Gain insights into competitive programming, explore C++ skills with theory, code samples, practice problems, and master faster implementation for contests like ACM ICPC, Google CodeJam, and HackerCup.

Competitive programming can be a great way to build out your programming skills, get on any major company’s radar, and earn a little extra cash along the way.

In this course, you will learn to prepare for competitive programming contests like ACM ICPC, Google CodeJam, Facebook HackerCup, and many more.

Each topic is broken down with a healthy mix of theory, code samples, step-by-step solved sample problems, illustrations, useful practice problems, and tips and tricks for faster implementation.

You will need some solid C++ foundations coming into this course, but by the end it, you will be confident enough in your C++ skills to take home the win.

Competitive Programming in C++: The Keys to Success

# Algorithm analysis
Analyzing the run-time complexity of your solution is the most essential step in solving a problem to determine whether your solution is going to execute in time or if the judge is going to throw a TLE (Time Limit Exceeded) verdict.

# Cases
There are three cases when it comes to algorithm analysis:

1. Best Case
2. Average Case
3. Worst Case

When participating in online competitions, we are always interested in worst-case analysis. The test suite for the problem will still contain input, which forces your solution to its worst-case performance. 

# Big-O notation
The Big-O notation is used to define the upper bound of an algorithm. We will get into the complexity analysis of algorithms in their corresponding lessons. Here we’ll see the properties of the Big-O notation.

Complexity is expressed in terms of the input size. Take for example, searching for integer x through a list of N integers. This will require anywhere between `1` (Best case => x is the first element) and `N` (Worst case => x is the last element) operations. We say that this is an `O(N)` solution, i.e., linear in input size (N). This will become clearer once we go through some samples.

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In the next lesson, we will see how to calculate run-time complexities for simpler cases.

In this lesson, we'll cover algorithm analysis.


Introduction

Complexity Analysis

Number Theory

Arrays and Vectors

Sieve of Eratosthenes

Strings

Sorting

Linked List

Stack

Queue

Binary Tree

2 Pointers

Heap

Binary Search Tree

Balanced Binary Search Tree

Course Conclusion

Algorithm Analysis

Algorithm analysis