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Beginning Java Data Structures and Algorithms
Beginning Java Data Structures and Algorithms

Beginning Java Data Structures and Algorithms: Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner

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Beginning Java Data Structures and Algorithms

Sorting Algorithms and Fundamental Data Structures

In the previous chapter, we saw how the intersection problem can be improved by using a sorting algorithm. This is common with many problems. If the data is organized in an ordered manner, a more efficient algorithm can be developed. In this chapter, we will start by exploring three types of sorting techniques, which are bubble, quick, and merge sorting. Later, we will learn different ways to organize data using fundamental data structures.

By the end of this chapter, you will be able to:

  • Describe how bubble sorting works
  • Implement faster sorting with quick sort
  • Characterize merge sorting
  • Build a linked list data structure
  • Implement queues
  • Describe the stack data structure

Introducing Bubble Sorting

Bubble sorting is the simplest sorting algorithm out there. The technique involves making multiple passes over the input array and swapping unordered elements close to one another. The technique is called bubble sort, as the sorted list "bubbles" up from the tail end of the list.

Understanding Bubble Sorting

All sorting algorithms accept a list of elements and return them ordered. The main difference between each algorithm is the manner in which the sorting is done. Bubble sorting works by swapping adjacent elements. This pushes the sorted elements toward the end of the list.

Snippet 2.1 shows the pseudocode for bubble sort. The algorithm involves three simple tasks, which...

Understanding Quick Sort

Quick sort is a big improvement over bubble sort. This sorting technique was developed by British computer scientist Tony Hoare. The algorithm works in three main steps:

  1. Select a pivot
  2. Partition the list so that elements on the left of the pivot are less than the value of the pivot and the ones on the right are greater
  3. Repeat steps 1 and 2 on the left and right parts separately

Since recursion is required for quick sort, we will begin this section by giving an example of recursion. Later, we will see how the partitioning in the quick sort algorithm works, and in the end, we will put the recursion techniques to use in the final part.

Understanding Recursion

Recursion...

Using Merge Sort

Although the quicksort on average is pretty fast, it still has the theoretical worst time complexity of O(n²). In this section, we shall examine another sorting algorithm, called merge sort, in which the worst time complexity is O(n log n). Similar to quick sort, merge sort belongs to the divide and conquer class of algorithms.

Merge sort can be summarized in three simple steps as follows: 

  1. Split the array in the middle
  2. Recursively sort each part separately
  3. Merge the two sorted parts together

In the following section, we will develop the preceding steps gradually, at each turn slowly building our understanding of how merge sorting works.

Although merge sort is theoretically faster than quick sort, in practice, some implementations of quick sort can be more efficient than merge sort. Additionally, the merge sort uses about O(n) memory...

Getting Started with Fundamental Data Structures

Data structures are a way to organize data so that it is efficiently accessible for the problem you are trying to solve. Choosing the right data structure will depend on the type of problem you're trying to solve (dictating the manner you access your data), the amount of data you need to organize, and the medium you use to store your data (memory, disk, and so on).

We have already seen and used one example of a data structure. In the preceding sections, we have made extensive use of arrays. Arrays are the most primitive of data structures. They provide access to your data using an index and are fixed in size (also called static). This is opposed to other dynamic data structures that can grow and make more space for data whenever it's needed.

...

Summary

In this chapter, we laid the foundations for the more complex upcoming sections. In the first few sections, we saw how a simple problem such as sorting can have many solutions, all with different performance properties. We explored three main implementations, which were bubble, quick, and merge sort.

In later sections, we introduced data structures and studied various implementations and use cases of linked lists, queues, and stacks. We also saw how some data structures can be used as building blocks to build more complex ones on top. In the next chapter, we will study hash tables and binary trees, two important and widely used data structures.

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Key benefits

  • Explains in detail different algorithms and data structures with sample problems and Java implementations where appropriate
  • Includes interesting tips and tricks that enable you to efficiently use algorithms and data structures
  • Covers over 20 topics using 15 practical activities and exercises

Description

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.

Who is this book for?

If you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques.

What you will learn

  • Understand some of the fundamental concepts behind key algorithms
  • Express space and time complexities using Big O notation.
  • Correctly implement classic sorting algorithms such as merge and quicksort
  • Correctly implement basic and complex data structures
  • Learn about different algorithm design paradigms, such as greedy, divide and conquer, and dynamic programming
  • Apply powerful string matching techniques and optimize your application logic
  • Master graph representations and learn about different graph algorithms

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 30, 2018
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781789537178
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Product Details

Publication date : Jul 30, 2018
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781789537178
Category :
Languages :

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Table of Contents

7 Chapters
Algorithms and Complexities Chevron down icon Chevron up icon
Sorting Algorithms and Fundamental Data Structures Chevron down icon Chevron up icon
Hash Tables and Binary Search Trees Chevron down icon Chevron up icon
Algorithm Design Paradigms Chevron down icon Chevron up icon
String Matching Algorithms Chevron down icon Chevron up icon
Graphs, Prime Numbers, and Complexity Classes Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Daniel Lopes Nov 04, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book contains all of the most important concepts and algorithms that will follow you throughout your Software Engineering career. A good example are the Hash Tables, that has a huge benefit to learn the concept beforehand before you go rooting around the low-level Java implementation (always good to go read the Java code when available).I see great value in this book also for interview readiness, which - whether you master it or not - covers most topics needed to be a proficient Software Engineer. Highly recommended.
Amazon Verified review Amazon
Jio dddss Oct 02, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
you first feel you cheated by book price but book content is very good and easy.i just readed first page i understand every thing from this book very low pages in this book but it is good for beginner .you should require java basic knowledge
Amazon Verified review Amazon
Jason Jun 30, 2019
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Great
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Tanu deb Nov 21, 2023
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Udemy Verified review Udemy
Robert Kigobe Jan 04, 2022
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Udemy Verified review Udemy
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