#### Overview of this book

Java 9 Data Structures and Algorithms covers classical, functional, and reactive data structures, giving you the ability to understand computational complexity, solve problems, and write efficient code. This book is based on the Zero Bug Bounce milestone of Java 9. We start off with the basics of algorithms and data structures, helping you understand the fundamentals and measure complexity. From here, we introduce you to concepts such as arrays, linked lists, as well as abstract data types such as stacks and queues. Next, we’ll take you through the basics of functional programming while making sure you get used to thinking recursively. We provide plenty of examples along the way to help you understand each concept. You will also get a clear picture of reactive programming, binary searches, sorting, search trees, undirected graphs, and a whole lot more!
Table of Contents (19 chapters)
Java 9 Data Structures and Algorithms
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Why Bother? – Basic
Cogs and Pulleys – Building Blocks
Protocols – Abstract Data Types
Detour – Functional Programming
Efficient Searching – Binary Search and Sorting
Efficient Sorting – quicksort and mergesort
Concepts of Tree
More About Search – Search Trees and Hash Tables
Advanced General Purpose Data Structures
Concepts of Graph
Reactive Programming
Index

## Analysis of the complexity of a recursive algorithm

Throughout the chapter, I have conveniently skipped over the complexity analysis of the algorithms I have discussed. This was to ensure that you grasp the concepts of functional programming before being distracted by something else. Now is the time to get back to it.

Analyzing the complexity of a recursive algorithm involves first creating an equation. This is naturally the case because the function is defined in terms of itself for a smaller input, and the complexity is also expressed as a function of itself being calculated for a smaller input.

For example, let's say we are trying to find the complexity of the `foldLeft` operation. The `foldLeft` operation is actually two operations, the first one being a fixed operation on the current initial value and the head of the list, and then a `foldLeft` operation on the tail. Suppose T(n) represents the time taken to run a `foldLeft` operation on a list of length n. Now, let's assume that the fixed operation...