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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? 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

Vectors versus lists

Prepending an element to a linked list is very fast. In fact, it is an O(1) operation, meaning the original list is simply pointed at by the new element node. The change happens only at the head of the list. As we don't need to traverse the list at all, this is a fixed cost, that is, O(1) operation.

Accessing an element at some index n is slower, meaning it is proportional to the number of elements in the list. We need to start at the head and keep traversing the nodes and keep counting. We do this until we reach the nth node. If we access the second last node, we will have traversed almost all of the list.

When any operation could make us look at almost all the elements, the complexity would be O(n). This means it would be proportional to the number of elements.

On the other hand, appending an element to a list is costly when we need to preserve the original list. In the next chapter, we need to traverse and copy all the elements, so we will preserve the current...

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