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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

Chapter 3. Lists

Let's start looking at the first fundamental data structure: lists.

Lists permeate the functional world. LISP is one of the earliest programming languages. The name LISP means list processor.

The imperative world also uses lists. In an algorithmic sense, lists are great for growing incrementally, for example, when we append elements to an existing list. List append is an O(1) operation in the imperative world. Deleting and inserting nodes anywhere in the list is an O(1) operation too. When we insert or delete a node, its predecessor and successor (if any) are the only nodes affected-a few pointers are juggled and the insertion or deletion is done.

Lists

For example, in the preceding list, when we insert node K, the algorithm is pretty simple:

Set k.next = c.next 
Set c.next = k 

This works! Largely as in the imperative world, mutating a node in place is okay.

In the functional, immutable world, things are pretty different though. In this chapter, we will take a close...

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