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++

# Greedy Algorithm
As the name suggests, a *Greedy Algorithm* always makes the choice that seems to be the best at that moment. This means that it makes a *locally optimal* choice in the hope that this choice will lead to a *globally optimal* solution.

**How to decide which choice is optimal?**

This algorithmic designing technique is mainly used to solve optimization problems. In these problems, usually, we have an objective function that needs to be optimized (maximized or minimized) at a given point. A Greedy Algorithm makes greedy choices at each step to ensure that the objective function is optimized. The Greedy Algorithm has only one shot to compute the optimal solution; it never goes back and reverses the decision. 

# Components of the Greedy Algorithm
Greedy Algorithms have the following five components:

* **Candidate set:** A solution is created from this set.
* **Selection function:** Used to choose the best candidate to be added to the solution
* **Feasibility function:** Used to determine whether a candidate can be used to contribute to the solution
* **Objective function:** Used to assign a value to a solution or a partial solution
* **Solution function:** Used to indicate whether a complete solution has been reached


# Major areas in which the Greedy approach works
Greedy algorithms are used to solve many problems, such as:
* Finding the shortest path between two vertices using Dijkstra’s algorithm (*refer to the Graphs chapter*)
* Finding the minimal spanning tree in a graph using Prim’s/Kruskal’s algorithm (*refer to the Graphs chapter*)
* Optimization problems

# Where does a Greedy approach fail?
In many problems, the Greedy algorithm fails to find an optimal solution. It may instead produce a worse solution. In the `Dynamic Programming` chapter, we will discuss that the problem that looks like it could have been solved using the Greedy approach doesn’t give us the optimal solution.

Learn the Greedy approach to solving coding problems.

Overview

Number Theory

Number Theory

Divide and Conquer

Greedy Algorithms

Greedy Algorithms

Recursion and Backtracking

Recursion and Backtracking

Dynamic Programming

Graphs

Bonus Lessons

Introduction to Greedy Algorithms

Greedy Algorithm