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Swift Data Structure and Algorithms

You're reading from   Swift Data Structure and Algorithms Implement Swift structures and algorithms natively

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884504
Length 286 pages
Edition 1st Edition
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Author (1):
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Mario Eguiluz Alebicto Mario Eguiluz Alebicto
Author Profile Icon Mario Eguiluz Alebicto
Mario Eguiluz Alebicto
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Table of Contents (10) Chapters Close

Preface 1. Walking Across the Playground FREE CHAPTER 2. Working with Commonly Used Data Structures 3. Standing on the Shoulders of Giants 4. Sorting Algorithms 5. Seeing the Forest through the Tree 6. Advanced Searching Methods 7. Graph Algorithms 8. Performance and Algorithm Efficiency 9. Choosing the Perfect Algorithm

Binary search trees


Binary search tree basic operations such as access, search, insertion, and deletion take between O(n) and O(log(n)) time. Being both values the worst and the average scenarios. At the end, these times are going to depend on the height of the tree itself.

For example, for a complete binary search tree with n nodes, these operations could take O(log(n)) time. But if a tree with the same number of nodes n is built like a linked list (just 1 child per node), having more levels/depth for the same n nodes, then the operations are going to take O(n) time.

In order to make basic operations such as insertion or search, we need to scan nodes from the root to the leaves. Because of this, we can infer that the height of the tree (the distance or nodes between the root and a leaf) will affect the time we spend performing basic operations.

Now, before jumping into the code of some operations, such as inserting and searching nodes in a binary search tree, lets recall the basic property...

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