Data Structures & Algorithms in Kotlin (Second Edition): Implementing Practical Data Structures in Kotlin

Learn Data Structures & Algorithms in Kotlin! Data structures and algorithms are fundamental tools every developer should have. In this book, you'll learn how to implement key data structures in Kotlin, and how to use them to solve a robust set of algorithms. This book is for intermediate Kotlin or Android developers who already know the basics of the language and want to improve their knowledge. Topics Covered in This Book Introduction to Kotlin : If you're new to Kotlin, you can learn the main constructs and begin writing code. Complexity : When you study algorithms, you need a way to compare their performance in time and space. Learn about the Big-O notation to help you do this. Elementary Data Structures : Learn how to implement Linked List, Stacks, and Queues in Kotlin. Trees : Learn everything you need about Trees — in particular, Binary Trees, AVL Trees, as well as Binary Search and much more. Sorting Algorithms : Sorting algorithms are critical for any developer. Learn to implement the main sorting algorithms, using the tools provided by Kotlin. Graphs : Have you ever heard of Dijkstra and the calculation of the shortest path between two different points? Learn about Graphs and how to use them to solve the most useful and important algorithms. Book License What You Need Book Source Code & Forums About the Cover Acknowledgments Content Development Contributors Introduction Who is this book for? The goal of this book Chapter 1: Kotlin & Kotlin Standard Library Introduction to Kotlin The Kotlin Standard Library Key points Chapter 2: Complexity Time complexity Other time complexities Comparing time complexity Space complexity Key points Chapter 3: Linked List Node LinkedList Adding values to the list Removing values from the list Kotlin collection interfaces Becoming a Kotlin mutable collection Challenges Key points Chapter 4: Stack Data Structures Stack operations Implementation push and pop operations Challenges Key points Chapter 5: Queues Common operations Example of a queue List-based implementation Doubly linked list implementation Ring buffer implementation Double-stack implementation Challenges Key points Chapter 6: Trees Terminology Implementation Traversal algorithms Challenges Key points Chapter 7: Binary Trees Implementation Traversal algorithms Challenges Key points Chapter 8: Binary Search Trees Case study: array vs. BST Implementation Challenges Key points Chapter 9: AVL Trees Understanding balance Implementation Challenges Key points Chapter 10: Tries Example Implementation Challenges Key points Chapter 11: Binary Search Example Implementation Challenges Key points Chapter 12: The Heap Data Structure What is a heap? The heap property Heap applications Common heap operations Sorting and comparing How do you represent a heap? Inserting into a heap Removing from a heap Removing from an arbitrary index Searching for an element in a heap Heapify an array Testing Challenges Key points Chapter 13: Priority Queues Applications Common operations Implementation Challenges Key points Chapter 14: O(n2) Sorting Algorithms Bubble sort Selection sort Insertion sort Generalization Challenges Key points Chapter 15: Merge Sort Implementation Performance Challenges Key points Chapter 16: Radix Sort Example Implementation Challenges Key points Chapter 17: Heap Sort Getting started Example Implementation Performance Challenges Key points Chapter 18: Quicksort Example Partitioning strategies Effects of a bad pivot choice Challenges Key points Chapter 19: Graphs Weighted graphs Common operations Defining a vertex Defining an edge Adjacency list Implementation Adjacency matrix Implementation Graph analysis Challenges Key points Chapter 20: Breadth-First Search Example Implementation Performance Challenges Key points Chapter 21: Depth-First Search DFS example Implementation Performance Challenges Key points Chapter 22: Dijkstra’s Algorithm Example Implementation Trying out your code Performance Challenges Key points Chapter 23: Prim’s Algorithm Example Implementation Testing your code Performance Challenges Key points Conclusion