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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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Toc

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

Summary

Like stacks, queues are fundamental data structures. Queues help us realize the FIFO approach. We looked at how FIFO queues are implemented in the imperative world. We noted that we would end up with too much of copying if we insert new nodes at the end of a list. We could do this in the imperative world but would end up with O(n) copying performance to achieve persistent FIFO queues.

Instead, we looked at an innovative design involving two stacks. We also looked at the Scala implementation and discussed some Scala idioms.

Priority queues are an important variation of queues. We define a priority for each element of the queue and wish to pop the element with the highest priority.

Heap is a famous data structure for implementing the priority queue ADT. Heaps are realized with a full and complete binary tree. This is not a BST though. The heap invariant is this: the value at the root is less than its children.

We looked a beautiful algorithm, based on arrays, to implement heaps. However...

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