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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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Toc

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

Memoization - remembering past results


Memoization is the art of computer program optimization to speed up functions. Donald Michie coined the word memoization. Whenever, a memoized function is called for the first time, for a given input, its output value is calculated and cached. Next time when the same input is given as an argument, function does not compute the value but returns the value from cached location for that given input. In some programming language, we find some or other internal mechanism to implement memoization. But many programming languages require explicit work to implement memoization.

So, is it possible that a function can return different output for the same input? The answer is: this is possible. We can understand this from the following Scala example:

scala> val ab = scala.collection.mutable.ArrayBuffer(1,2,3,4)  \\ Line One 
 
ab: scala.collection.mutable.ArrayBuffer[Int] = ArrayBuffer(1, 2, 3, 4) 
 
scala> print(ab.remove(1))  // Line...
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