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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

List append


Consider appending a node to a list. In the mutation world, we traverse the list until we reach the end and then change the last node to point to the new node. This is costly when the list is long and has a complexity of O(n).

For a persistent list (immutable and structurally shared) appending a new value, we need to traverse until the end of the list, copying all the elements on the way.

However, as noted, appending to a list is anyway a slow operation. When we need to append values, we need to ask ourselves whether lists are the right fit for the problem.

Whenever we want to grow a list by appending to the end, we should instead use a vector. When we are done with all of the appending, we could convert the vector into a list, if needed.

We can look at the original list at V0. This list has three nodes, holding the values 12, 99, and 37.

When we append the value 17, the original three nodes are copied, and then at construction time, the node with the value 17 is added. The data...

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