### DS & A (Algorithm Analysis - Measures of Algorithm Complexity)

| Measures of Algorithm Complexity - CHAPTER 7 |In Chapter 4 we learned about the growth rate of an algorithm. So, as of now we already know what growth rate of an algorithm is. And In this chapter, we will discuss the different measures of algorithm growth rate/Algorithm complexity. The different measures for algorithm growth rate are: Big Oh (O)Big Omega (Ω)Big Theta (ϴ)Before we dive in and start discussing Big Oh, Big Omega and Big Theta I would like to introduce the concept of Upper Bound & Lower Bound

In simple terms, Upper bound of an algorithm is the highest growth rate that an algorithm can have. So, the algorithm cannot perform any worse than that.

Similarly, Lower bound of an algorithm is the lowest growth rate that an algorithm can have. And the algorithm cannot perform any better than that.

Upper Bound is not the Worst Case and Lower Bound is not the Best Case of an algorithm!!! It is because we are measur…

*So, What are Upper Bound and Lower Bound of an algorithm?*In simple terms, Upper bound of an algorithm is the highest growth rate that an algorithm can have. So, the algorithm cannot perform any worse than that.

Similarly, Lower bound of an algorithm is the lowest growth rate that an algorithm can have. And the algorithm cannot perform any better than that.

**CAUTION :**Upper Bound is not the Worst Case and Lower Bound is not the Best Case of an algorithm!!! It is because we are measur…