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Learning Functional Data Structures and Algorithms

You're reading from   Learning Functional Data Structures and Algorithms Learn functional data structures and algorithms for your applications and bring their benefits to your work now

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781785888731
Length 318 pages
Edition 1st Edition
Languages
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Authors (2):
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Raju Kumar Mishra Raju Kumar Mishra
Author Profile Icon Raju Kumar Mishra
Raju Kumar Mishra
Atul S. Khot Atul S. Khot
Author Profile Icon Atul S. Khot
Atul S. Khot
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Table of Contents (14) Chapters Close

Preface 1. Why Functional Programming? 2. Building Blocks FREE CHAPTER 3. Lists 4. Binary Trees 5. More List Algorithms 6. Graph Algorithms 7. Random Access Lists 8. Queues 9. Streams, Laziness, and Algorithms 10. Being Lazy - Queues and Deques 11. Red-Black Trees 12. Binomial Heaps 13. Sorting

A sense of balance


Many data structures have a balance invariant. After every update to the tree, the invariant is restored by rebalancing the structure. Why do we need this balancing? What do we mean by balance?

A Binary Search Tree, for example, could degenerate into a list. For example, consider a scenario where you insert sorted data into a BST. You will get a tree whose nodes have no left children. To all intents and purposes, you have constructed just a linked list in the garb of a tree. This would lead to pathetic access performance for O(n). A balanced BST won't have this problem.

A tree is perfectly balanced if the left and right subtrees of any node are of the same height.

We also have almost perfectly balanced trees. The subtrees' heights may differ by at most 1.

As we will soon see in the next chapter, balancing a BST allows us to have guaranteed O(logn) search times. The next chapter discusses Red-Black trees, which are a very popular balanced variant of the BST.

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