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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

Complexity


What is the runtime complexity of node insertion? A Red-Black tree of n internal nodes has height at 2*log(n+1).

This means that operations, such as searching for a node, have logarithmic time. The insertion operation we just saw is also proportional to the height of the tree.

When we insert a new node and violate the second invariant, we fix it and always color the subtree's root red. This could create further invariant violation upward in the tree.

However, as the height is at most 2*log(n+1), the insertion operation has a runtime complexity of O(logn). So, for example, for a tree with 4294967296 nodes, which is 232, we have to perform up to 32 invariant fixes. This is a very good number, making Red-Black trees one of the most popular variants of Binary Search Trees.

Linux's completely fair scheduler uses Red-Black trees. See http://www.ibm.com/developerworks/library/l-completely-fair-scheduler/ for more information.

Java 8's TreeMap is implemented using Red-Black trees. See https...

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