The ultimate guide to coding interviews. Developed by FAANG engineers. Boost problem-solving skills with number theory, dynamic programming, and graph theory. Get interview-ready in just a few hours.

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Whether you’re gearing up for online coding challenges, code-a-thons, or interviews, then this course is for you. With this course, you will solidify your problem-solving skills ensuring a swift sail through any problem. 


You will be tasked with solving some of the most frequently asked questions that are brought up in FAANG interviews. You will start with the concepts of Number Theory and Divide and Conquer, and gradually move towards more complex problems like dynamic programming and graph theory.


With each problem you solve, there will be insightful explanations and full implementation details to really hammer home the concepts. By the time you finish this course, you will have the confidence to solve any problem, no matter the difficulty.

Competitive Programming - Crack Your Coding Interview, C++

# Introduction
There are many ways to represent a graph. The two most common ways of representing a graph are explained below:
## Adjacency matrix
An *adjacency matrix* is a V*V binary matrix A. Element $A_{i,j}$, is 1 if there is an edge from vertex **i** to vertex **j**. Else, $A_{i,j}$, is 0.

> Note: A binary matrix is a matrix in which the cells can have only one of two possible values - either **0** or **1**.

The adjacency matrix can also be modified for the weighted graph in which instead of storing **0** or **1** in $A_{i,j}$ the weight or cost of the edge will be stored.
* In an undirected graph, if $A_{i,j}$ = 1, then $A_{j,i}$ = 1.
* In a directed graph, if $A_{i,j}$ = 1, then $A_{j,i}$ may or may not be 1.

The adjacency matrix provides constant-time access $(O(1))$ to determine if there is an edge between two nodes. The space complexity of the adjacency matrix is O($V^{2}$).



Learn the techniques to represent graphs in computer memory.

Overview

Number Theory

Number Theory

Divide and Conquer

Greedy Algorithms

Greedy Algorithms

Recursion and Backtracking

Recursion and Backtracking

Dynamic Programming

Graphs

Bonus Lessons

Graph Representation

Introduction

Adjacency matrix