. Recursion Tree
3. Master Theorem (Divide and Conquer)
4. Master Theorem (Substract and Conquer)
5. Method of Guessing and Confirming
6. Amortised Analysis

Space Complexity tells about how much memory an algorithm will take to solve the problem.

For space complexity of iterative solution we will find the number of variables declared and used. If it remain constant than complexity is O(1) and if it increases or decreases than complexity is O(n).

For space complexity of recursive solution we will find how much stack is used. For example four statements are gone in stack due to recursion so the space complexity can be finded out by checking the steps of stack i.e 4 in this case.

• n - Number of Inputs
• N - Number of Steps

Data Structures are the Structures formed by Data Types which makes our code efficient and we can use data in a systematic way. For example Stacks,Queues,Lists,arrays,etc.There are two types of Data Structures:

Data Structures in which data is organised linear and in sequential order. For example

• Arrays,
• Lists,
• Queues,etc.

Data Structures in which data is organised in non linear and in non-sequential order. For example

• Trees,
• Graphs,etc.

It is a type of sort in which the order of same identical elments remain same after sorting. For example

• Before sorting elements are

3 , 4 , 5 , 4 , 7 , 1 , 9

• after sorting it becomes

1 , 3 , 4 , 4 , 5 , 7 , 9

It is a type of sort in which the order of same identical elments get different after sorting.

For example

• Before sorting elements are

3 , 4 , 5 , 4 , 7 , 1 , 9

• after sorting it becomes

1 , 3 , 4 , 4 , 5 , 7 , 9