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Learning JavaScript Data Structures and Algorithms

You're reading from   Learning JavaScript Data Structures and Algorithms Hone your skills by learning classic data structures and algorithms in JavaScript

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785285493
Length 314 pages
Edition 2nd Edition
Languages
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Author (1):
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Loiane Avancini Loiane Avancini
Author Profile Icon Loiane Avancini
Loiane Avancini
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Table of Contents (13) Chapters Close

Preface 1. JavaScript—A Quick Overview FREE CHAPTER 2. Arrays 3. Stacks 4. Queues 5. Linked Lists 6. Sets 7. Dictionaries and Hashes 8. Trees 9. Graphs 10. Sorting and Searching Algorithms 11. Patterns of Algorithm 12. Algorithm Complexity

Self-balancing trees


Now that you know how to work with BST, you can dive into the study of trees if you want to.

BST has a problem: depending on how many nodes you add, one of the edges of the tree can be very deep, meaning a branch of the tree can have a high level and another branch can have a low level, as shown in the following diagram:

This can cause performance issues when adding, removing, and searching for a node on a particular edge of the tree. For this reason, there is a tree called the Adelson-Velskii and Landi's tree (AVL tree). The AVL tree is a self-balancing BST tree, which means the height of both the left and right subtrees of any node differs by 1 at most. You will learn more about the AVL tree in the next topic.

Adelson-Velskii and Landi's tree (AVL tree)

The AVL tree is a self-balancing tree, meaning the tree tries to self-balance whenever a node is added to it or removed from it. The height of the left or right subtree of any node (and any level) differs by 1 at most...

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