Book Image

Hands-On Data Structures and Algorithms with Kotlin

By : Chandra Sekhar Nayak, Rivu Chakraborty
Book Image

Hands-On Data Structures and Algorithms with Kotlin

By: Chandra Sekhar Nayak, Rivu Chakraborty

Overview of this book

Data structures and algorithms are more than just theoretical concepts. They help you become familiar with computational methods for solving problems and writing logical code. Equipped with this knowledge, you can write efficient programs that run faster and use less memory. Hands-On Data Structures and Algorithms with Kotlin book starts with the basics of algorithms and data structures, helping you get to grips with the fundamentals and measure complexity. You'll then move on to exploring the basics of functional programming while getting used to thinking recursively. Packed with plenty of examples along the way, this book will help you grasp each concept easily. In addition to this, you'll get a clear understanding of how the data structures in Kotlin's collection framework work internally. By the end of this book, you will be able to apply the theory of data structures and algorithms to work out real-world problems.
Table of Contents (16 chapters)
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Section 1: Getting Started with Data Structures
Section 2: Efficient Grouping of Data with Various Data Structures
Section 3: Algorithms and Efficiency
Section 4: Modern and Advanced Data Structures

Learning about notations

So far, we've understood why analyzing the complexity of an algorithm is really important. Now it's time to understand how to analyze the complexity. Before moving ahead with the how-to part, let's understand that, once the complexity of an algorithm is analyzed, there should be some way to represent it. For that, we usually use different notations.

As mentioned earlier, an algorithm can behave differently based on the size of the input given to it. And in the computer world, if you're building software, your algorithm should be prepared for any size of input. So, it's obvious that we need to analyze the complexity of every possible case.

The complexity of an algorithm can broadly fall into the following three categories:

  • Best case analysis: Best case defines the minimum time required by an algorithm to produce the output. It...