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Beginning Java Data Structures and Algorithms

Beginning Java Data Structures and Algorithms

By : James Cutajar
4.6 (10)
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Beginning Java Data Structures and Algorithms

Beginning Java Data Structures and Algorithms

4.6 (10)
By: James Cutajar
Buy this Book

Overview of this book

Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems. This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.
Table of Contents (8 chapters)
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Preface
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Preface
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Who This Book Is For
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What This Book Covers
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To Get the Most out of This Book
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1
Algorithms and Complexities
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Algorithms and Complexities
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Developing Our First Algorithm
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Measuring Algorithmic Complexity with Big O Notation
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Identifying Algorithms with Different Complexities
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Summary
2
Sorting Algorithms and Fundamental Data Structures
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Sorting Algorithms and Fundamental Data Structures
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Introducing Bubble Sorting
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Understanding Quick Sort
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Using Merge Sort
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Getting Started with Fundamental Data Structures
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Summary
3
Hash Tables and Binary Search Trees
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Hash Tables and Binary Search Trees
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Introducing Hash Tables
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Getting Started with Binary Search Trees
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Summary
4
Algorithm Design Paradigms
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Algorithm Design Paradigms
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Introducing Greedy Algorithms
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Getting Started with Divide and Conquer Algorithms
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Understanding Dynamic Programming
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Summary
5
String Matching Algorithms
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String Matching Algorithms
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Naive Search Algorithm
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Getting Started with the Boyer-Moore String Searching Algorithm
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Introducing Other String Matching Algorithms
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Summary
6
Graphs, Prime Numbers, and Complexity Classes
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Graphs, Prime Numbers, and Complexity Classes
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Representing Graphs
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Traversing a Graph
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Calculating Shortest Paths
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Prime Numbers in Algorithms
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Other Concepts in Graphs
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Understanding Complexity Classes of Problems
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Summary
7
Other Books You May Enjoy
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Other Books You May Enjoy
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Traversing a Graph

A common activity on a graph is visiting each vertex of it in a given order. We will start by introducing the breadth-first search, and then follow with depth-first search. Both of these techniques form the archetype for many important graph algorithms, as we will see later with the cycle detection and Dijkstra's algorithm for single-source shortest paths.

Breadth-First Search

Given a graphG = (V, E)and a source vertexs, breadth-first search explores the edges ofGsystematically to discover every vertex that is reachable froms. While doing so, it computes the smallest number of edges fromsto each reachable vertex, making it suitable to solve the single-source shortest path problem on unweighted graphs, or graphs whose edges all have the same weight.

Breadth-First Search (BFS)is named so because it expands the frontier between discovered and undiscovered vertices uniformly across the breadth of the frontier. In that sense, the algorithm first explores vertices at distancekfrom...

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