Algorithms and Complexities

An algorithm is a set of logical instructions to perform a particular task. Algorithms are everywhere nowadays. As a software developer, understanding the core principles of algorithms and data structures will enable you to make informed decisions on how to approach a particular problem. This is valid whether you're working in a bank writing accounting software or doing medical research data, mining genetic code. How do we determine which is the right algorithm to use when more than one solution to a problem exists? In this chapter, we will examine different types of algorithms and discuss how the performance varies in each. We will discuss what makes an algorithm more efficient than another and how to express the complexity of each.

The common examples of algorithms include traffic lights regulating congestion on the streets, face recognition software on smartphones, recommendation technologies, and so on.
It's important for you to understand that an algorithm is just a small part of an application used to solve a well-defined problem. Examples such as sorting a list of numbers, finding the shortest route, or word prediction are all correct. Big software applications, such as email clients or an operating system are improper examples.

By the end of this chapter, you will be able to:

• Define an algorithm with an example
  
• Measure algorithmic complexity
  
• Identify algorithms with different complexities
  
• Assess various examples with different runtime complexities