The ultimate guide to Big-O notation for coding interviews, developed by FAANG engineers. Learn algorithm complexity in simple terms and get interview-ready in just a few hours.

This course is intended for professionals that lack formal education in computer science, and that are in search of a simple and practical guide to algorithmic complexity. The course explains the concepts in layman's terms, and teaches how to reason about the complexity of algorithms without requiring one to have an extensive mathematical skillset. This course can also be handy for revising complexity concepts or Big-O analysis before interviews. Finally, the content also scratches the surface of some advanced analysis topics to provide a more encompassing image of the complexity theory.

Big-O Notation For Coding Interviews and Beyond

<h2>P equals NP or P doesn't equal NP?</h2>
Since we can solve problems in P in polynomial time, we can also verify them in polynomial time. Thus we can say every problem in P is also in NP. Note we haven't made the converse claim whether problems in NP are in P or not. Or said another way, is P a proper or improper subset of NP? We'll come back to that later.
One of the most important open questions in theoretical computer science is whether P=NP. It's so important and famous that Clay Institute includes it as one of the <a href="http://www.claymath.org/millennium-problems/p-vs-np-problem" target="_blank">millennium problems</a>, offering $1 million dollars in prize money for a solution! If P equals NP then the two classes would collapse into one. A pictorial representation is shown below

In this lesson, we discuss the famous is P=NP dilemma.

Basics

Formal Analysis Tools

Recursive

Data-Structures

Amortized Analysis

Probabilistic Analysis

Complexity Theory

The End

Get Rich with Complexity

P equals NP or P doesn’t equal NP?