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Swift Data Structure and Algorithms

You're reading from   Swift Data Structure and Algorithms Implement Swift structures and algorithms natively

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884504
Length 286 pages
Edition 1st Edition
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Author (1):
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Mario Eguiluz Alebicto Mario Eguiluz Alebicto
Author Profile Icon Mario Eguiluz Alebicto
Mario Eguiluz Alebicto
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Table of Contents (10) Chapters Close

Preface 1. Walking Across the Playground FREE CHAPTER 2. Working with Commonly Used Data Structures 3. Standing on the Shoulders of Giants 4. Sorting Algorithms 5. Seeing the Forest through the Tree 6. Advanced Searching Methods 7. Graph Algorithms 8. Performance and Algorithm Efficiency 9. Choosing the Perfect Algorithm

Radix tree

In trie tree, we have seen that each edge contains a single letter or single part of a key. Radix trees are like a compressed version of trie trees, where the edges can contain more than a single letter, even an entire word (if we are using them for words/letters).

This is very effective, reducing the amount of memory and space the tree needs. Let's see an example:

Radix tree

Trie tree (left) and radix tree (right) for the same input

In the preceding figure, you can view the difference between a trie tree and a radix tree for the same input data, PLAN, PLAY, POLL, and POST. Note the following:

  • The radix version of the trie uses fewer nodes; one of the purposes of the radix trees is to reduce the amount of memory used. This is because each key has more information (each edge), so we need fewer edges.
  • We can perform this compression of single letters to partial words in edges when a node has a single child. Note the trie tree edges [L ->A], [L -> L], and [S -> T...
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