__Complexity of an algorithm:-__

Complexity of an algorithm is a function ( f(n) ) which gives running time and or space in the term of input size (n) .

__Asymptotic Notation :-__ To select the best algorithm we need to check the efficiency of the each algorithm . Efficiency can be measured by computing the time complexity of each algorithm . Time complexity can be calculated by Asymptotic.

__1) Average Case ( Ó¨):-__ The theta notation bounds a function from above and below, so it defines exact asymptotic behavior.

Î© (Omega) describes the best running time of a program. We compute the Î© by counting how many iterations an algorithm will take in the best-case scenario based on an input of N. For example, a linear search will take o(1) running time because in the best case element will find at the first index so the linear search will terminate after finding the element.

The Big-O notation describes the worst-case running time of a program. We compute the Big-O of an algorithm by counting how many iterations an algorithm will take in the worst-case scenario with an input of N. We typically consult the Big-O because we must always plan for the worst case.

__Recommended Post:__

__Full C course:- __

__Key points:-__

- How to set limit in the floating value in python
- What is boolean data type
- How to print any character without using format specifier
- How to check that given number is power of 2 or not
- How to fix limit in double and floating numbers after dot (.) in c++
- How to print a double or floating point number in scientific notation and fixed notation
- How to take input a string in c
- How to reduce the execution time of program in c++.

__Cracking the coding interview:-__

__Array and string:-__

__Tree and graph:-__

__Hackerearth Problems:-__

- Very Cool numbers | Hacker earth solution
- Vowel Recognition | Hackerearth practice problem solution
- Birthday party | Hacker earth solution
- Most frequent | hacker earth problem solution
- program to find symetric difference of two sets
- cost of balloons | Hacker earth problem solution
- Chacha o chacha | hacker earth problem solution
- jadu and dna | hacker earth solution
- Bricks game | hacker earth problem
- Anti-Palindrome strings | hacker earth solution
- connected components in the graph | hacker earth data structure
- odd one out || hacker earth problem solution
- Minimum addition | Hackerearth Practice problem
- The magical mountain | Hackerearth Practice problem
- The first overtake | Hackerearth Practice problem

__Hackerrank Problems:-__- Playing With Characters | Hackerrank practice problem solution
- Sum and Difference of Two Numbers | hackerrank practice problem solution
- Functions in C | hackerrank practice problem solution
- Pointers in C | hackerrank practice problem solution
- Conditional Statements in C | Hackerrank practice problem solution
- For Loop in C | hackerrank practice problem solution
- Sum of Digits of a Five Digit Number | hackerrank practice problem solution
- 1D Arrays in C | hackerrank practice problem solution
- Array Reversal | hackerrank practice problem solution
- Printing Tokens | hackerrank practice problem solution
- Digit Frequency | hackerrank practice problem solution
- Calculate the Nth term | hackerrank practice problem solution

__Data structure:-__

- Program to find cycle in the graph
- Implementation of singly link list
- Implementation of queue by using link list
- Algorithm of quick sort
- stack by using link list
- program to find preorder post order and inorder of the binary search tree
- Minimum weight of spanning tree
- Preorder, inorder and post order traversal of the tree

__ MCQs:-__

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