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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

Leftist trees


Think about the problem we'd face if we try to adapt this array-based algorithm to a persistent version. The swap will result in expensive copying, so an insert/pop would have complexity amounting to O(n).

A leftist tree is a data structure that we can use to implement the priority queue ADT. Before you look at the core data structure, look at the rank of the tree.

We first make the tree a full binary tree. If we put explicit leaves in such a tree, every node (other than the leaves) will have two children.

For more information, visit:

http://stackoverflow.com/questions/12359660/difference-between-complete-binary-tree-strict-binary-tree-full-binary-tre

Let's have a look at the figure now:

We define the rank of a binary tree as per the length of its right spine. The rank of the leaf is 0. In the preceding figure, the rank of the tree at root 9 is 2 as we need to cross over the right node with value 27. The right spine for every node is drawn with a dashed line.

A leftist tree is...

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