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Java 9 Data Structures and Algorithms

Java 9 Data Structures and Algorithms

By : Ray Chawdhuri
2.3 (3)
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Java 9 Data Structures and Algorithms

Java 9 Data Structures and Algorithms

2.3 (3)
By: Ray Chawdhuri
Buy this Book

Overview of this book

Java 9 Data Structures and Algorithms covers classical, functional, and reactive data structures, giving you the ability to understand computational complexity, solve problems, and write efficient code. This book is based on the Zero Bug Bounce milestone of Java 9. We start off with the basics of algorithms and data structures, helping you understand the fundamentals and measure complexity. From here, we introduce you to concepts such as arrays, linked lists, as well as abstract data types such as stacks and queues. Next, we’ll take you through the basics of functional programming while making sure you get used to thinking recursively. We provide plenty of examples along the way to help you understand each concept. You will also get a clear picture of reactive programming, binary searches, sorting, search trees, undirected graphs, and a whole lot more!
Table of Contents (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Why Bother? – Basic
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1. Why Bother? – Basic
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The performance of an algorithm
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Optimization of our algorithm
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Fixing the problem with large powers
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Summary
2
2. Cogs and Pulleys – Building Blocks
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2. Cogs and Pulleys – Building Blocks
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Arrays
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Linked list
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Doubly linked list
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Circular linked list
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Summary
3
3. Protocols – Abstract Data Types
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3. Protocols – Abstract Data Types
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Stack
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Queue
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Double ended queue
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Summary
4
4. Detour – Functional Programming
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4. Detour – Functional Programming
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Recursive algorithms
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Lambda expressions in Java
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Functional data structures and monads
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The concept of a monad
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Analysis of the complexity of a recursive algorithm
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Performance of functional programming
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Summary
5
5. Efficient Searching – Binary Search and Sorting
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5. Efficient Searching – Binary Search and Sorting
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Search algorithms
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Sorting
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A problem with recursive calls
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Summary
6
6. Efficient Sorting – quicksort and mergesort
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6. Efficient Sorting – quicksort and mergesort
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quicksort
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mergesort
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Complexity of any comparison-based sorting
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The stability of a sorting algorithm
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Summary
7
7. Concepts of Tree
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7. Concepts of Tree
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A tree data structure
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The tree abstract data type
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Binary tree
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Summary
8
8. More About Search – Search Trees and Hash Tables
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8. More About Search – Search Trees and Hash Tables
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Binary search tree
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Self-balancing binary search tree
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Red-black tree
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Hash tables
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Summary
9
9. Advanced General Purpose Data Structures
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9. Advanced General Purpose Data Structures
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Priority queue ADT
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Heap
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Linked heap
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Binomial forest
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Sorting using a priority queue
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In-place heap sort
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Summary
10
10. Concepts of Graph
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10. Concepts of Graph
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What is a graph?
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The graph ADT
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Representation of a graph in memory
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Traversal of a graph
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Cycle detection
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Spanning tree and minimum spanning tree
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Finding the minimum spanning tree
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Summary
11
11. Reactive Programming
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11. Reactive Programming
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What is reactive programming?
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Producer-consumer model
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Functional way of reactive programming
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Summary
12
Index
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Index

Recursive algorithms

As I have already pointed out, recursive algorithms are a different way of thinking about solving a problem. For example, say our problem is to write a program that, given a positive integer n, returns the sum of numbers from zero to n. The known imperative way of writing it is simple:

public int sum_upto(int n){
  int sum=0;
  for(int i=0;i<=n;i++){
    sum+=i;
  }
  return sum;
}

The following would be the functional version of the problem:

public int sum_upto_functional(int n){
  return n==0?0:n+sum_upto_functional(n-1);
}

That's it–just a one-liner! This is probably nothing new to Java programmers, as they do understand recursive functions. However, an imperative programmer would use recursion only when nothing else worked. But this is a different way of thinking. How do we justify that it is equivalent to solving the problem for a smaller input and then composing it with something else? Well, we are certainly first computing the same function for an...

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