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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

Greedy algorithms and backtracking


What do we mean by greedy algorithms? What is backtracking? By being greedy, the algorithm matches the longest possible part. Backtracking algorithms, upon failure, keep exploring other possibilities. Such algorithms begin afresh from where they had originally started, hence they backtrack (go back to the starting point).

We all follow the process of backtracking in real life. For example, to get to an address, we go to a well-known landmark, then try the first lane, for example. If there is no success, we backtrack to the landmark again and try another lane (we may ask a passerby for help). We keep doing this until we get to the address or give up the search altogether.

A well-known example of greedy and backtracking algorithms from the programming world is how a regex match happens. We use a simple regex using greedy qualifiers such as * and +:

Here is a quick Scala REPL session to see the greediness in action:

scala> val regex = "^(.*)(.+)$".r 
regex...
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