Book Image

Hands-On Data Structures and Algorithms with JavaScript

By : Kashyap Mukkamala
Book Image

Hands-On Data Structures and Algorithms with JavaScript

By: Kashyap Mukkamala

Overview of this book

Data structures and algorithms are the fundamental building blocks of computer programming. They are critical to any problem, provide a complete solution, and act like reusable code. Using appropriate data structures and having a good understanding of algorithm analysis are key in JavaScript to solving crises and ensuring your application is less prone to errors. Do you want to build applications that are high-performing and fast? Are you looking for complete solutions to implement complex data structures and algorithms in a practical way? If either of these questions rings a bell, then this book is for you! You'll start by building stacks and understanding performance and memory implications. You will learn how to pick the right type of queue for the application. You will then use sets, maps, trees, and graphs to simplify complex applications. You will learn to implement different types of sorting algorithm before gradually calculating and analyzing space and time complexity. Finally, you'll increase the performance of your application using micro optimizations and memory management. By the end of the book you will have gained the skills and expertise necessary to create and employ various data structures in a way that is demanded by your project or use case.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
PacktPub.com
Contributors
Preface
5
Simplify Complex Applications Using Graphs
Index

Space complexity and Auxiliary space 


Space complexity and Auxiliary space are two of the most often confused and interchangeably used terms when talking about the space complexity of a certain algorithm:

  • Auxiliary Space: The extra space that is taken by an algorithm temporarily to finish its work
  • Space Complexity: Space complexity is the total space taken by the algorithm with respect to the input size plus the auxiliary space that the algorithm uses.

When we try to compare two algorithms, we usually have a similar type of input, that is, the size of the input can be disregarded and thus what we do end up comparing is the auxiliary space of the algorithms. It's not a big deal to use either of the terms, as long as we understand the distinction between the two and use them correctly.

If we were using a low-level language such as C, then we can break down the memory required/consumed based on the data type, for example, 2 bytes to store an integer, 4 bytes to store floating point, and so on....