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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

Invariants


Let's discuss the concept of an invariant. What is an invariant? It is a condition that always holds. For example, refer to the following code:

scala> val arr = Array.fill[Int](5)(-1) 
arr: Array[Int] = Array(-1, -1, -1, -1, -1) 
 
scala> for(i <- 0 to 4) { 
     |   arr(i) = 0 
     | } 

To comprehend the preceding code, the following statements are always true:

  • At the start of each iteration, elements at indices 0 to i-1 are zero

  • After each iteration of the loop, elements 0 to i are zero

This is called a loop invariant. We can reason the code using invariants.

Another example of an invariant for a singly linked list is that every list node gets pointed at by one, and only one pointer. In other words, two adjacent pointers will never point to the same node.

Invariants can be relaxed temporarily and then restored. For example, consider the imperative version of the list node insertion algorithm:

       q <- new node 
       p <- node...
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