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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? FREE CHAPTER 2. Building Blocks 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


We looked at another common data structure called a deque. A deque is a double-ended queue, so insertions and deletions are possible at both ends. We looked at pushFront, popFront, pushBack, and popBack operations. We also looked at some applications of deques.

Deques have a simple implementation in the imperative world. A doubly linked list could be used for implementing it. A vector could be used too.

We looked at two important concepts: amortization and lazy evaluation. We took a detailed look at Scala's Streams. We saw what a thunk is and how it helps us with delayed evaluation.

We then used all these concepts to check whether we can create more efficient queues. As we saw in the previous chapter, reversing an in list and appending it to the out list could be costly.

Then we applied the same concepts to look at how to implement persistent and immutable deques. We also saw how balancing a data structure plays a vital role in improving performance.

With all this knowhow, in the next...

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