Book Image

Swift Data Structure and Algorithms

By : Mario Eguiluz Alebicto
Book Image

Swift Data Structure and Algorithms

By: Mario Eguiluz Alebicto

Overview of this book

Apple’s Swift language has expressive features that are familiar to those working with modern functional languages, but also provides backward support for Objective-C and Apple’s legacy frameworks. These features are attracting many new developers to start creating applications for OS X and iOS using Swift. Designing an application to scale while processing large amounts of data or provide fast and efficient searching can be complex, especially running on mobile devices with limited memory and bandwidth. Learning about best practices and knowing how to select the best data structure and algorithm in Swift is crucial to the success of your application and will help ensure your application is a success. That’s what this book will teach you. Starting at the beginning, this book will cover the basic data structures and Swift types, and introduce asymptotic analysis. You’ll learn about the standard library collections and bridging between Swift and Objective-C collections. You will see how to implement advanced data structures, sort algorithms, work with trees, advanced searching methods, use graphs, and performance and algorithm efficiency. You’ll also see how to choose the perfect algorithm for your problem.
Table of Contents (15 chapters)
Swift Data Structure and Algorithms
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Splay trees


Splay trees are a specific type of binary search tree in which there is an operation called splaying, which grants the tree the ability to quickly access recently visited nodes.

The splay operation puts the last accessed node as the new root of the tree. Recently visited nodes always have a minimum height, therefore they are easy and quick to access again. We can say that splay trees optimize themselves by performing a mix of searches and tree rotations.

The average height is O(log(n)) and the worst (and most unlikely) scenario is O(n). The amortized time of each operation on a n-node tree is O(log(n)). The amortized time analysis is used when we don't always expect the worst scenario so we can consider different scenarios (not just the worst) in the overall time complexity of the algorithm.

Two common uses of splay trees are caches and garbage collections. In both cases, we get the benefits of quick access to recently visited nodes, so this particular implementation of the binary...