In this chapter, we gave an introduction to algorithmic complexity and the notation to describe it. We have shown you how big O notation can be used to describe how well an algorithm scales as the input gets bigger. We have also seen various examples of complexities and shown you how you can intuitively differentiate between them. Understanding big O notations comes in handy when you need to design and implement new solutions or when you are diagnosing performance issues.

#### Beginning Java Data Structures and Algorithms

##### By :

#### Beginning Java Data Structures and Algorithms

##### By:

#### Overview of this book

Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems.
This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.

Table of Contents (8 chapters)

Preface

Free Chapter

Algorithms and Complexities

Sorting Algorithms and Fundamental Data Structures

Hash Tables and Binary Search Trees

Algorithm Design Paradigms

String Matching Algorithms

Graphs, Prime Numbers, and Complexity Classes

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